(Note: This essay was originally published on Gamasutra.com in 2007. To help prevent link rot, I’m reposting it here with minor edits and fixed links. I’ve also included a workshop presentation on the topic if folks want to teach these ideas in their classes.)
1. Moving Beyond Alchemy
“…it was clear to the alchemists that “something” was generally being conserved in chemical processes, even in the most dramatic changes of physical state and appearance; that is, that substances contained some “principles” that could be hidden under many outer forms, and revealed by proper manipulation.”
I recently happened across a description of alchemy, that delightful pseudo-science of the last millennium that evolved into modern chemistry. For a moment I thought the authors were describing the current state of the art in game design.
Every time I sit down with a finely crafted title such as Tetris or Super Mario Brothers, I catch hints of a concise and clearly defined structure behind the gameplay. It is my belief that a highly mechanical and predictable heart, built on the foundation of basic human psychology, beats at the core of every single successful game.
What would happen if we codified those systems and turned them into a practical technique for designing games?
In a Time Before Science
“Throughout the history of the discipline, alchemists struggled to understand the nature of these principles, and find some order and sense in the results of their chemical experiments—which were often undermined by impure or poorly characterized reagents, the lack of quantitative measurements, and confusing and inconsistent nomenclature.”
Historically, the process of understanding games has been limited by numerous factors ranging from messy experimental practices, spiritual reliance on untested theories of play, and confused terminology. We are still alchemists of our trade, mixing two-parts impure story with one-part polluted game play with three-parts market voodoo.
As an industry, we need to go beyond the mystical hand waving that defines modern game design. It is now possible to craft, test and refine practical models of game design built from observable patterns of play. We can describe what the player does and how the game reacts. Recently, we’ve begun to crack open why players react to certain stimuli and are able to create models that predict pleasure and frustration.
This essay will describe one such model.
Fundamental Science Forms The Future
Diagram 2: Condensation polymerization of Nylon, (a substance not available to alchemists)
The bigger hope is to move our alchemical craft towards the founding of a science of game design. We currently build games through habit, guesswork and slavish devotion to pre-existing form. Building a testable model of game mechanics opens up new opportunities for game balancing, original game design and the broader application of game design to other fields.
The advent of basic chemistry gave us tools to build a new world of technologies far beyond that imagined by our alchemist forefathers. Plastics, engines, fabrics, power sources revolutionized our lives. It is a worthy effort to crack the fundamental scientific principles behind the creation of games.
2. The Foundations Of A Model Of Game Design
Where chemistry separated itself from alchemy by building testable models of physical atoms, a science of game design concerns itself with testable models of human psychology.
Many of the attempts to define games have focused on the mechanistic elements of the game, such as the primitive actions that the system allows the player to perform or the tokens that the player manipulates. The approach has been to treat games as self contained logical systems.
Mechanics and aesthetics are certainly important pieces of any model of game design, but in the end, such analysis provides little insight into what makes a game enjoyable. You end up with a set of fragmented pieces that tell you almost nothing about the meaningful interactions between the game as a simulation and the player as an active and evolving participant. Games are not mathematical systems.They are systems that always have a human being, full of desires, excitement and immense cleverness, sitting smack dab in the center. To accurately describe games, we need a working psychological model of the player.
Our player model is simple: The player is an entity that is driven, consciously or subconsciously, to learn new skills high in perceived value. They gain pleasure from successfully acquiring skills.
Diagram 3: The player follows clues to the acquisition of a new skill
Let’s dig into three key concepts in our player model.
Driven to learn
A skill is a behavior that the player uses to manipulate the world. Some skills are conceptual, such as navigating a map while others are quite physical, such as pounding in a nail with a hammer. This ties into an intrinsic motivation towards self-determination. “I want to do what I want to do. And skills help me get there.”
Driven To Learn
Play is instinctual. In low stimulation environments where we are not actively pursuing activities related to food and shelter, people will begin playing by default. Strong feedback mechanisms in the form boredom or frustration prod us into action. Given a spare moment, we throw ourselves into playing with blocks or dolls as children and more intricate hobbies as adults. It is a sign of our need for meaningful stimulation that solitary confinement remains a vicious punishment for the most hardened criminals.
The flip side is that we are rewarded for learning. The sensation that gamers term ‘fun’ is derived from the act of mastering knowledge, skills and tools. When you learn something new, when you understand it so fully you can use that knowledge to manipulate your environment for the better, you experience joy.
There is a reasonable amount of neuroscience available to support this claim. Edward A Vessel, a cognitive neuroscientist at the NYU Center for Neural Science writes:
“These “aha” moments, when a concept or message is fully interpreted and understood, lead to a flood of chemicals in the brain and body that we experience as pleasurable. It feels good to “get” it. The deeper the concept is, the better it feels when we are finally able to wrap our head around it.”
Upon the click of comprehension, a natural opiate called endomorphin, a messaging chemical in the brain similar in structure to morphine, is released. As humans, we are wired to crave new information constantly. In some sense, what you and I term curiosity can be interpreted as our brain looking for its next fix of deliciously fascinating information.
As game designers, we deal with the fun, boredom and frustration on a regular basis. It is good to recognize that these are biological phenomena, not some mystical or mysterious sensation. For more thoughts on the topic, I encourage you to have a quick read through Raph Koster’s book “A Theory of Fun for Game Design”
Players pursue skills with high perceived value over skills with low perceived value
Play is, perhaps counter intuitively, a deeply pragmatic activity. Our impulses to engage in play are instinctual, selected for by evolution because it provides us with the safe opportunity to learn behaviors that improve our lot in life without the threat of life threatening failure. We play because we are built to expect the eventual harvesting of utility from our apparently useless actions. We stop playing when we fail to find that utility.
The perception of value is more important than an objective measurement value. Humans are not creatures of pure logic. We know people exhibit consistent biases in how they weigh their actions. For example, they’ll often undertake bizarre risks because they are unable to properly evaluate statistical odds. We’ve also realized that people have substantial limits on how much information they can take into account when making any one decision. Many decisions are made based on highly predictable ‘gut’ reactions that have their own subconscious rules.
3: Interaction Loops
With our player model in hand, we can describe how the player interacts with the game.
The basic ingredients of a game are, if not standardized, at least well described in a variety of books and rambling by designers across the past decade or two. I’ve taken the basic ingredients of tokens, verbs, rules, aesthetics, etc and remixed them into a self contained atomic feedback loop called an interaction loop. Each unit describes how the player gains a new skill.
Diagram 4: The player follows clues to the acquisition of a new skill
An interaction loop feedback loop is composed of five main elements:
Decision: The player observes any known affordances and weighs possible outcomes.
Action:The player performs an action. For an interaction loop encounter by a new player, the action might involve pressing a button. More advanced atoms might instead require the player execute a batched set of actions such as navigating a complex maze.
Simulation: Based off the action, an ongoing simulation is updated. A door might open.
Feedback:The game provides some form of feedback to the player to let them know how the simulation has changed state. This feedback can be auditory, visual, or tactile. It can be visceral in the form of an exploding corpse or it can be symbolic in the form of a block of text.
Modeling: As the final step, the player absorbs the feedback and updates their mental models on the success of their action. If they feel that they have made progress, they feel pleasure. If they master a new skill or other tool, they experience an even greater burst of joy. If they feel that their action has been in vain, they feel boredom or frustration.
A shorthand diagram that I find useful for recording atoms is as follows:
Diagram 5: Our canonical interaction loop
For example, let’s dissect the act of jumping in Mario
Diagram 6: The interaction loop of the player learning how to make Mario jump
Decision: A player notices a button on the controller. They know from past experience, it is pressable.
Action: An inexperienced player pushes a button.
Simulation: The simulation notes the action and starts the avatar of Mario on the screen moving in an arc.
Feedback: The screen shows the user an animation of Mario jumping.
Modeling: The user forms a causal mental model that pressing the button results in jumping.
Implicit in this model is that the atom is often looped through multiple times before the user understands what it teaches. The first pass may only clue the user that something vaguely interesting happened. The user then presses the button again to test their theory and Mario once again bounces up into the air. At this point, the player smiles since they realize they’ve acquired an interesting skill that may be of use later on.
This Thing We Call Play
“Man is a Tool-using Animal … Nowhere do you find him without Tools; without Tools he is nothing, with Tools he is all.” – 19th century essayist Thomas Carlyle
Upon the acquisition of a shiny new skill from a skill atom, players experiment with it. They try it out in different environments and see if it does anything useful. This semi-random exploration is the classic ‘play’ activity that we see children perform. For example, when a new player masters how to jump, you’ll notice they’ll almost immediately start happily hopping about the level. On the surface, it is a silly frivolous activity. In reality, we are observing humanity’s instinctual process of learning in action.
In the course of experimenting, the player will occasionally stumble across something in the environment that gives them interesting information that might lead to the mastery of a new skill. At this point, you’ll see the behavior of the player become more deliberate. A mental model begins coalescing in their minds. In our jumping example, the player starts bumping against a platform. They may even reach the top of a platform. It is very common that skills acquisition requires multiple passes through the new skill atom before mastery is achieved.
Eventually, the player uses an existing skill to grok another skill. They experience a wash of pleasure and start the process all over again.
Chaining Of Game Mechanics
We can visually represent how players learn by linking our basic interaction loops together to create a directed graph of atoms called a skill chain.
Diagram 7: Two linked atoms
The skill from one atom feeds into the actions of another atom further down the chain. By linking more and more atoms in, you build a network that describes the entire game. Every expected skill, every successful action, every predicted outcome of a simulation, every bit of required feedback can be included in a simple, yet functional fashion.
A skill chain is a general notation that can be used to model pretty much any game imaginable. Your design can be broken down into dozens of simple atoms that link together to form a clear and easily readable map of how the game plays. The skill chain, with its ability to describe the player experience instead of the mere mechanics of the game, provides a far richer description of the meaningful moments that occur during gameplay.
How Players Interact With A Skill Chain
Players will travel from atom to atom like Pac-Man following a trail of dots towards the power pellet. They move from one skill to the next even when they have only a vague concept of the ultimate destination.Chomping up those dots is good.
One of our peculiarly human limitations comes into play at this point. Players are unable to predict the value of a new skill more than a couple atoms down the chain. As long as there is a new skill with potential value within our prediction horizon, players will pursue it. There may be no actual long term payoff other than the pleasure of the experience, but we don’t care. As long as there is a promise of a long term payoff and the short term rewards keep coming, we assume that there will be some final benefit from our efforts.
Diagram 9: Players have limited foresight
If you look at this from an evolutionary perspective, our behavior makes quite a bit of sense. Many useful skills take upwards of five to 10 years to master. During those early days of our education, the basic playful activities such as gossiping about which kids have cooties seem rather silly. Later on however, our mastery of politics, science, or in the case of the cooties, mating rituals, yields a hugely positive impact on our well being.
The just-so story here is that playful folks that instinctually engaged in long term learning with no immediate benefit were the ones that mastered agriculture, hunting and language. These folks thrived. Those that did not died off.
However, our brains never evolved to deal with modern games. The existence of a set of interaction loops that are tuned just to entertain us and that never actually lead up to a real world skill is something new to the world. At their most puerile, games are a grand hack. The minute by minute experience fits all our biological heuristics and sounds all the right bells. So we keep on playing. And we wonder why so many games have such horrible endings.
4. Status Of Atoms In The Skill Chain
A skill chain provides some rather useful information about the state of the player as they engage the game. Imagine that the skill chain is the instrumented dashboard that lights up with the player’s progress. At any point in time you can tell the following information
Mastered skills: Skills that have been recently mastered.
Partially mastered skills: Skills that the player is toying with, but has not yet mastered.
Unexercised skills: Skills the player has yet to attempt.
Active skills: Skills that the player is actively using. (aka the Grind)
Burned out skills: interaction loops that the player has lost interest in exercising.
Diagram 10: Icons for skill status
We’ve talked a little bit about mastered and partially mastered skills. Unexercised skills are pretty self explanatory. If a player can’t perform the actions necessary to understand a skill, that atom will never be exercised or mastered. Mastery flows down the chain and if players are blocked early on, they’ll never reach the further atoms.
The two states that are worth a bit more explanation are active skills and burned out skills.
The player only experiences the joy of mastery for an atom only once. After the moment of mastery, a biological feedback system kicks in that dampens the pleasure response to exercising those same pathways again. What was once exciting becomes boring.
However, players will continue exercising an already mastered atom as a new tool for manipulating their world. A mastered atom is as good as a shiny new hammer hanging from a workman’s belt. When a new opportunity comes up, typically in the form of an atom further down the skill chain, the player makes use of their new skill to advance their knowledge.
Players have enormous patience. They are willing to exercise a basic interaction loop thousands of times in order to achieve mastery of a higher order atom. Players jump innumerable times in Super Mario Brothers in order to reach more powerful skill sets further down the chain.
A skill that has been mastered and is now simply being used to activate other icons is represented by the lit light icon.
Diagram 11: Active Icon
Players don’t always bridge the gap between one atom and the next. They master a new skill, they play with it but fail to find any interesting use for it. This is known as burnout.
Diagram 12: Burned out icon
For example, suppose our player pressed the jump button. They performed the jump and we recorded their mastery of the skill. However, this particular player never figured out how the jump might be useful. Perhaps they didn’t jump near the platform and receive interesting feedback on the next atom.After a short period of experimentation with no interesting results, the player stopped pressing the jump button entirely.
When a player burns out on a particular atom, the consequences ripples up and down the chain.
Early Stage Burnout
In the example above, the Reach Platform atom will never be mastered. The foundational skills are not in place. In a deeply linked skill chain, a burnout early on can chop off huge sections of the player’s potential experience. You can think of learning curves in terms of managing early stage burnout.
Later Stage Burnout
On the other hand, a burnout later on down the chain can devalue active skills.
For example, assume we have a single platform in our jumping game and there is really nothing on it. The player jumps on the platform, discovers no interesting new activities and so stops jumping on platforms. This, in turn, atrophies the Jump skill, because if the player doesn’t need to jump on platforms, why would he bother jumping?
Burnout Is Our Gateway To Testability
Burnout is a very clear signal that our game design is failing to keep the players attention. As you watch burnout creep across a game’s skill chain, it is a signal that players will soon stop playing the game.They are becoming bored, frustrated and perhaps even angry.
Perhaps most importantly, we can measure when burnout occurs for an individual atom. This gives us, as game designers, unprecedented qualitative insight into how a particular design is performing with play testers. When you start tracking burnout along with the other skill states, you can visualize the problematic areas with great clarity and accuracy. The entire topic of measuring performance of a game through instrumentation of its skill chain is a rich topic for further exploration.
Diagram 13: Skill atrophy due to later stage burnout
5. Advanced Elements Of A Skill Chain
We’ve covered the basic elements of a skill chain and how to record that status of the player’s progress.There are only a few more pieces we need so that you can start building your own skill chains.
Pre-existing skills: How the skill chain is jump started.
Evocative Stimuli: How we represent story and other aesthetic aspects of modern game design.
Players bring an initial set of skills to a game. These skills always form the starting nodes of a skill chain. Accurately predicting the player’s existing skill set has a big impact on the player’s enjoyment of the rest of the game.
Diagram 14: How pre-existing skill feed into initial interaction loops
Lack Of The Correct Initial Skills
If the player lacks expected skills, they will be unable to engage the initial atoms in the game. In our example about jumping, imagine a player that didn’t realize that you need to push the button on the joystick in order to do something. Such an example may seem ludicrous, but it is one faced by many non-gamers whenever they are faced with a freakishly complex modern controller. Many game designs automatically assume the ability to navigate a 3D space using two fiddly little analog stick and a plethora of obscure buttons. Users without this skill give up in frustration without ever seeing the vast majority of the content.
It is very important to realize that such users aren’t stupid. They merely have a different initial skill set. One of our jobs as designers is to ensure that the people who play our game are able to master the game’s early interaction loops. Ultimately this means making an accurate list of pre-existing skills for the target demographic and building our early experience around those skills. Don’t assume skills that may not be there.
Pre-mastery Of Skills Taught In The Game
The flip side of all this is that if players have already mastered existing skills, the process of mastering early atoms is likely to be quite boring. When a player, who has completed a dozen hardcore titles, plays a game sporting a 10-minutes navigational tutorial they become bored. All the reward notes are sour because their jaded brain doesn’t react at the appropriate points. If a game doesn’t teach the player anything new, the player is very likely to experience burnout on the early atoms.
Targeting the correct set pre-existing skills is a balancing act. If you choose correctly, you’ll end up with an ‘intuitive’ game that players enjoy. If you choose incorrectly, you risk frustration, boredom and inevitable burnout.
Evocative Stimuli using Arcs
Games are laden with story, setting, and imagery intended to evoke a particular mood and other intriguing but mostly non-functional elements. Gamers derive great pleasure from this feedback. We can represent much of this mélange of artistry with the use of a special type of atom known as an arc.
Arcs are atoms that the designer knows will never result in a useful in-game skill, but that still evokes the past experiences or mental schema. When the player experiences the information cues, existing player memories are activated and the brain greedily sucks up the clues. For example, many players have pre-existing associations with mushrooms. If you are of a certain age and a certain liberal background, you may even own a rainbow colored T-shirt that sports a mushroom or two. When such a person plays Super Mario Brothers for the first time, they are quite likely to perk up at the sight of magic mushrooms. An interaction loop in their brain is activated, they start activating ideas about mushrooms, and begin free associating why might dear Miyamoto have placed such a counter culture reference in the game.
Of course, the reality is that for the psychedelically minded, the mushroom imagery is flavor only.
Now these evocative arcs can be useful! If the player had read Alice in Wonderland, they might associate mushrooms with changing in size. In this case, the fact the mushroom makes you bigger already has a pre-existing mental pathway and when the player experiences it again, they are essentially reinforcing that path. So evocative arcs can influence what mental schema (existing skills) players tap into when forming models of cause and effect.
The downside of evocative stimuli is that most players rapidly burnout on such sleights of hand. The first time you see the mushroom, you might think it’s ‘mushroom-y-ness’ interesting. The second time, you see it as its utilitarian nature: An icon representing access to a tool (growing larger) that helps you navigate the world more efficiently.
We’ve covered a lot of ground in this essay. Hopefully, the diagrams give you a good understanding of how to describe a game using skill chains.
Using Skill Chains
As a tool, I’ve found that skill chain diagrams dramatically improve my understanding of how a game works, where it fails and where there are clear opportunities for improvement.
Creating a skill chain provides you with the following information:
Clearly identify the pre-existing skills that the player needs to begin the game
Clearly identify the skills that the player needs to complete the game
Identify which skills need feedback mechanisms.
Identify where the player experiences pleasure in your game
Alert the team when and where players are experiencing burnout during play
Provide a conceptual framework for analyzing why players are experiencing burnout.
Though it takes a little practice, interaction loops aren’t all that complicated to define and are really no more of a burden than writing unit tests for a chunk of code.
Skill chains are a deep topic and we’ve described only the most basics aspects of how they function.Further topics of inquire include:
I like to imagine that models like skill chains will help raise the level of intent and predictability in modern game design. With the concepts in this essay, you can start integrating this model into your current games and collecting your own data. We’ve got some immensely bright people in our little market and it is almost certain that they can improve upon this foundational starting point. By sharing what you’ve learned, we can begin to improve our models of design. What happens if game designers embrace the scientific process and start to build a science of game design?
The alchemists of ages past dreamt of turning lead into gold. They performed mad experiments with imprecise equipment and questionable theories of how the universe worked. Modern game designers are not really so different. Those not simply here for the sake of profit instead rally around equally fantastical dreams such as creating a game that makes the user cry or enlightening the world with games of politics or hunger. We crib cryptic notes from past successes and chortle merrily when our haphazard experiments manage to mildly entertain our audience. We are on the leading cusp of deep human / software interaction and yet we know so little.
It is only by gaining a deeper understanding of the fundamental building blocks of design that game designers will gain the power to break free from the accidental successes of the past. With practical techniques gained from controlled experiments, we will create radically effective new applications. When we have our basic chemistry, our basic systems of measurement and our basic atomic theory, perhaps then we can consistently build games that tap into the heart of human psychology.
The reproducible application of psychological manipulation of individuals and groups using software is big heady stuff. In the short term, I would hope that a deep understanding of models like skill chains help us crack open the rigid craftsmanship of existing genres so that we can build better, more potent games. Long term, it will be interesting to see what world changing uses we can find for our ever improving psychological technology.
References And Notes
Workshop on using interaction loops and skill chains
Irving Biederman and Edward Vessel, American Scientist, May-June 2006
Abstract: “From hand-held DVD players to hundred-inch plasma screens, much of today’s technology is driven by the human appetite for pleasure through visual and auditory stimulation. What creates this appetite? Neuropsychologists have found that visual input activates receptors in the parts of the brain associated with pleasure and reward, and that the brain associates new images with old while also responding strongly to new ones. Using functional MRI imaging and other findings, they are exploring how human beings are “infovores” whose brains love to learn. Children may enjoy Sesame Street’s fast pace because they get a “click of comprehension” from each brief scene.”
Relationship of Skill Chains to MDA (Mechanics, Dynamics, Aesthetics)
This is a question that has been posed on occasion. MDA is a game analysis framework put forth by Robin Hunicke, Marc LeBlanc and Robert Zubek. It is one of many descriptive techniques that categorize the elements of a game. MDA is particularly useful to new design students because it has the key insight that the player experience (what they call Aesthetics) is a second order effect derived from playing the rules of the game.
The major difference between the two approaches is that MDA stops there. There is little attempt to model how rules and feedback produce the actual player experience with the game. There’s just these fluffy, conceptual categorical buckets. Since there’s no casualty, MDA analysis also fails to provide any objectively testable structure. With skill chains, you can always hook up logging software and observe where atoms light up and where they burn out.
As game budgets expand once more, the success of a title often depends on producing large amounts of high quality content. This is not a trivial task. Mistakes setting up your content plans can easily result in panic, shipping delays, scope cuts, rework and crunch. Modern developers live on the content treadmill so we might as well embrace it.
For a long time I’ve been interested in content architectures, the tooling and data structures behind what content we make. This somewhat obscure topic drives much of the production efficiencies available to a team. A poor content architecture can easily result in an equivalent player experience costing 10 times as much time and labor. That’s the difference in output between a 30 person team and a 300 person team; a lot of money and human life to naively misspend.
Who this is for
Producers: Anyone above level of associate producer should know this topic down cold. To paraphrase the words of designer Crystin Cox, “I want to be able to ask a producer whether I should use a placeholder or a vertical slice when building an experience.” To a large degree this is your job since these early decisions drive much the team’s ability to deliver on a schedule and adapt to unexpected changes.
Designers: If you decide what the team makes, you owe it to them to also understand the best possible methods of building the desired outcome. Design leaders maximize the impact of the experience they deliver while working within a fixed budget.
Engineers: You’ll be building many of these tools and pipelines. Wouldn’t it be nice if they were useful? Wouldn’t it be nice if other disciplines could communicate their needs? Knowing how to think about serving content authors improves the game, your work and results in happier cross team relationships.
What we’ll cover
Content architectures are a broad topic best approached holistically. Existing content architecture experts are usually veteran developers who have multiple games and dozens of failures under their belt. Unfortunately that means this essay needs to spend time intro-ing the basics before we get to the more advanced considerations. Apologies for the slow build!
Terminology: Basic definitions of what we are manipulating in a content archichitecture.
Concepts: Key concepts that help us think about our content.
Constraints: What are the specific content choices for a given project that shape our architecture?
Basic architectural patterns: How might we organize our content?
Advanced patterns – Manual composition: How do we manage rigidities in the content pipeline?
Advanced patterns – Automated composition: How do we reduce rigidities with automation?
Meta – Tool authoring: How do we build tools that multiply our authoring efforts?
Let’s start off with some basic definitions that work for most forms of content you’ll run into. I’m abstracting the discussion away from specific content (levels, character, textures) so we have the building blocks to think conceptually about any content in our game. We want to get to “content algebra”, instead of always asking “how many apples does Bob have?”
Content: Content is an authored set of data intended to be displayed in some broader game system and consumed by the player to create a meaningful experience. More traditional forms of content include things like a chapter of a book, or a painting in a museum.
We often think of game content as generic media like 3D models or text. And that’s definitely where we spend a lot of effort. However each game also contains data files like a loot table, level progression or powerup. These need to be designed with care.
Chunks: Traditionally content comes in the form of content chunks. This is a piece of the player experience that is standardized and reproducible. Examples of chunks include
Level: A game like Super Mario Bros has discrete levels. Each module is self contained and consists of a set of platforms, enemies and win conditions. A game is then composed of multiple levels.
Player character skin: A bundle composed of 3D model, UVs, textures, shaders, animation rigs and state machines. The player has a choice between multiple skins.
Weapon: A set of properties for weapon damage, rate, cooldown. As well as associated art, economics costs, etc.
Player buff: A set of modifications that occur to an external set of properties. Along with constraints on when and how the buff is triggered.
The contents of each chunk differ. Weapon A has different data than Weapon B. But the data structure and how that data feeds into other systems is shared.
Standards: Standards are rules and constraints that define a content chunk. They help reduce risk by removing unexpected variability and associated thrash. They help improve quality by focusing an author on excelling at a particular well-defined space. They help improve efficiency by eliminating common blockers and streamlining workflow. They help teams scale, by allowing multiple authors to work coherently on the same project.
Sets: These chunks are organized into sets. You might have 20 levels in a game. That’s your game’s set of levels. Or 500 barks. That’s your game’s set of barks.
Composition: Chunks can be assembled together into new composite chunks. A level chunk is a composite of enemies, level tilesets, powerups and other modular elements. The level designer likely did not create any of these sub-components, but they put them together to form a unique player experience.
Composition is a creative act of authoring. Someone needs to make deliberate choices on what is included and its relations with the other elements. Even a writer composes words they did not create on the page. A painter composes color they did not create on a canvas.
Dependencies: When you split content up into chunks and string them together in a content architecture, we create dependencies. In order for content to work or have meaning, it requires that other content or systems are functioning exactly as expected. The act of creating chunks always creates dependencies since there’s a fuzzy line for where content wants to reside. Standards help catalog and isolate dependencies. Later on, we’ll see many of the tools for managing content architectures are about structuring dependencies in a useful manner.
Questions worth asking about your game: But we often don’t take the time to think of ‘words’ or ‘color’ as standardized chunks. They are just the invisible air we breathe. Eliminating our blindness to the ‘intentionality of the default’ is the first step one must take. As a content architect you need to expand your perspective and see these elements as explicit design choices.
What are your chunks?
What are their standards?
What are their sets?
How are they composed?
What are their dependencies?
In order to design a content system, it helps to have a mental model of how content ‘works’. Here are some of the big picture rules of content authoring.
Content delivers value: We author and deliver works of art to players in order to provide them with meaningful experiences. We can build content that harms or wastes a player’s life. Or we can build content that enriches their life.
Content is consumed: Content can be experienced a certain number of times before players feel like they understand the experience and are ready for something different. Some content becomes a touchstone for an ongoing socio-economic player ritual, but most is used and then put aside. The player exhausts their motivation to return to the content.
Consumption is iterative. Players experience a chunk of content 1 to N times before they move on. Chunks that are experienced once and then discards are seen as Highly Consumable. Ones that can be experienced many times without being discarded are seen as Evergreen.
Authoring is iterative. How does an author deal with the uncertainty inherent in a diverse audience’s consumption of the content? You iterate. You deploy the content and observe the reaction of those consuming it. Then you revise the content and test once again.
At the most basic level, authors do this with themselves in a process called ‘self playtesting’. They switch between a creation state and a consumption state. With writing and painting this happens moment-by-moment in a tight iterative loop. For example when writing, the following happens thousands over times:
I write a word
Then immediately read what I wrote and react.
Then I revise.
Games have longer feedback loops than many forms of media. As we author, we can imagine in our minds how it might play out, but our existing skills and understanding of the game systems pollute our empathy. Some systems like multiplayer, economic or long term progressions yield surprising results or large play surfaces. Self-playtesting ends up being unreliable. So we need to rely on much less frequent cycles of playtesting with others.
Authored content exhibits varying degrees of leverage: Leverage is a measure of efficiency, how much value the content delivers relative to its cost.
Leverage = Meaningful contribution to the player experience / Sum of total authoring and tooling costs
High leverage: An evergreen piece of content (such as a National Anthem that that took hours to write and is used millions of times over hundreds of years) is high leverage.
Low leverage: A comic in a book that took days to draw, but is viewed only once and then forgotten is considered low leverage.
Other factors: The full cost/benefit structure includes the cost of set up the toolchain, the amount of content you make and the content pipeline everything needs to flow through. We’ll talk about these more in the Constraints section below.
Leverage is a useful concept used in planning, but understand that it is inexact. Once content hits an audience, they may choose to elevate what the author thought of as a minor element to evergreen status. There are scenes from a comic like Calvin and Hobbes that were just as expensive to create as any other scene, but their resonance with the audience turns them into a much greater experience.
Building content architectures involve an upfront cost: You need to pay for tooling. And learning the tools. And iterate on standards for your content. This is all before the team as author any shippable content chunks.
Traditional marginal media content costs are mostly linear. Once you’ve standardized on a chunk of writing, video or imagery, there are few meaningful economies of scale. The cost to create one comic panel is roughly the same as the cost to create a similar panel 100 pages later. Most efficiencies occur by descoping standard chunks and cleverly interweaving low cost chunks with high cost chunks.
In games, we can create non-linear content architectures: Content architectures can introduce non-linear leverage into the process of content creation. Such that for each additional hour of author labor, we get some more rich player experience out than if we had naively been making traditional content.
Diagram 1: Stages and costs of content chunk creation
This graph helps visualize trade offs.
A – Tooling Complete
B – Initial learning and prototyping cost paid, first content chunk created.
C – Break even on your fancy content pipeline. This is the first time all your work has a net benefit relative to just manually creating stuff from piecemeal.
D – Exhaustion sets in. Additional meaningful content is expensive because the player gains less value from each additional chunk of this type of experience.
Let’s say your goal is to create a high leverage content architecture. The first place to start is by understanding your constraints. I couch these primarily as questions a team needs to answer, since the answer will vary substantially based on the project. You’ll need to consider both sides of the leverage equation:
What is the cost of designing, building and testing the content?
What is the effectiveness of the content?
Cost – Prototyping: The goal here is answering the question “Would this imagined content deliver the experience we desire and how?”
What are your goals for this type of content?
How long will it take you to establish and explore the playspace limits for a particular class of content chunk?
What is the risk that this prototyping effort won’t pay off?
What are lower risk fallbacks if the prototyping fails to pay off?
Cost – Standardization: You need to create standards that eliminate edge cases and prevent the creation of weak content. This step is not free and often ignored.
How long will it take to create easy-to-communicate standards for the prototyped content?
How does the content fit into the content pipeline?
What tools are required to achieve desired efficiencies?
Cost – Iteration count on each chunk during production: Iteration is also not free and is commonly ignored.
How many implementation->playtesting->feedback iterations are required before the content chunk is polished and ready for the player?
Cost – Iteration speed: The speed of iteration typically determines how many you can fit. In my experience, the quality of content is directly correlated with the number and frequency of polishing iteration.
How long does it take to iterate on a chunk? Consider author iterations, where the author is testing based off their own playtesting perceptions. And also consider external iterations.
How can tooling be improved to speed up iteration?
Cost – Human resources: Each iteration heavy process needs to be designed, tested, optimized and mastered by living human beings operating at human-speed not computer-speed.
How many people across various disciplines does this chunk cost to make?
How long does it take people to master the creation of a chunk?
Cost – Technology: All that data only works because it hooks up into code.
What is the cost of the tech that supports the content?
Can you reuse or extend existing code when you add a new content use case?
What sort of dependencies and rigidities do certain tech choices create?
Cost – Game systems: Game play is a complex interlocking system of game mechanics and associated feedback loops. The content expresses and explores the playspace created by these systems.
What is the base cost of the game mechanics the content feeds?
How much content and of what types do game systems need to be fun?
How many game systems need to be in place before you can test the validity of the content?
How long does it take to balance the content across the various systems in order to test it?
Cost – Communication: As you add more people, their interdependence often increases the need to talk through design intent and issues. Hand-offs can be expensive or sources of blockage.
What are the hand-offs?
How do you make the hand-offs as efficient as possible? Where are blockage, delays or backlogs occurring?
Cost – Risk of failure: No creative undertaking as a certain outcome. Risk is converted directly into a cost in the form of rework or needing to implement an alternative design. For any specific class of content, you might not pay the cost, but over time the project as a whole will pay a higher cost for higher risk content.
What is higher risk content? Time and resources are two factors that have a giant impact. But the factor I’ve found most predictive is the past experience of the individual or the team. An experienced team will often know how much time and resources they need. An inexperienced team will be too busy exploring what they don’t know to budget effectively.
In order from lowest risk to highest risk
Content you’ve successfully made many times.
Content you’ve made 1 to 2 times.
Content similar to something you’ve made before.
Content that has clear playable examples in another game and you seek to copy the identical functionality.
Content inspired by something someone has made, but has not been demonstrated.
New content that has no direct analogue.
Note that there is both individual risk and team risk when talking about experience. If a task involves lots of people and they have not worked together before, they have a much higher risk of failure even if an individual contributor successfully worked on a similar project in the past.
One might think this sort of risk spectrum results in cookie cutter content. But that is not necessarily so, especially with smaller teams. A style of content produced by someone who has spent years working on an uncommon set of skills will often be lower risk than that same person trying to copy a popular style of content. Always consider the fit between creative skills and content, not just popularity or examples.
What is your team good at? What are they experienced at?
What content standards can you borrow from other projects?
What is the risk of failure for this chunk? Does it fit in a portfolio of risk?
Are your fallbacks if a prototype fails lower risk?
Cost – Late Revision: Only at the end of the project does the team start getting high volumes of quality player feedback. With live games, the bulk of the critical feedback will happen long after launch. So now you’ll need to update key load bearing chunks of content. What was ‘finished’ needs to be opened up, rebalanced, revised or completely redone.
Late revision is particularly problematic for games-as-a-service. If your initial launch is even slightly successful, the title will spend the majority of its life undergoing constant revision. The rigidity that you bake into content becomes a major constraint on the cost of future updates and whether or not your team can sustain the project. You live with it forever. Teams who only know single player games struggle here and need to reevaluate most of their assumptions.
What does it cost to change a chunk of content after it is finished and tied into all other dependencies? What does it cost to replace it?
What does it cost to change a set of content? What does it cost to replace it?
Diagram 2: Design insights happen throughout the schedule not just at the beginning.
Diagram 3: If your content pipeline is not amenable to late state changes, you’ll fail to capitalize on most of your design insights.
Total marginal chunk cost: So there are lots of costs that go into making a chunk of content. Be sure to honestly measure and summarize these. Blindly insisting on an optimistic fantasy helps no one.
After paying prototyping and standardization, what does it really take to call one additional chunk of content ‘finished’? Include iteration cost, human resource cost, communication cost.
In the cursed wail of every team edging like Zeno towards the finish line, what is the true cost of calling content “done”?
Effectiveness – Load bearing: We now can talk about the other side of the leverage equation. Let’s start with how some content is more important than other content. A game has pillars made of key experiences that it needs to deliver in order for it to be successful. This is the heavy weight of player, publisher and market expectations. Various mechanical systems and content support those pillars. Those that bear the most weight and would hurt the game most if they failed are considered “load bearing”.
It is also worth identifying content that is “non-load bearing”. These are places where you can use lower cost content. You might reuse existing content or apply generic purchased assets. Alternatively, you can use the fact that non-load bearing content is low risk in order to experiment and be playful. I often find some non-load bearing chunks like item descriptions and inject them with my quirkiest writing. Or give authoring of this content to someone who is learning. If this content fails, the game won’t fail.
What are the pillars of your game?
What content is critical to supporting those pillars?
Is a particular type of content load bearing? Or is it non-load bearing?
What is the fallback if this content doesn’t deliver on its promise?
Effectiveness – Optimal set size: No practical system is scale free. On one hand, you want this number to be as high as possible in order to maximize the prototyping investment. However, standardized content chunks also fade in effectiveness over time as well. There is often less marginal utility to a player as they experience the 200th level compared to the 1st level. And if you are crazy enough to make a 5000th level, the utility can turn negative. Some players start to see the patterns behind your standardization and will ignore or resent non-meaningful variation.
What is the size of the playspace this content addresses? Is it small? Is it large?
What is the sweet spot for set size where each chunk of content remains distinct and meaningful to the player?
Effectiveness – Resonance with real player motivation: This should fall out of the exercise of determining if content is loadbear, but it is worth treating as its own thing. The best content helps players fulfill their deepest intrinsic motivations. When content and system support support the various factors of self-determination theory, we see increased retention, engagement and player satisfaction.
Does the content facilitate competence? Does it help the player learn skills? Or feel a sense of growth?
Does the content facilitate autonomy? Does it help the player feel like they’ve chosen their path? Does it help them express their identity?
Does the content facilitate relatedness? Does the content connect the player with others who support them? Does it enable reciprocation loops that deepen relationships?
Basic Architectural Patterns
Now that you’ve got a bunch of knowledge about what type of content you need to make, you need to build the system that helps you make that content. Here are some techniques I think about when building high leverage content architectures.
Take these with a grain of salt. I find that as a team gains experience in a domain, they develop new tools and vocabulary custom tailored to the tasks at hand. So I encourage you to set strong constraints and then deliberately grow your team’s ability to experiment with and iterate on more efficient tools.
Each of the following tools will likely take your team a full game or two to start to understand and master.
Lego blocks: Embrace composition by building player facing experiences out of highly reusable standardized content chunks. Consider a non-lego block design like early graphical adventure games. Every pixel on the screen was hand placed. Every interactive puzzle was hand-scripted. Deep in the code there were common structures, but there was very little modularity or reuse.
Consider a game like Super Mario Bros. The world is composed out of standard block types, standard enemy types and standard player moves. Tiles are placed on a grid so their relationship to one another is highly predictable. The cost to create a screen of a Mario game is much less than the cost to create a screen of an adventure game. (Thankfully, no one measures gameplays by screen any longer!)
Modular blocks intended to be composed together are not limited to tiles. In the puzzle game Road Not Taken, each object was built out of a stack of standardized behaviors. A block might have the ability to be pushed. Or it might have another ability to slide if pushed. Or it could break. Or duplicate itself. Or move on its own. And by mixing and matching a relatively small number of these lego-like behaviors, we built out dozens of distinct objects.
What are the legos of your game?
What pieces of your games are not standardized building blocks? How might you turn them into reusable legos?
How do your legos snap together to build interesting compositions?
References: Lego blocks usually use referencing where this is a master object stored in some central location and then an instance of that content is used in the composition.
You may store instance specific properties. There’s a trade off here. In general you want to specify the minimum number of instanced properties as possible since global late revision that touch a 1000 instances are expensive. It is better to store the bulk of the behavior on the master so that if you can make a change in one central location, the change happens everywhere. However, some instanced properties let you adapt the instance to the current context.
What properties should be on the master?
What should be on the instances?
Templates: As you compose structures using your reusable chunks, you discover that there are some patterns you repeat again and again. Certain sub-elements might shift around, but there’s a recognizable boilerplate structure you keep needing to rebuild. To minimize work use templates, reusable structures that have blanks the author can fill in details.
Consider rooms in a Diablo-like game. There was a set of templates that defined each room. During level generation instances of the rooms would be plunked down and connected with hallways. However, inside each room a subset of different objects or enemies might appear. So even though there were standardized, reusable templates, each instance of the room felt different.
What are your common reusable patterns? Can you turn those into templates?
Which elements in those patterns can be varied in order to provide players with meaningfully different experiences?
Decoupling: As we’ve discussed, splitting content into chunks and assembling them into compositions creates dependencies. Dependencies aren’t always bad. References are a form of dependency where instances depend on the existence of their master. However there are many dependencies that increase both initial content creation cost and future iteration costs.
For example, recently we built a quest that required you to purchase an ingredient (onions!) from the store. The contents of the store were defined in chunks of data. While the quest asking for store items was defined in a totally different chunk. If the store didn’t have onions, the quest was not completable. Which just so happened to break the entire game.
This showcases some common issues with dependencies.
Difficult to spot: It wasn’t obvious looking at the quest that there was a dependency on the store. The quest config said nothing at all about where you get an onion and it was only by sorting through the entire config system we found the connection. I call this content pattern “Chunnel Design” after the famous tunnel that goes under the English channel. They dug the tunnel from both the French side and the English side with plans to meet up blinding in the middle. If either effort had been off, the tunnel wouldn’t have connected.
Expensive to fix: Instead of making a change in one location, we needed to make a change in multiple locations. With tangled dependencies, this can get quite expensive. In one project, we had to update 5 separate locations to get an item to show up in the store. A five tunnel chunnel. 🙂
Ambiguous ownership: The quest wasn’t able to specify anything about how a player gets the onion. And the store had no idea that someone might want the onion. Neither piece of content was responsible for making sure that the desired experience was delivered to the player. Even if we did fix the issue, it wasn’t clear we fixed it in the right spot. And the next time we fixed a similar issue, we might make a different decision. Which leads to edge cases and more unexpected problems later on.
Decoupling at the most basic level is the process of eliminating unnecessary problematic dependencies.
What dependencies are helping speed up authoring?
What dependencies are slowing down iteration?
Can you remove these costly dependencies?
Can you explicitly state dependencies in your data so they are obvious upon inspection?
Can you give ownership of the experience to fewer chunks, instead of spreading it across multiple chunks?
Can you add automated validation so you are instantly alerted when dependencies break?
Content pipelines: As you start to engage with both composition and decoupling, we start splitting complex content into stages of work. Early stages of work, composed of templates and referenced masters feed into later stages of composed instances. Each stage has its own required tools, processes for ingesting data from previous stages and processes for exporting data to subsequence stages. Put it all together and you’ve got a directed graph called a content pipeline.
Diagram 4: Sample content pipeline
A content pipeline might involve the following three sub-pipelines of character art, terrain art and behavior code feeding into a finished level. Notice that various content chunks pass through multiple stages in a fixed order across many tools in order to create the final output.
Directed pipelines have some interesting properties
Stages are composed in a fixed order: This ensures reproducibility of results. Selecting the right order is a big design choice that impacts your content production schedule. I often think of this as “up pipeline” and “down pipeline”. Changes at base stages cause ripple effect down pipeline. Changes down pipeline have fewer later stage dependencies, but have a linear cost to make change. Which can be a very expensive number if that surface area of content at the end of the pipeline is large.
Manual composition: Order matters so much because often the earliest pipeline stages are created and locked down. Then subsequent stages are built on top and the earlier stage is never changed. In platformers, designers build a chunk of player movement with locked jump distances. And then the layer of level design is built on top of this. Manual composition creates strong dependencies. Changing or replacing a locked stage invalidates the later stages. If those stages (such as hand crafted level) took time to build, naive changes to earlier stages can cause immense project thrash. Managing scheduling of locked stages is one big reason why we have producers and those miserable gantt charts. There are tricks to get around this issue, such as using stubbed in dummy data or placeholders. We’ll talk more about that below.
Automated composition. One way of reducing these dependencies is to automate the composition process. Procedural generation is one form of this. The rooms in a rogue-like are placed via an algorithm. If the rooms get bigger, that constraint is passed up to the next layer and the hallways connecting the rooms adapt accordingly. Unlike manual composition, the author can then make a change on almost any stage and the end content is rebuilt automatically. (Photoshop was so transformative because it pioneered automated layer composition in the visual arts)
Content at each stage can be referenced: Each layer is defined in a master chunk and instanced.
Automated composition + referenced chunks offers immense leverage by reducing the cost of authoring iteration. A content author can compose multiple layered compositions. And if late changes need to be made to ever base layers, it is less of an issue.
Observation – Non-linear leverage appears in how you build the pipeline: What we are seeing here is a key truth. Non-linear leverage in your content architecture rarely comes from how you structure your base chunks. Instead it appears in how you build the composition of those chunks. In my experience, the more you can move into hierarchies of composition, more leverage is available. This introduces its own complexity and cost so it isn’t a silver bullet.
Advanced Patterns – Manual composition
Sadly, it is rare that we can apply automated composition to every composition process in the pipeline. Anywhere there is manual composition, the order that elements are created matters. This presents some challenges:
How do you schedule work so the right stuff is complete before the next stage needs it?
How do you reduce the cost of making mistakes?
There are some common strategies. Any or all of these can be mixed and matched.
Vertical Slice: Build out a representative segment of the final content at full fidelity, test it to verify validity. Then meticulously lock down standards for each pipeline stage. In production, build content to these standards and trust that the end result will deliver on the promise of the vertical slice.
Issue – Slow iteration: However, building the vertical slice is expensive and leads to slow iterations. Imagine building out a whole level with complete mechanics and final art, discovering it doesn’t work and then throwing that away. I think of it as “Building the game five times” More often than not, teams get into the second or third iteration and are canceled.
Issue – Bureaucracy: Another issue with vertical slices is that it puts immense pressure on the standards. They must be perfect and they rarely are. The answer is often more documentation. This acts as an organizational tendency for large bureaucracies and large teams where waste is common. Due to rigidities in the system, change — when it does occur — is often a destructive coup or pogrom. Vertical slices are very common in AAA.
Bottoms up design: Identify most core “up pipeline” stages. Prototype them. Test them. Ensure they are fun. Polish them to a high degree of fidelity.
Now lock down that element of the design. Then move onto the next stage of the pipeline that builds on the locked down stage and repeat.
For example, if you are building a platformer, build, polish and lock down the most perfect jumping you can create. Then build a small level with blocks based off jumping so your game grows like an onion from the innermost layers. When you hear the advice “Focus on a fun core mechanic” it is usually a sign of bottoms up design.
Issue – Highly systemic games: An issue here is many games require multiple interlocking systems to be in place before you know the game is fun. Consider a game like Animal Crossing. It certainly has central mechanics like chopping trees and running around. But (having just worked on a game in this genre) until economy, narrative, pacing, affordances, inventory, other minigames are all in place, the game is desperately unfun.
Issue – Late stage changes: The other issue is again one of managing late stage changes. If you discover that you screwed up an aspect of the core gameplay early on, it can be expensive to pay the cost of that change rippling out across all the dependent layers of the content pipeline. An MMO (Age of Conan) baked the timing of their attacks into their female character animations. When community playtesting suggested they needed to speed these up, it was an expensive fix. The early assumptions baked into the content architecture bit them.
Placeholders: Build a vertical slice of your game, but fill it with low fidelity placeholder content. This lets you test the game quickly and identify issues. And since the placeholder content is relatively cheap to make, throwing it away doesn’t destroy your budget. As you become more confident of the validity of the work, you start refining and polishing.
Placeholders can be used with either vertical slices or bottoms up design and they inherit most of the same issues. Bottoms up design often results in piecemeal prototypes that don’t really tell you how the final game will play. Vertical slices still result in a lot of throw away work, but since you are using placeholders, iteration is much less expensive.
A version of the vertical slice + placeholder that I’m intrigued by is the “playable skeleton”. With this strategy, you create a full version of the game that is playable end-to-end as inexpensively as possible. And then you perform subsequent polishing passes until the game reaches a shippable state. Thimbleweed Park was built using a similar technique with a full playable version of all game rooms complete and iterated on before final art was added.
Issue – ignorant stakeholders: A common issue with placeholders is that stakeholders do not have the critical sophistication to understand what is placeholder and what is final. Games have been canceled when an executive looked at a graybox level and wondered why this game they are spending millions on is so obviously ugly. Many teams end up with a secret rule to only show their publishers near final art and claim it is placeholder. The risk of getting that one ignorant person is too high for honesty. And education can be an impossible lift.
Issue – weak player affordances and feedback: Players also don’t always understand placeholders. There’s an art to picking comprehensible placeholders that work well in a placetest; abstract boxes and colors are almost never the right answer. Instead go for lower fidelity content that is still thematically and symbolically representative. If you are supposed to be petting a dog, use a picture of a dog. You’ll learn important lessons iterating on the right affordances and feedback even in a prototype.
Scaffolding systems via value anchors: The challenge of cheaply validating systemic designs is unsolved. It is common, even when using vertical slices or playable skeletons to spend months (or years!) in the dark valley of faith as various systems slowly come online.
For example, in order to test a crafting system, you need to build the crafting system, a UI, add the crafting content, add sources for that content, balance the sources, balance the crafting costs and finally anchor the crafted items to a functional purpose within the broader game. Even if you build the base crafting functionality quickly, the other elements take a lot of time and effort to coalesce.
One approach is to stub in value anchors early in production. This is usually a large sink that’s easy to build but still gives purpose to the various content systems. By building the anchor first, you have something to judge the activities against. Later you can still add secondary activities and more nuanced anchors.
When you prototype an RPG, you can create a player level that is fed with XP. Then you can have various activities like combat feed into XP. Player levels feedback into power which in turn allows tackling of harder monsters. Later you can add additional skills, enemies and resources that expand the system. But you’ll always have something playable from early one.
Animal Crossing has a large sink in the form of paying bells to upgrade your house. Whatever activity you do results in items that can be sold to generate bells. This creates a simple skeleton to slowly add more activities, more resources and ultimately more player goals.
Anchors are a bit tricky to get right because they aren’t purely mechanical. They are about setting up systems of value and tie into deep player motivations. The reason upgrading the house in Animal Crossing is interesting is not because of the mechanics of upgrading! It is because the house holds your decoration and items, which in turn act as a signal of identity, progress and status. In our Animal Crossing-like Cozy Grove, we’ve built a prototype that had upgrading your ‘house’ without the decorating aspects. It didn’t anchor player value at all.
Advanced Patterns – Automated Composition
There are also content architectures that open up when you enable automated composition. This is an exciting open area ripe for additional experimentation and research. I expect over the next decade or two, we’ll see a steady adoption of content architectures with various forms of automated composition. Here are a few ideas that I’ve found helpful to get you started.
Thinking of procedural generation as an authoring helper: Broadly, many of our existing tools in this space are termed “procedural generation”. But this field has problematic roots.
Researchers and new proc gen developers look for magical algorithms that provide fountains of surprising new content. Like old cranks searching for perpetual motion machines, they hope to one day crack the problem of an infinite experience generator. It is very much the perspective of an engineer who is not an artist but still wants to magically create without learning art. Though certain machine-learning efforts show promise, I personally have no interest in this particularly philosophical approach.
How does it make the content creator more efficient?
Does your content author understand the tool?
How can they create richer content that resonates with players?
How can they reduce iteration time?
How can they decrease the pain of late changes?
It is these last two area where procedural generation techniques shine. A good automated composition pipeline allows designers to make changes at most stages and have those changes flow through into the end experience with little to no manual rework.
However, procedural generation has a very real upfront cost. You need to abstractly design about your content and how it is assembled. And build all the tooling for those specialized chunks. And then build the automation that assembles them. This can cost many multiples of just building a single content chunk manually. Long term, you accumulate benefits in terms of cheaper iterations, but it is rarely clear that the initial investment was worth it.
Technique – Combinatorics: Do you need 1000 chunks of content in a set? If so, the cost of making that content is often high. And the post-release cost of changing that set is likely high as well.
One technique is to split your desired content into sub-chunks that are arranged in orthogonal sets. And then use combinatorics to generate an expanded set of final content that covers a wider surface.
For example, in our game Cozy Grove, we have shells on the beach. This is split up as follows
Shell type: This is a small set of 6 basic types like clam, conch, whelk, starfish, cowrie, coral. Each of these chunks contains a set of properties for image, price, chance of spawning.
Shell season: This set contains 4 seasons and color variations across those seasons. It also contains filtering information so shells don’t spawn in the wrong season.
Shell rarity: A set of five rarities. Each contains modifications to chance of spawning and price. Additional information about which bitmap to use.
Master shell definition: This tells how these 3 orthogonal sets are to be combined. It also contains any properties shared across all shells, like behaviors or dusting value.
Once each of those is defined, there’s an automated composition step that combines them all together to generate 120 (6 * 4 * 5) expanded variants. This also provides us with non-linear leverage where adding one new shell type adds 20 new shells to collect.
Issue – Bowl of Oatmeal: Combinatorics make it trivial to create what Kate Compton calls Bowls of Oatmeal, vast amount of content that is neither perceptually unique or differentiated. Players will tend to latch onto patterns shared across your spread of content and filter out non-meaningful variation. The infinite yet weakly differentiated worlds of No Man’s Sky are one example.
There are a few techniques I’ve found useful here.
Choose smaller set sizes that don’t trigger player exhaustion. Small, highly differentiated sets are often much better than large undifferentiated sets. If you split your placespace up too finely, you get oatmeal.
Use cheaper content like names to obscure the rote nature of combinatorial expansion. One thing we do for shells is give every combination of season and type a unique name. That’s only 24 names and took very little time. And concatenating “rarity + 24 unique names” results in strings that feel unique.
Technique – Chocolate Chips Cookies: Another composition pattern is to mix high fidelity setpieces in a low cost substrate. You can think of your templated setpieces as chocolate chips. Players love them, but if they repeat them too often, they burnout on consuming them. So they must be used sparingly. And the substrate they are embedded in is the cookie dough. Pleasant, filling, endlessly edible. But not very unique or interesting.
Individually, these two types of content have flaws. The dough is low cost, but also results in bland experiences. The chocolate chips are high cost and overly consumable. But they provide great peak moments. By creating a pacing structure so that just as players are getting bored of the dough, they encounter a chip, the value of both can be extended.
In rogue-likes, you author setpieces in the form of rooms and boss encounters. And then you embed those in levels composed of randomly generated hallways and generic rooms. Just when you are getting tired of slogging through endless corridors, you see a magical unique room that changes the rest of your run.
Imagining the final experience, what aspects deserve to be meticulously authored? What aspects are filler?
What are your set pieces? Prototyping, standards, production processes and costs. How often can each one be used before players consume them?
What is your substrate?
What is the ratio of set pieces to substrate?
What is the pacing of setpieces?
Advanced Patterns – User content
There’s also a set of more volatile patterns that involve leveraging your players. You give up control and risk quality, but sometimes gain new sources of content far beyond the resources of your team.
Player sourced testing: If you have a strong pre-release community, you can ask them to test the game. This is perhaps obvious, but in the language of the model we’ve been discussing, it facilitates getting back rapid and rich feedback on your iterations. This path also includes analytics.
Player sourced game content: You can go further and source actual content chunks. The most common example of this is crowdsourced localization, but it can be extended to other types of content.
In Realm of the Mad God, we crowdsourced much of the pixel art. Some important lessons from this and crowdsourcing localization:
User friendly tools: Players don’t have the patience to learn typical developer tools.
Robust standards: You need explicit, heavily validated standards. Developers need a path creating the content right. Players need to be prevented from creating the content wrong. These sound similar, but the latter is a much harder requirement.
Credit: Acknowledge their contributions. This goes a long way towards encouraging them to help out. We held contests that were very effective.
Mods: Post launch you can open your game up to mods. It is quite common for long lived popular games to source entire expansion packs or members of the ongoing dev team from the mod community. It is a gift that keeps giving.
In-game social content: You can also build tools inside of your game and incentivize players to create content for other players. There are many variations of this, but the main thing to note is that good UGC systems require you to design your game around them. Not a simple add-on, but something at the heart of the core loop. Examples:
PvP: Players act as enemies for other players. Counterstrike, Chess.
Base builders: Players create bases for other players to destroy. Clash of Clans
Building games: Players cooperatively build in a space together. Minecraft, Factorio
Design games: Players create levels for other players to play. Super Mario Maker, Dreams
Meta: Designing tools
So far we’ve been mostly talking about how you design your data and the structure it lives within. But don’t forget that authoring this content is a human process; someone needs to create by hand the work feeding these magnificent pipelines. And for that you need great tools.
The goal of tools: Tools multiply the efforts of content authors. They help create:
Richer content: Tool unlock the ability to make types of content that were otherwise impossible or too time intensive to consider.
Cheaper content: Tools enable an author to create a chunk of content of a desired quality level more quickly.
More polished content: By reducing iteration time and improving feedback, an author is able to quickly polish their poor rough drafts into something that delivers
Unless you get into generative systems, they tend not to be used to create large quantities of new content from some base seeds. That’s more the role of combinatorics or other proc gen techniques.
All game tools are custom designed: The first and most critical lesson you should learn is there are no standard tools. Every tool needs to be custom tailored to best fit the following constraints
Skills of author: What level of abstraction does the author work best in? What affordances help them do their job? Game tools generally target intermediate and experts.
Requirements of the content chunks: What is the minimal set of data that should be hand authored to make an effective chunk?
Ingestion of the content: What is the efficient process by which authored content is connected up with the rest of the game?
Iteration requirements: How do the tools enable the author to make and see changes rapidly?
I suspect some of you are thinking, “But I have Photoshop! I have Maya! I have Unreal! Those are standard tools.” Sweet summer child.
Modern commercial tools are powerful enough to do almost anything. Without identifying and serving the previous constraints, you will flail. So like it or not, you still need to establish standard practices, procedures, naming conventions and automation scripts in order to use even something as ‘standard’ as Photoshop to efficiently build your specific game. There will always be a tool design process for each game, even if it is built on top of an existing tool chain.
A process for designing your tools
Constraints: Identify the four constraints for a particular type of content: Author Skills, Content requirements, Ingestion Pipeline, and Iteration requirements.
Initial Sample: Create an example of the type of content you are making. Get feedback from stakeholders if this is what you want to build.
Brainstorm building the sample: Talk to a real author. Not an imaginary one, but an actual person who is going to be creating these things. How would they build this? Is there anything that exists that could be leveraged? What are problems and workflows they imagine will come up? Small, cross functional strike teams are very effective if multiple people are involved.
Build a first version: Try for the 20% of features that gets you 80% of the functionality. Test the pipeline of creating and ingesting and seeing the content in the game end to end.
Get an author to use the first version as soon as possible: Have them make real content that is expected to be in the game. Listen to their complaints and dreams.
Fix issues: Fix as many easy issues immediately. Prioritize one or two big asks for the next rev. Repeat these last two steps until the tool converges on something ‘good enough’; it will never be perfect.
Mistake – Not basing the tool features off real content needs: The most common pitfall that plagues tool creation is that feedback and iteration steps (2, 3, 5 and 6) simply never happen. An engineer makes a tool. They (or antsy producers) declare the tool finished and the rest of the team is told to use it.
Often this first pass contains the wrong features.
Or weeks are wasted over engineering aspects that are unimportant.
Or they’ll have built in major workflow problems that are invisible to them because they don’t understand that X is an operation you need to do 300 times in an hour, not once per week.
In the best case, content authors don’t even use the tool and find cheaper workarounds that get the job done. You just lose the engineering effort. In the worst case, content authors use the tool but they spend truly enormous amounts of wasted time jumping through avoidable hoops. The result is typically bad, hacky content that was expensive to create. And often needs to be thrown away.
Mistake – Delays building real content: The next most common pitfall is that there is a large time gap between the first version being built and an author uses it to create real content. In addition to general problems of skipping iteration, waiting too long has the following negative effects.
Change becomes expensive. Code and processes petrify over time. When an engineer still has the code in their brain, feedback from the author is much easier to implement. Small tweaks happen quickly.
Authors are never taught how the tool works. An immediate dialogue between the creator of a tool and the content author inevitable results in knowledge transfer. So many times I’ve realized that there was a keyboard shortcut already implemented for a laborious task. But the conversation happened a month after the tool was built and the engineer had forgotten.
Tip – Shadowing: Content authors infest old tools like fungus in a moist fecund jungle. Strange content will seep out of every crevice in the toolchain. Wait long enough and you’ll see workarounds built off hacks forming the foundation huge swaths of your content. Authors learn, adapt and push tools in ways many find horrifying. In the process, inefficiencies creep in as the tool ends up being used in ways it was never intended.
This is normal. And it is a good thing. Clever content creators are discovering new opportunities and new requirements that couldn’t be predicted until they put a few hundred (or thousand) hours into actually building the desired content.
The first step in supporting your fungal creators is to understand how the tool is used in the real world. Shadowing is when a toolmaker watches a content creator build something. It is like playtesting for your tools.
Share a screen as a content creator builds something. If they start doing something strange, ask them why. The answers are delightful.
Record how long things take. Is anything surprising? A fun exercise is predicting how long you think things will take, and then compare it to reality.
Brainstorm ways of reducing iteration time. Can steps be removed or automated? Can automated steps be sped up? How would you make this process 10X faster?
Review standards: Do they need updating? Can edge cases or expensive exceptions be avoided going forward?
We’ve only managed to cover the most basic aspects of game content architectures. I hope you find enough here of interest to explore further. Observe your own projects with a critical eye, experiment when possible and share notes with others. For deep skills that cross multiple disciplines, a document alone will never be enough.
Be humble. Content architectures are not a magical silver bullet for making more meaningful content with less effort. They can be a huge pain in the ass that introduces immense complexity, costs and risk into your game. Because of the effort it takes to build and tune them, they often delay your ability to start playing the game.
Diagram 5: When each incremental chunk is expensive and you need a lot of them, a higher leverage content pipeline might be worth your time.
Learning curve: Any content architecture and toolset has a substantial learning cost. The specific team using the system needs to understand and practice building great content with the tools. I don’t mean to frighten anyone, but this can take years. A level designer who has been using Unreal for 3 years will generally be a lot more effective than one who has been using it for 6 months. A team that has been building content for a specific genre on a specific engine will be much the same.
Often the best tools and processes are the tools you know: I’m regularly amazed at how simple tools and simple content in the hands of experienced, talented teams results in world-class experiences. The content architecture of a novel isn’t complicated. Just a series of chapters composed of a few hundred pages. Authored using bog-standard text editors. Yet we give that to writers with years of experience under their belts and amazing work emerges.
A lot of times, you can just throw talent at a problem. And if you need to scale up, throw more bodies into the pipeline. This path is always an option as long as you’ve kept your content modular and highly decoupled.
The final constraint: Hand-authoring is our ultimate pinch point. Humans can only work as fast as humans work. They need to dream, experiment, clumsily and slowly make mistakes. It takes “human time” to have moments of insight and creative breakthroughs.
Naturally, as beancounters and producers, we want to multiply those efforts. To stretch out that costly thing and increase efficiency.
But this hand-authored content is also the soul of our games. Dilute it too much and you destroy the very thing that provides value. “More, Faster” is not better if you are churning out garbage.
Your content architecture is a delicate balance act. Where do you put all your limited, beautiful, messy, human effort in order to provide the highest quality experience for the player? A worthy design challeng e.
For Project Horseshoe 2019, an annual game designer think tank, our workgroup investigated how economics could help promote prosocial values. You can read the other reports here: https://www.projecthorseshoe.com/reports/
Attendees: Randy Farmer, Joshua Bayer, Tryggvi Hjaltason, Erin Hoffman-John, Daniel Cook, Ray Holmes
“What should young people do with their lives today? Many things, obviously. But the most daring thing is to create stable communities in which the terrible disease of loneliness can be cured.”
— Kurt Vonnegut, 1974
Multiplayer games can help build a player’s social support network. What would game design look like if our goals included reducing loneliness, decreasing toxicity and boosting a player’s positive connections with others? This paper looks at how we might use economics, an often dehumanizing and antisocial discipline, to support prosocial design goals.
What’s at stake
A multiplayer game can impact our player’s social health. By designing poorly we can do great harm. The two most likely negative outcomes are loneliness and toxicity.
Loneliness causes stress in humans broadly, relating to feelings of vulnerability, and can also provoke scarcity mindset, in which a host of negative outcomes occur. Scarcity mindset is a stress-induced “tunnel visioned” state that causes short term thinking associated with long term net negative outcomes.
Further amplifying the urgency of this problem is that increasing life expectancy is exacerbating loneliness. In a dark reinforcing loop, advancing age makes us more likely to be lonely, while it is known that loneliness poses particular health risks to the elderly. As the median age of the world population increases, so too will the seriousness of the loneliness epidemic. There’s an opportunity to be seized as ever increasing numbers of older people play games.
As is further explored in our appendix “Towards an action-based framework for mitigating loneliness”, we can rely on the heavily validated UCLA Loneliness Scale to provide a baseline measure for what we mean by loneliness (see more in appendix subsection “defining loneliness”).
It is a truism that people are mean to one another on the internet. There’s a growing recognition that toxicity in an online community stems in large part from weak social design combined with weak enforcement of positive social norms.
At the root of much toxicity is the misdirection of our human need to belong. When humans lack membership in healthy, eudaimonic organizations, they experience stress and seek to rapidly remedy the situation, often in long-term sub-optimal ways. They may fearfully lash out at others, imagining that putting others down helps them rise in status. They join tribal groups who use their shared pain to wreak havoc in the world in an attempt to control their feelings of fear and loneliness. Being a troll can fill an absence of purpose (as we will describe below, purpose is a core component of conquering loneliness) and this feels better to many than the isolation of not belonging. Toxicity is a rational (though naive and self-defeating) short-term strategy that emerges in the face a lack of human connection.
We often think of toxicity as bad people taking advantage of a poorly hardened design. (There is a small amount of truth to this theory; a tiny percentage of players are sociopaths.) As a result, we attempt to treat the symptoms of trolling and griefing with ever-increasing moderation or community management.
However, we are learning that a badly designed social system actively generates toxicity, often at a rate that will inevitably overwhelm the human resources aimed at controlling it. The systems can inadvertently isolate people in closed-off loops where their fundamental social needs are ignored. In toxic systems every new user is potentially rewarded if they adopt toxic behaviors.
Increasing social support networks as an overall solution
The broad solution to the bulk of both of these issues is to design systems that build relationships between players: preventing fire, rather than creating fire which must then be fought. If people are thriving, with strong social support networks, shared goals, and opportunities to grow, they’ll be less lonely. And they’ll be less likely to act out in toxic ways.
The grand project of prosocial game design
There are numerous pitfalls facing designers who seek to increase their players’ social capital. These are organized into at least three major categories.
Psychology: Humans require a series of well-documented steps in order to build friendships. You need the right sized groups of people, in correct density environments, engaged in mutually dependent reciprocal activities. If these psychological requirements are not met, players will always fail to form relationships. This topic is covered in detail in the 2016 Project Horseshoe report “Game design patterns that facilitate strangers becoming ‘friends’”.
Logistics: Human beings have rigid limits on how many relationships they can maintain and how long it takes them to form new ones. When we try to put players together into groups online, there arise numerous logistical challenges in satisfying all the spatial, temporal and psychological constraints. This topic is covered in detail in 2018 Project Horseshoe report “Design Practices for Human Scale Online Games”.
Economics: Online games are built upon an economic foundation of resources: their creation, transformation, trade and consumption. Almost all social systems interact with these economic systems. However, common economic practices (and their underlying theory) often unintentionally incentivize asocial or antisocial behavior. In particular, many of the key elements required by the psychological and logistical aspects of friendship formation are systematically undervalued within common economic practices.
This paper focuses on the final category: economic aspects of prosocial game design. We’ll cover the following topics:
The fundamental need for economic systems when designing multiplayer games;
The challenges inherent in applying economic theory to prosocial systems;
An approach to using economics by first defining prosocial values and goals;
Prosocial economic design patterns;
Dark patterns of economic design that sabotage prosocial systems.
Part 1: Economics & Games
Game designs always have an economy
When folks who have taken a course in micro or macro-economics think of economic design in a multiplayer game, they immediately imagine things like supply and demand, auction houses or player-to-player trade. And these are indeed classic economic systems. However, game design uses the term ‘economics’ in the broadest sense of the flow and transformation of resources, value, and incentives for player behavior.
First let’s discuss how game designers treat their economies, and then we’ll get into what we can learn from the study of economics.
A game designer’s definition of game economics
Almost all game systems that manipulate player incentives, acquisition of resources, or use of those resources, can be examined with an economic lens.
The practical version tends to take the shape of something similar to Joris Dorman’s definition of an ‘internal economy’.
The internal economy
When we build a game, we create a cartoon world that players agree to mostly operate within. Nothing within the cartoon world is real but we can still build meaningful relationships between virtual objects that give players an interesting system to manipulate.
The economic operations involving the creation and manipulation of endogenous value in the cartoon world are known as the internal economy.
Boundary between the real world and the game world
There’s limited permeability of the boundary between the real world and the cartoon world. You can think of this as the designer writing out the import / export laws for their bubble of play. For example:
A designer might specify that you add virtual resources to the game by spending real world currency.
Or they can specify a time-based limit to the rate at which progress tokens can be earned.
Or players might go outside the official rules of how to play the game and share tips and spoilers that they did not earn directly inside the game. They might find ways of transacting in real world currency for in-game goods against the developer’s policy. There is always a black market.
Elements of the internal economy
We have a variety of economic elements within a game. Many of these come to us via computer science and systems theory, not directly from traditional economics. They were subsequently adopted by game developers looking to name the nuts and bolts of game development they’d been refining for decades.
Tokens: Within this world, there are tokens that act as goods, products, or currencies. A token can measure or quantify almost anything, including abstract concepts such as attention, time or value. Properties of tokens can themselves become tokens (it’s tokens all the way down).
Sources: There are operations within the world that produce new tokens, sometimes connected to resource sinks.
Pools: There are pools that store quantities and types of tokens.
Sinks: There are operations, usually connected to locations or objects, that take tokens out of circulation.
Transforms: Some operations transform tokens in number or type.
Then there agents who operate mechanisms made out of these elements.
Players: Human agents who have various permissions for triggering different transforms, sources and sinks.
Black box: Computer agents or systems which also trigger various transforms, sources and sinks. Often the player’s job is to understand the systems of cause and effect hidden inside the black box.
With this relatively simplistic set of building blocks, a designer can model most economic flows within a game. This includes complex or emergent phenomena like various feedback loops, ownership (just another property of a token), or trade.
Economics drive game balancing
In additional to being the structural foundation that all systems design in a game rests upon, economics also impact player behavior via incentive structures.
We can ask questions such as: why does a player want a particular weapon in the game?
And then we can instrument and trace the flow of resources in the game.
And formulate a theory that the weapon has a utilitarian value in helping the player accomplish some other goal, such as killing a key boss.
And decide if the boss killing isn’t happening as frequently as we want, and so choose to make the weapon drop slightly more frequently.
This flow exemplifies classic metrics-based game balancing. And it is nearly impossible to do efficiently without spreadsheets and graphs tracking all the relationships of elements within the internal economy.
Economic systems are everywhere in games
Once you start looking, you’ll see economic systems everywhere. Consider the following common game system through the lens of game economics:
Leveling systems: XP points are tokens that emerge from the source of killing enemies and are in turn transformed into various skill tokens once they accumulate to a level cap in the leveling pool.
Items: A weapon is a token that enables the player agent to perform various transforms on the pool of enemy health tokens in order to generate XP tokens they own.
Chat: The chat channel has a budget of attentional and time resources it consumes with each text message token. Players generate the tokens and transform them into knowledge or relationships. If the pool of attentional resources is consumed by too many messages flooding in, there’s no attention left to process the messages and the information is lost.
Part 2: Challenges of applying economics to social systems
The thought that first came to mind when investigating the topic of ‘prosocial economics’ is that we should see what real-world economics has said about related ideas. Sadly, traditional real-world economics does not map perfectly to game economics. There’s been some impressive work exploring the overlap, but both the methods and goals of the two disciplines can be quite different.
Limited resources: There are limited resources in the world;
Unlimited needs: Greedy humans have essentially unlimited needs for those resources.
This leads to economists spending most of their time trying to answer a few big questions:
What goods and services do you (as a society) produce?
How do we produce them efficiently from our limited resources?
How do you deliver them?
Who gets them?
Right off the bat we can see there are some critical constraints that aren’t shared by game economics:
No scarcity: We usually don’t have limited resources unless we have deliberately decided to limit them. We can create as many virtual goods as desired whenever we want. Player attention and cash are the few limited resources we care about.
Virtual goods: Our goods and services operate in a cartoon world. We can imagine them to be whatever we desire — even infinitely transforming — as long as they fulfill their role in the internal economy or satisfy the player’s psychological needs. The movement of goods is not an issue unless we want it to be one.
Digital ownership: Anyone can own a given item if we allow them to. Just because Bob has the Magic Sword of Smiting doesn’t mean you can’t have one as well.
Economics only recently has embraced psychology and computation
Economics is an ever-evolving discipline, but it has theoretical foundations that reach back to at least the 1700s. The influence of older ideas and models continues to this day. For cross-disciplinary spelunkers, there’s simply a lot of economics history that needs to be parsed through in order to distinguish a modern, validated idea versus an ideological fossil.
Poor integration with psychology
Psychology wasn’t a thing, so early economic models of human behavior are problematic. Here’re just a few head scratchers.
Homo economicus: The most common behavioral model assumes that humans are atomic individuals who operate rationally and selfishly. We know now that humans have limited attention, are contextually altruistic, are highly tribal, and exhibit a wide range of irrational cognitive biases.
Individuals are the best judge of their needs: We know from more modern research that much of relationship formation and short-term valuation is obscured in order to foster long-term, mutually beneficial support relationships. Our brains are not conscious of the base psychological processes driving some of our most pressing needs and are thus unable to value relationships rationally.
Weak social modeling: Most economics models ignore basic human behavior such as friendship networks, affiliation networks, limits on cognitive resources (ex: attention) or altruism. Highly nuanced and layered group behavior is bundled up into cartoon-like institutional entities (The State).
Interesting areas of investigation include behavioral economics, which is starting to grapple with a few of these issues using piecemeal experiments.
Recent adoption of computational models and data collection
Modern economics study (since the 90s) has increasingly used computers to test more complex models. However, these build incrementally on early work that was limited by the data collection and computing capabilities of the time. Where game developers essentially have a panopticon that records every possible player interaction within our cartoon worlds, economists are usually desperately making do with any data at all.
Reliance on proxies: Often economics studies can track poor fidelity aggregate data (macroeconomic values) or smaller quantities of shallow data divorced from context (pricing or purchasing logs). They are forced to create proxies of scavenged proxies when attempting to describe the real world.
Weak sampling: It is expensive to sample everything in the real world so data is lost. Individual actions are often lost and there’s no way to get detailed or complete historical trails of underlying data.
In games, metrics and processing complex models is relatively cheap. We may gain more from studying game theory (especially iterated computational simulations) and some microeconomics. It is not currently obvious what macroeconomics offers beyond general rules of thumb.
The practice of economics erases many of the social phenomena we are interested in examining
Economics embraces reductive utilitarianism and posits you can put a price on anything. Once you make this critical assumption, there are all sorts of wonderful things you can do with prices, buying, selling, etc. However, simply putting public prices on relationship interactions breaks them.
Transactional relationships: We tend to be intrinsically motivated to connect to others and invest long term in a relationship where no extrinsic value is ever publicly admitted. When a relationship transitions into being an extrinsically rewarded transactional relationship, the relationship often suffers a catastrophic loss of value.
Undervaluing long standing social networks. Human groups create public goods in the form of unmeasured relationships, social norms and cultural practices. These public goods are incredibly valuable in terms of individual health and happiness. Yet they are not readily measured by economic transactions. Economics in general struggles with public goods, and social public goods are even more invisible.
Over-emphasis of short-term measurable improvements: Efficient production using measured inputs and outputs is an easily optimizable value. But it may or may not be a long term benefit. Unmeasured factors (aka externalities) often dominate social systems long-term. Politics, weather, technological change. And when these drive catastrophic failures, the response is usually “Whoops, well, we never promised we were perfect.” And tragically, the same tools with the same underlying flaws are redeployed for the next round.
A historical vs an experimental focus
The practice of economics is as much historical mapmaking as it is a science. Economists are mostly poking at existing, highly complex socio-economic systems and attempting to accurately measure results. Interventions intended to bring about future results are as much guesswork as they are predictions of proven models.
Reliance on natural experiments: Many large scale macroeconomic theories are essentially untestable in laboratory conditions. Instead, there’s a tendency to look for ‘natural experiments’ and fit the models to the data. Most important natural economic situations occur rarely (ex: major recessions) and are burdened by confounding variables. To a degree, models based on natural experiments become an exercise in data mining, with incentives for overfitting and with limited opportunity to invalidate resulting models. Some journals encourage pre-registering of study results in order to reduce p-hacking and other poor research practices, but this is not yet widespread or mandatory.
Underpowered experiments: Many economics experiments involve small groups (often college students in a class). Meta-analysis show that upwards of 50% of empirical economic studies results are not reproducible. And upwards of 80% overstate their effect size by 2-4 times.
Scope of experiments: Certain emergent elements of economies (such as firms or tribes) tend to show up only in large populations that run for extended periods of time. Social systems in particular are heavily mediated by group size and the relationship formation process that drives social effects collesces over 1000s of hours of real human interaction. Experiments done in a 45-minute classes or 3-hour labs are often poor analogues for long-term social economies.
In the last couple decades, there’s been an increased reliance on randomized trials (what game developers would consider a version of AB testing) and increased focus on confronting economics’ replication crisis. However, bringing scientific rigor to economics still appears to be a work in progress.
All this makes it a challenge to pull clear models out of economic literature and apply them directly to our game designs. At best, some microeconomic theories seem to be generally true within given contexts. But like many design tools, these are subtle instruments to be wielded to craft a desired outcome.
The practice of economics has increasingly become intertwined with the politics of governmental policy. Politics is as much a world of using the right rhetoric and building the right alliances, as it is about doing quality science with reproducible lessons.
Your typical theoretical economist will wait years, if not decades, before witnessing any policy changes that actively test their theories. And often the economists who are most successful are those that invest in the political relationships and rhetoric that makes their work palatable. This dynamic drives insidious corruption.
Scientist rhetoric: With the dramatic success of physics in unlocking the atom, economics was seen as a comparatively unreliable practice full of quacks, poor predictions and fake math. In response, modern economists cloaked themselves in complex, yet non-verifiable mathematical models and public claims of being truth speakers. “The math says X” acted as a powerful appeal to logic and credibility…whether or not the math was ever right.
Ideology: If economics is a design practice, what is the desired outcome being designed? More political creatures long ago realized that economics is a tool that can push a society towards a given set of social values. Various conservative and liberal political agendas regularly put forth economic policies intended to drive quite disparate futures.
Faux-economists: Much of the popular discussion of economics is delivered by political talking heads who have very little academic understanding of its limitations. They use scientist rhetoric and ideological propaganda techniques to push their agenda to the public. This problem is further exacerbated when political entities fund economic research.
The political influence alone makes it incredibly difficult for those outside of economics to distinguish if shared lessons are reliable insights or heavily biased propaganda. The latter has deep, deep roots that are often invisible and unquestioned to the more devoted practitioners of any given affinity group.
The conundrum of prosocial economics
So we are left with two conflicting thoughts after all our investigation
Economic design is required: We must carefully design, build and balance economic systems when creating multiplayer games. To paraphrase Douglas Martin, the alternative to good economic design is always bad economic design, not no design at all.
Economic tools are poor: The more academic study of economics gives us fewer reliable insights than might be hoped for. There’s a slight overlap between the crafting of economics of the real world and the crafting of economics of virtual worlds. But what insights might exist are heavily obscured by poor modeling of human behavior, weak experimental tools and a two-century deep cesspool of political propaganda.
We are not equipped to immediately solve this conundrum. There is clearly a vast project, far outside the scope of this paper, where those educated in the field of economics and game design dig through the dismal wastes of economic theory. Perhaps there are intellectual treasures to be found. For those future seekers, we’ve tried to document many of our unanswered questions in the Conclusion.
For the rest of this paper, we track back to our domain of expertise, game design. As game designers we can apply a prosocial aesthetic framework that helps us use economics ideas (if not the full economics discipline) to drive designed experiential results.
Part 3: Reframing economics as a tool for expressing a system of prosocial value
What if, instead of trying to treat economics as a science, we use it as a set of tools that support a design practice?
Game design aesthetics
A critical goal of game design is to create an aesthetic experience for the player. There’s some set of values the team is aiming towards creating.
Explicit values: Teams regularly write down their project pillar or an “X statement”. For example a team might decide to build a go-kart racing game that parents can play with their young children, with the hope that this will form warm family memories that will last a lifetime. There’s an audience, a desired outcome, and a set of values that the team works towards.
Implicit values: Games also are designed according to unstated values. For example, a 4X strategy developer might recreate the genocidal values of colonialism. When asked if this was their intent, they claim to have not thought about the issue at all; they were merely mimicking the practices of the established genre. However, unexamined ignorance of inherited values still results in a game with those values. Ignorance does not create ideologically neutral games.
Building, with intentionality, towards an aesthetic destination
With this design perspective, we have no need for rhetoric of manifest destiny or inevitability of scientist math. Instead, we are humbly upfront that designers will do the following:
Be explicit about their values.
Make design choices to the best of their ability…
…In order to create a player experience…
…That supports their selected set of moral values.
Now, the process is not entirely arbitrary. Since we are dealing with real humans containing real temporal, spatial, psychological and material constraints, this is an engineering exercise. We are craftspeople doing hard, practical labor. Game design can never be a purely theoretical fantasy or exercise in hopeful hermetic elegance.
With clear explicit values, it is possible to measure the result on our players and judge the success of our work. The machine that we build for our players either achieves our aesthetic goals or it does not. And we can then tweak and tune our system rules accordingly so that future iterations might hone more closely to our ideals.
Now let’s come back around to economics. What if we treat economics as a design practice instead of a science? One that is also seeking to create an aesthetic outcome? To express a set of designer-selected values?
In popular culture, this perspective on economics is uncommon. It is more likely to hear claims that there is One True Way of building an economy. Much of this is your basic rhetorical polarization, where if no one seems to be listening, you shout your opinion more loudly and with less nuance. Some of it may purely be a result of the relative youth of economics as a science. For now, however, let’s put the One True Way on the backburner.
Let us entertain the thought that there might be many valid economy designs, each of which deliver a particular set of aesthetic values. Our goal as economic designers is to craft the systems that drive our selected set of values.
Again, just as in game design, this craftsperson framing does not mean we are allowed to dream up any old fantasy. There are truths and common emergent dynamics in economic systems. Trade creates value! It also destroys it by missing key externalities. Supply and demand generally work! For certain types of goods and certain types of markets. There are selfish agents within any sort of exchange economy, as well as altruistic ones.
What sort of world do we wish to build and how does the economy we design serve those values?
We invite you to adopt an explicit set of prosocial values when you build your games and their supporting economies. These values include both experiences we want to build towards and outcomes we want to avoid.
Prosocial play involves players behaving in a manner that benefits the community as a whole. It is composed of many designed systems that facilitate the following:
Friendship: The formation and maintenance of healthy, meaningful friendship networks between players.
Thriving individuals: The facilitation of individuals’ eudaimonic happiness. Individuals feel competence, volition, and relatedness, both for themselves and for their friends.
Altruism: The promotion of activities that involve intrinsically motivated altruism and cooperation.
Positive group norms: The spread and enforcement of shared altruistic social norms within and across groups.
Shared goals: The definition and adoption of shared group goals. Players work towards those goals via mutual interdependence, and achieve feelings of purpose and meaningfulness.
There are also values we wish to avoid generating with our social systems.
Individual toxicity: Individual toxicity is when poorly socialized individuals resort to antisocial behaviors in order to meet their internal psychological needs. These are expressed as griefing, bullying, sociopathy, narcissistic abuse and other ego-centric patterns that put the individual above the group. These behaviors often provide short term benefits to the egotist and poor outcomes for everyone else in the community.
Group toxicity: Group toxicity is when intergroup friction (resource competition, enforcement of social norms, competing group identities) results in unhealthy interactions. While competition between groups can motivate group performance, it also can breed damaging behaviors such as organized aggression, hate crimes or racism. There’s a growing movement in games (centered around community management) dedicated to fighting toxicity of all types in games.
Loneliness: Loneliness is what a person feels when they want to be with other people, but cannot. This may be due to a sparse relationship network, or logistical challenges that prevent them from connecting. Loneliness is one of the key emotions we feel when our social support network fails. Like pain telling us the stove is hot and we should pull our hand away, loneliness tells us that our current social situation is untenable long term and we should seek out connection with others.
Benefits of prosocial design aesthetics
There are of course many potential values a designer might select. So why would we pick these specific prosocial values?
Support from psychology: There’s a growing body of research that suggests these values result in happy individuals and groups. This research (like all incomplete science) will no doubt grow and change over time, but it is the strongest experimentally verified foundation we’ve found for improving the lives of our players.
Ethics: Responsible social systems design requires an ethical core. Much like medical doctors, we are operating on human beings. Huge populations of humans, in fact. As ethical practitioners, we need our own equivalent of the Hippocratic oath. We should do no harm, and if possible, improve the social health of our players. The prosocial values listed are an attempt at creating a code of conduct that is a minimum ethical bar.
Part 4: Prosocial economic design patterns
The following is an incomplete set of economic design patterns. Like all design patterns, they provide the canny designer with early tools for supporting prosocial values in their game using economic systems. Be careful; patterns do not guarantee results. They are instruments to be wielded with skill, precision and craft. If you want to get the result you desire, each of these patterns benefits from a lifetime of intentional practice.
This set of patterns is by no means complete. There’s immense work to be done exploring and extending these ideas through hands-on practice and iteration with live game populations. But it helps to begin somewhere.
Pattern: Friendship formula
To start with, we need a richer psychological foundation to build upon than economists’ rational optimizer. A useful model for social systems design is the distinct process by which human relationships form. This contains elements of contextual reciprocity that are found in across multiple field: Social psychology, newer flavors of both economic game theory, economic altruism models (see Appendix) and behavioral economics. We can use the friendship formula as a key tool to design prosocial values into a game.
Key friendship factors
Though every friendship has a unique history, they all require several key factors
Proximity: People must be able to interact with one another.
Repeat encounters: The same people need to identify and interact with one another repeatedly. For example, matchmaking systems that match different strangers together are weak social systems because there will never be enough repeat encounters to facilitate true friendship formation.
Reciprocity: People exchange resources with one another in the form of attention, conversation, gifts and support. One party makes an overture and the other party responds. This is an opt-in process at every stage. Successful reciprocity loops tend to increase in value and effort over time.
Disclosure: As the relationship grows, people will disclose intimate information to one another. This helps build trust and fine tune mutually negotiated social norms.
The accumulation of trust
As the reciprocation process continues, the participants gain trust in one another. The higher the trust, the greater the strength of the relationship. Ultimately highly trusted friends form key long-term support networks in times of need. It is a blind investment for most people; with each short-term interaction, they don’t fully know why they are investing. Yet long-term the strong support network predicts improve health and a longer, more satisfying life.
Accelerator of friendship formation
There are additional factors that help friendships grow more quickly.
Similarity: If two people feel they are similar to one another, they are more likely to initiate the friendship process.
Intensity: If two people are in an intensely emotional situation due to high risk, large amounts of resources in play or time pressure, they are more likely to become friends.
Autonomy support: Self-determination theory suggest that people who support one another’s autonomy needs (the need to feel like you are choosing your path in life) are more likely to invest further in the friendship.
Pattern: Measuring trust
A key metric of relationship strength is trust between two individuals. As trust in a community increases, support networks flourish. However, trust is typically considered an externality, a factor poorly measured or valued by economic systems. So it gets optimized out in the name of efficiency. By measuring trust, we help ensure that it is treated as a first class citizen alongside more material concerns.
What to measure
Trust is an internal factor that cannot be measured directly so instead we need to rely on proxies. These won’t be 100% reliable (and represent a major area for further discovery and research), but are a starting point.
First, measure pairwise bonds: We’ll start by looking at player dyads. This is the most fundamental network connection and more complex network topologies can be derived from this data. There are two different (asymmetric) bonds for any given pair of player; the bond player A to player B and the bond from player B to player A. For simplicity, many proxies collapse this into a single symmetric bond.
Active time spent together: One of the easiest symmetric bond proxies is simply tracking if players are in the same area together. Due to players being idle, it is usually wise to track time only if they are actively playing. This metric does not track the intensity of the trust, but merely the duration. Higher values of time spent together means your dyad fulfills the initial requirements of the friendship formula (repeat, serendipitous encounters).
Success together in high trust situations: If your game has cooperative or team activities, you can track if various dyads are successful and how often.
Time spent talking positively: In games with text chat, we can go one step further and track number of times that a player talks to another player using positive language. Tracking simple words lists with positive affect are a good baseline and there is more sophisticated language analysis available if necessary. There are versions of this technique that can be applied to vocal analysis as well to determine positive or negative tone. The nice thing about this metric is that it tracks asymmetric relationship bonds, where one person talks a lot or uses relatively more positive language than another. Time spent talking, especially on private channels is a sign of increased intimacy and is almost always correlates with high trust.
Analysis and tuning
Once you have these metrics, the development team uses them to tune the system. This is for the most part standard data analysis; you create baselines and then track to see if any of your design changes are impacting the baselines positively. Note that these metrics are not usually player facing for the reasons listed below in Challenges.
Total trust over time for each dyad. Trust should accumulate slowly over hundreds or thousands of hours. Total trust is a reasonable proxy for overall strength of the pair’s relationship.
Rate of trust accumulation. Large gaps in the accumulation of trust suggest a pause in the relationship. Humans have a limited budget of active relationships so it is very natural for someone to stop reciprocating with another player as the relationship fades. Gaps let us track the length of the relationship. As well as if the relationship is rekindled at some later point.
Create trust metrics for higher order groups: Total trust can be aggregated from dyads and tracked for an entire guild. Or a cohort that comes in from a particular onboarding source. You can often correlate these with other variables like guild churn.
In Steambirds Alliance, a cooperative MMO, we measure trust by a ‘togetherness’ factor. When a player kills an enemy, all nearby players also get XP for the kill (a positive sum resource as in Pattern: Positive Sum Resources below.) This event is tracked and stored on each player as a list of other players nearby that also got XP. We do initial tracking on the client and then send periodic lists to our metrics server. We post-process this data to generate various graphs.
So how would we categorize the strengths and limitations of this metric?
Symmetric bond: Due to how XP is given we don’t know if one players has higher trust than the other. So this metric is limited to assuming that both players like one another the same amount.
Mixed trust: We’ve got a weak tracking of “success together in high trust situations.” There’s some nuance. Players who are getting XP from fighting smaller enemies need lower coordination so trust doesn’t need to be as high. But players who are getting XP from higher coordination boss battles are likely in a higher trust relationship. In the future we could split those two metrics and test if the increase in fidelity helps us better track useful behavior.
Actively playing: We assume that when players get XP, they are actively playing (since inactive players don’t get XP) and they are working with others to blow up their mutual NPC enemies.
No accounting for freeloaders: We don’t account for free-loaders or bots because we haven’t observed them being a meaningful population of players.
A mistake we made early on was looking primarily at metrics like retention and monetization. These simply don’t tell us much about what motivates players to play. If I were to build the game again, I would have implemented the togetherness metric for the very first private alpha. Player relationships are top-level intrinsic motivators and by only measuring them late in the process, we completely misunderstood the state of the game we were building.
Challenge: Avoid sharing pairwise trust metrics
Imagine if your friends all had a trust score hanging over their head. And when you do some small things, you witness the number change. Your relationships would suddenly become transactional in nature with clear extrinsic motivators in the form of your willingness to make that number go up or down. And we know transaction relationships and extrinsic motivators reduce trust. Are your interacting with your friends because you like them and they like you? Or are you trying to make a stupid number move?
So never share detailed trust metrics with your players. Trust, like many social variables, if revealed to the observed subject as an operationalized metric irrevocably changes the subject’s behavior. You’ll ruin the validity of your metrics and likely degrade the relationships between your players. (This is also one of the reasons why ‘likes’ in social media end up being a source of toxicity and in general a very poor practice.)
Challenge: Sharing group health
You can, if needed, share some high order group health information. The best practice here is to keep it vague, heavily delayed and multi-dimensional so that the underlying metrics cannot be easily gamed. A common use for group health information is to drive positive behavior by directing players towards a few key activities that developers know will improve overall social capital. Think of directives that are broad like the Ten Commandments so that players maintain agency and localized judgement. Avoid suggesting highly specific (and thus identifiable and gameable) activities.
Challenge: Trust differs across social contexts
Individual trust exists on top of a bedrock of group norms. For example, a pickup basketball team is a high coordination, moderate trust group. Players know that within the context of the basketball court they can trust one another to play according to the social norms (the rules) of pickup basketball. If you only sampled this social context, you might imagine that everyone playing is in a deep relationship with one another.
Yet, this relationship is contextual. Outside the basketball court, two players might never talk. When you create your proxies for trust and social capital, it is worth taking into account context. The more rigid and proscribed the rules of group coordination, the less actual trust is required for players to work together. And your metrics of trust may not travel to other portions of your game.
A way around this is to track multiple trust metrics in multiple contexts. High trust dyads in multiple context should be treated as having stronger relationships than those with high trust metrics in only a single context. Note that one of the more interesting to measure contexts is family bonds or mate bonds. These are often have large impacts on behavior but are rarely perfectly visible from inside the game.
Pattern: Positive sum resources
Positive sum (also called non-zero sum) resources are a key economic tool for ensuring cooperative play.
Zero sum resources
Material resources in the real-world are zero sum resources. If I own a piece of pie, there is one less piece of pie for you to own. If I consume that piece of pie, it is lost to you forever. This probably makes you irritable due to loss aversion. An economy of zero sum resources is a world of scarcity. The challenge economics attempts to solve is how we might split up these limited resources in an efficient fashion. Inevitably this involves some form of competition either via trade, negotiation or warfare. All of these tend to reward (at least in the short-term) selfish strategies and their resulting social toxicity.
Positive sum resources
However, in digital worlds, resources are mere bits. Making more of a resource is free. If we found a positive sum digital pie, you could have a slice and I could have a slice and the pie would be undiminished. My getting a piece does not prevent you from getting a piece. There is no need for competition between two parties over a scarce resource. This area of exploration is connected to the software theory of agalmics: non-scarce resources.
There are a few natural positive sum resources, and correspondingly game systems based on non-scarce resources are — scarce. Time, for example, is something that everyone experiences equally and simultaneously (it is also not transferable). Information is usually positive sum. If I read a book, you can too.
With code, we can make almost any resource positive sum. When a monster drops loot for one player, it can also drop loot for any other player that did damage. Whether or not players compete over a resources becomes a design choice, not a fundamental constraint.
When doing prosocial designs, positive sum resources are one of the first tools you should reach for.
Watch people play and look for moments of competition or toxicity.
Are there zero-sum resources at the heart of those interactions?
Can you turn those into positive sum resources in a way that players are no longer incentivized to be toxic?
Challenge: Building games around positive sum resources
If you are new to game design, you might imagine that games require zero-sum competition or at least a sense of winning to be enjoyable. Luckily there are many classes of gameplay that work with positive sum resources
Coordination and cooperation activities: Players with differentiated and limited capabilities can work together to accomplish goals larger than themselves.
Races: You can still have competitive challenges where players try to do their best within some limited amount of time or a fixed number of moves. Most cumulative scores are an inherently positive sum resource where anyone can earn points independent of other players.
In general, almost any Player vs Environment (PvE) game is amenable to being redesigned using positive sum resources.
Challenge: Infinite sources and imbalanced economies
If everyone gets resources, how can we prevent our sources from generating too many resources and flooding the world with abundance? We often rely on scarcity to creating prestige tokens or tune the pacing of gameplay.
Per player caps: Capping the number of harvestable positive sum resources per player restricts the flow into the world. A tree might be harvestable for apples, but any single player can only harvest the tree once.
Per group caps: We can also cap total number of harvestable items per group. This helps prevent intergroup competition by ensuring each group has equitable resources.
Transaction costs: Even with caps, the total number of items increases for each additional resource receiving entity (individual or group) in the world. With cheap trade transactions between entities, it is possible to pool vast numbers of resources. A thousand players means a thousand times the items. However, you can prevent global pooling with large transaction taxes. Transport might be expensive or you could charge a hard currency as a trade fee. This ensures that players are encouraged to use the resources locally vs globally.
Appropriate sinks: Any flow of resources, no matter how large can be balanced with large enough sinks. Measure the rate that positive sum resources are coming into the world. In real world economics this can be a tricky thing to figure out. In a virtual world it is a simple metrics check. Then tune sinks such that an equivalent is sucked out. For example, you might find that 20,000 new Magic Coconuts are flooding into the economy each day. A decay rate of 24 hours ensures that this (newly) highly perishable good never accumulates more than 20,000 units.
It is important to internalize that as a game economy designer, you control the sources, the sinks and the narrative justification for why the world works as it does. Scarcity as well as abundance are aesthetic choices.
Challenge: A human’s total relationship budget is a zero-sum resource
We might imagine that relationships are also positive sum resources. Me being friends with you shouldn’t have any impact on whether or not I can be friends with someone else. The reality is complicated.
In a highly local context, when you consider a few people at a time, forming a new relationship creates a positive sum public good. This is shared between the people in the relationship and essentially creates value in the form of social support and improved coordination. It is often beneficial to make overtures to weakly connected players you encounter on a regular basis.
However if you zoom out and consider the entire social network of an individual, they have limited social resources to spare. The social psychology concept of Dunbar’s Layers suggests that humans have a relatively strict budget on both the total number of relationships and the number of high strength relationships their brain can manage.
For someone with a full set of friends, investing in relationships in one layer pulls resources from from other layers.
Like many social resources, this is a difficult-to-acknowledge trade off. By explicitly acting upon that ideas that total social energy is zero-sum, especially in localized small group settings, the relationship become codified and transactional in nature. And thus suffers a drop in trust.
Pattern: Knowledge Resources
Nobel Prize winner Paul Romer has looked at a specific form of positive sum resource known as a knowledge resource. By taking a particular set of scarce zero-sum resources and performing learned transformations on them, we can derive vastly more utility than if we had just used them naively. For example, wood might be burned in an open fire pit to create the desired resource ‘heat’. However, if someone knows how to build a brick stove, we can burn the wood hotter, store heat in the stone and ultimately gain more heat from less wood. From this perspective, knowledge is positive sum resources that help dramatically increase the efficiency of using scarce goods.
A wonderful prosocial attribute of knowledge goods is that supply is determined by the number of clever people you have creating them. Since knowledge is research by clever people, the more clever people we have playing, the more knowledge we’ll likely gain.
This is the opposite of most zero-sum scenarios, where having more people around drives increased competition for scarce goods. With appropriate design of your knowledge good economy you can make it so instead more smart players are an advantage, not a threat.
Some examples of knowledge goods
Player skill: When players learn actual skills such as learning to execute a tricky fighting combo or defeat a complicated boss, they’ve acquired a knowledge good. In terms of interaction loops, this involves combining base level interaction (like jump in Super Mario Brothers) into compound interactions (such a using a double jump to get past a tricky section). Player skills, like most knowledge goods are teachable and create an economy of attention and narrative around passing skills efficiently onto other learners. Streamers traffic, in part, by showcasing their knowledge goods (they also foster parasocial relationships and acts as reference relationships, but that’s a whole other set of topics)
Technological process: Conceptually similar to player skills, technological processes involve combine existing resources and tools using the right order, amount and methods in order to create more useful resources and tools.
Explorable spaces: If you have maps full of unknown areas or hidden secrets, the act of exploring the space yields knowledge. You can share this information with trusted allies.
Spies and espionage: Games like Eve set up competitive games, where knowing where and when an attack or resource transfer occurs makes the difference between success or failure. This adds a layer of trust and betrayal where sharing knowledge goods with the wrong people may result in your downfall.
Challenge: What about virtual knowledge?
Video games have a long history of creating tokens that represent real knowledge. Instead of actually training to gain the skill of fighting with a sword, instead players are awarded with a virtual token (or virtual skill in Jesse Schell’s terminology) that says they can now fight with a sword. Or at least perform the themed in-game action that looks like sword fighting.
Virtual knowledge is a straightforward game resource that we can choose to make positive sum or not. Since it is just a token, our systems can trivially pass it around or give it to various players. As unlocks, items or whatever.
To make virtual knowledge more social, you need to build in some form of transfer mechanism between players. Real knowledge goods have an implicit transfer mechanism in the form of conversation, but that doesn’t work for virtual knowledge. In MUD of yore, the only way to learn a game skill was to approach a skilled player and ask them to ‘trained’ you. Usually for a fee or time. There were fun variations where an advanced player can only advance further if they manage to teach a newer player one of these virtual skills. These creates a tit-for-tat reciprocation loop where both players are getting something they need.
In general, when setting up transfer mechanisms, such as the one here with virtual skills, try to create a natural interdependency between players. Economic mechanisms that encourage players to seek out and interact with other players helps facilitate the friendship equation.
Pattern: Voting Resources
A specific form of positive sum resource is a vote. Each voter has an explicit ownership of their vote and there are usually rules to prevent vote selling. If more voters appear, using the magic of positive sum resources, they also get a vote.
Votes are then transformed via a decision mechanism (aka voting) that determines whether or not some course of action is taken. Voting is a social system for managing politics.
The important thing to note here is that we typically don’t think of votes as economic resources. They are often talked about as part of the domain of political science and most literature covers a handful of relatively conservative systems (plurality, ranked voting, etc). But once we reframe them in economic terms, we gain a large number of tools for manipulating and building novel prosocial voting economies.
In the multiplayer VR game Beartopia, players could build various communal projects for their shared virtual village. However, there was a limited amount of public space and it was undesirable let anyone simply build what they wanted without buy-in from other players.
So we designed an obfuscated voting system themed as crafting.
If a player did a minimum amount of work, they could harvest berries from a bush. Berries were a crafting ingredient used in various communal projects.
Secretly behind the scenes, the crafting ingredients were considered to be positive sum voting resources. They were instanced per player. They were untradeable. They had a cap on how many could be accumulated.
When a communal building project came up, players needed to pool their crafting resources to complete it. The was the equivalent of needing to vote for the project.
The cost for large projects was determined by the number of players in the world. It was tuned so that it would require a substantial amount of participation by players the world to create long-lived objects that consumed public space.
We also had short-term public objects that almost any individual could create. They might take a day of individual harvesting labor but also expire in 8 hours. Since these were not a lasting consumption of the public good (communal space), we could set the crafting thresholds lower. Thus allowing more individual control on short time spans. We wanted a system where individuals were empowered to make local changes, but you needed the political power of increasingly larger groups to make more permanent communal changes.
By putting expiration dates on most public projects, we ensured that with a lack of ongoing public attention, public goods would revert back to the public domain. This helped create persistent long term shared goals for players simply seeking to maintain the status quo.
Pattern: Interdependency of player roles
One of the early lessons of the industrial revolution was that division of labor allowed workers to vastly increase their productivity at multi-step tasks. Groups of specialized workers working together were more productive than an equally sized group of generalists.
This pattern for organizing human resources has three interesting attributes that make it pervasive throughout social systems design.
Increased efficiency: Functional interdependence is relied on economic incentives; the specialized players are more effective or efficient a certain types of resource acquisition or transformation. In other words there are natural incentives readily obvious to the individual player that they should specialize in order to maximize their personal playtime.
Coordination required: Interdependency leverages some form of process or skill (a knowledge good!) to coordinate all the players together. To gain the benefits of specialization players need to interact with others. And teach one another.
Trust-based: Finally, it is a social system fueled by trust. Each member of the group needs to have trust that other members of the group will perform their roles according to the plan. There are downsides for the individual members if the group falls apart. Specialization has a substantial opportunity cost; such players are unlikely to be effective generalists. In the real world, if the societal coordination systems that let specialists (most game designers, apart from Jason Rohrer) work together failed, we would all starve.
Challenge: High performance, specialized group activities scares new players
Because there are strong penalties associated with the failure of high coordination activities, it takes a huge amount of group trust to pull off the most efficient (and complex) activities. One of the biggest fears of new players is that they’ll be required to engage in specialized, high coordination group performances. If they fail, their reputation with this new group of people is tarnished forever. Leading with high risk, high coordination activities generally will send folks running from your game.
So designers need to build a ladder of activities in their game, starting with low trust activities between generalists and moving towards higher trust activities between specialists. The following illustration from the paper The Trust Spectrum shows the basic progression.
One response from systems designers is to build systems that allow coordination between specialized players at lower levels of trust. The thought goes, “Since trust is rare and hard to acquire, perhaps we could get efficiency out of our specialized groups in more mechanistic and scalable ways”
In the real-world, we’ve seen this in a practice known as deskilling, where highly trained skills are turned into a series of rote actions that are simple to perform and teach. These deskilled actions are coordinated, not by trust, but by an algorithmic (often computerized) system. A very early version of this was the assembly line. These systems scale to larger groups and can make use of broader labor pools. If you only care about the output of the system, they can be quite attractive.
However, if you care about the experience of the players, there are a couple questionable things happening here.
Deskilling removes the opportunity cost of specialization. There is no investment in a given role. Role switching cheap.
There’s less trust required to coordinate. The simplicity of the actions plus the role of the computerized coordinator means that groups practicing together see less overall efficiency gain.
Each worker becomes a low-trust cog that is easily replaced if they do not do their specialized role.
Deskilling systems are typical low trust systems that are helpful to new players. However, they are unable to facilitate the formation of high trust relationships.
Pattern: Shared Vulnerability
We know from psychology studies that shared pain acts as “social glue”: experiences of shared struggle create tight bonds of trust that yield greater social cohesion and measurably improved cooperation.
This can be harnessed in games through structuring experiences whereby players experience shared struggle early in the formation of a group. In many online games, players have discovered this organically and include it in their guild rituals.
Example: Guild Onboarding in Eve Online
When high-retention fleets were studied in Eve Online, a pattern emerged in the fleet manuals (often exceeding 80 pages) created by these high-functioning organizations.
One particularly high functioning fleet created a formula that was to be followed exactly:
Recruiter finds new recruits in a neutral zone
Recruiter gives everyone the same ship
Recruiter gives each new recruit a role that they understand. The intention is to make sure the recruit feels useful to the larger whole.
Specific behavioral instructions are given, for example: “A. Find this target, shoot it.” Then “B. Shoot targets of type N that approach.”
Some organizations specify redundancy in roles, so that there is no identifiable single point of failure in the mission.
Then the newly formed group is taken into combat with the specific intention that they will all die together.
After this happens, the recruiter explains that it was okay, and that it was a bonding exercise.
The recruiter also replaces all lost materials. The intention is to demonstrate support in a time of need.
This playbook of an experience creates high retention in groups through this mechanism of a shared memory and an establishment of interdependence, loyalty, and generosity.
Challenge: Formalizing Trauma
The risk of relying too heavily on this is that it creates downstream undercurrents that influence a game’s overall culture by grounding the bonding event in shared trauma. If these traumas are significant enough, they can convey lasting damage onto the social relationships of the group members. We can assume that most in-game traumas are far less significant than real world traumas, but these experiences fall into an unstudied place and it can become hard to determine how much pain is too much.
It seems possible to assume that the infliction of fear and experience of loss will have some repercussions on the group’s future dynamics. Further, the coping mechanisms that develop under crisis may not transfer to more peaceful contexts. Therefore the shared trauma of an early experience may have to be continued thematically through the game (a game about war continues being about war) which then sets a dynamic across the experience that is hard to disrupt. The challenge then is to carefully calibrate the kind of shared vulnerability — which is likely a very wide design space — and manage a thoughtful transition to more peaceful forms of gameplay that amount to recovery therapy.
Challenge: Solidifying Out-Group Hostility
Because these mechanisms for bonding are explicitly successful within guild contexts, which are tribal contexts, it is not clear whether the benefits persist or are possible without a kind of enemy tribe. For combat-based games, this is highly effective, but it isn’t as clear how it would translate to a non-zero-sum shared massive environment. Out-group/inter-tribal hostility is a powerful design mechanism in and of itself that is of questionable prosociality — being in a kind of “adrenaline” category of design mechanism that results in very high levels of comfort within established groups and higher isolation and discomfort outside of or between groups.
Pattern: Player-to-player Trade
Trade increases overall value by allowing exchange between players who own differentiated goods. By giving up something a player doesn’t need for something that players does need, both players in a transaction come out ahead. There’s a lot to say on this topic; many mistake this topic for the totality of economics. For a very brief overview, see the appendix on Trade.
From a prosocial perspective, the question we are interested in is “How does trade improve human relationships?”
On a macro level, by creating abundance through trade, we theoretically can escape a world where poverty-level survival dominates our daily lives. Excess resources could then be invested in our relationships. The not dying from famine, war, disease, ignorance and other outcomes of extreme scarcity is nice as well.
On a micro level, trade results in the reciprocal negotiation between at least two traders. As a result of this negotiation, people inevitably disclose their values with one another. And perhaps start to form a relationship. This style of trade is most common with ‘less efficient’, person-to-person barter systems. Though claims of efficiency depend on large part on what is being measured.
Challenge: Auctions dehumanize buyers and sellers
One of the great inventions of modern capitalism is the ability to boil down all of a person’s values into a single price on a commoditized good. A buyer can decide if they are willing to pay the price and the seller (by listing the price) automatically agrees to the subsequent sale.
Auction houses turn both the buyer and seller into low-trust, mechanistic entities. They can engage with a regularly updated listing of goods, quantities and prices and ignore the human on the other side. Humanity, in the form of face-to-face reciprocal interactions between people with names, histories, desires and culture, has been meticulously eliminated from the process. Too inefficient.
This results in immense improvements in material market efficiency. Selfish players clamor for such features in any game that includes trade. But it is worth asking if it drives the prosocial results that we desire.
Before you add a global auction house, consider the following ideas:
Games don’t require material efficiency. We have enough control over our economies that we can generate abundance on demand if required. Consider, players regularly ask for infinite power or inventory and we don’t give them those things. Because we know scarcity is a design element that drives our experiential outcomes. Similarly, there’s no law of nature stating we must climb an inevitable ladder towards ever greater efficiency in order to satisfy infinitely selfish actors.
Build barter or gifting-based trade system for friends: Barter systems are disliked because they put pressure on our limited trust and relationship budget. Negotiating prices with complete strangers can be time intensive and exhausting. And it takes away from time and effort spent with our core friends. However bartering or gifting with friends can be a very enjoyable social activity. What if you can easily and cheaply trade with players who are within your stable friend circle? A more free-form version of the same idea is to allow theft and betrayal so that trade tends to happen within high-trust circles.
Trade can be a specialized role that facilitates weak ties: Not everyone needs to trade with everyone to create large-scale, yet socially viable networks. Creating specialists trading roles that serve 150 to 500 people generates trade hubs that also serve as social hubs. These facilitate weak ties between denser, smaller friendship networks. Manage the density of trade hubs by culling those that dilute the 150 to 500 person sweet spot.
Pattern: Tying social metrics to business success
One of the great challenges of social design is that many business owners feel that it is an expensive extra. Should game designers play political games and show how social design drives business outcomes?
Find correlations with key business drivers
Split your population into higher trust and lower trust segments
Look for correlationships between trust and key business drivers like retention and monetization.
In general, you’ll likely find a very strong relationship. Intrinsic motivations, like social relatedness, are typically about 3X as strong a motivator than many of the extrinsic motivators found in more single player activities.
Use this correlations to justify additional investment in prosocial game design.
There are immense pitfalls that come from following this pattern. Profit motivated capitalism tends to be incredibly damaging to social systems design. See Dark Patterns below for examples.
Part 5: Dark patterns of economic design that sabotage prosocial play
Prosocial economics explicitly brings the tools of economics into social system design. And it promises to be an effective and scalable means of promoting societal values. This combination is a honeypot for bad actors. There is a future where the basic social technologies we’ve described in this paper will be used to create systems of immense evil, debasing the very aspects of friendship that we seek to elevate.
We’ve already seen some of these negative outcomes.
Facebook coopting social networks in order to sell unfiltered political advertising to the highest bidder.
China creating systems of social credit to survey and control those citizens who step out of a narrow range of acceptable behavior.
It is easy to imagine ideologically motivated governments, political parties and religious groups who co-opt the functionality of games to inject toxic tribal behaviors into the broader world.
Yet treating social systems design as a trade secret is also problematic. Again, the “alternative to good design is bad design.” To do good design, we need to grow a broad population of educated practitioners who are informed about both the craft and its negative outcomes. So that when things start going off the rails, we can identity and censure those who engage in dark social design patterns.
It is in the light of describing and enforcing ethical standards that we cover some of the darker patterns of prosocial economics.
Dark Pattern: Optimizing the system to improve proxy metrics instead of overall prosocial values
When a complex social phenomena (such as trust) is measured with proxy metrics, it obfuscates much of its expensive-to-measure nuance. This is exacerbated by the tendency to select proxy metrics because they are easy to measure, not because they are high quality proxies.
Subsequently, it is common for optimizers and balancers to start to mistake the proxy metric for the original phenomena. And as they make the proxy go up, they end up inadvertently damaging the hidden nuance of the original phenomena. Sadly, that nuance often turns out to be the real value we were trying to preserve and grow.
There are many examples of this:
GDP: In the real-world, you see governments optimize top-line GDP (gross domestic product). Over time, this proxy for economic growth and citizen well-being has become less correlated with these larger, more complex values. We see lower income segments suffering as small wealthy minorities accumulate the majority of newly generated wealth.
Togetherness in Steambirds: In our earlier example involving Steambirds Alliance, the togetherness variable was an easy-to-implement proxy for trust. As implemented, it depends on us checking if players are ‘near’ to another player when an enemy is killed. If we wanted to boost our togetherness values, we could simply increase the radius we check for ‘near’ players. With a large enough radius, the togetherness metric would hit 100%. However, despite the number going up, we’ve lost all insight into player ‘trust’.
Viral installs on Facebook: During the era of social network games, analytics teams optimized for increasing the virality of their games. ‘Virality’ was really a proxy for the complex social phenomena where a friend tells a friend about something they like and this trusted recommendation results in a highly engaged new player. In order to improve ‘virality’, social networks games began spamming friend lists with automated invites to games, often with minimal permission from the original player. These invites failed to trigger almost any of the important trust and reciprocation loops in the original phenomena. Instead, the spam damaged existing relationships and brought in low retention, unengaged new players. Pretty much all the games that seeded their audience with this distinctly low trust technique eventually failed.
Cross functional teams: Bringing multiple perspectives into the decision making process ensure that a single perspective does not dominate.
Holistic metrics reviews: Always return to the original prosocial pillars of the project and ask if your microdecisions and optimizations are still in-line with the holistic goals. What was the original intent of the proxies as they pertain to your prosocial pillars? Are you still measuring what you think you are measuring? It can be worth setting up a regular official review, but equally valuable is training the people making decisions in the field so they catch mistakes as they happen.
Player interviews: One method of capturing nuance is to talk to players directly. If you are only watching dashboards, you only witness what you are measuring. In depth qualitative interviews with key players uncover new trends and behaviors. What do they care about? What motivates them? What new skills or organizational techniques are they now using? You can then follow up with quantitative metrics gathering to understand the scope and impact of those behaviors.
Dark Pattern: Over reliance on extrinsic motivators
Motivational crowding is when a task that someone is intrinsically motivated to perform is instead encouraged with an extrinsic reward. As soon as the extrinsic reward ceases to be given, the person no longer wishes to do the task. Even if they were excited to originally do it for no explicit reward. The intrinsic motivation is said to be ‘crowded out’ by the extrinsic motivator.
Extrinsic motivators are much easier to put into systems. The game can dole out standardized rewards of commodity goods or currency and they can be triggered in a rote fashion upon the mechanistic completion of a well-defined task. For example, if we want to tell a person that their comment on a social media site was viewed and appreciated, we could add a ‘Like’ button and then report the total number of likes accumulated. We’ve turned a complex relationship into a tidy number you can watch ticking upward. Ding!
Intrinsic motivators are generally complicated and tied to an individual’s internal needs. Though intrinsic motivators are more effective, longer lasting and result in higher overall happiness of the person doing the task, they are far more difficult to design, measure and systematize.
The result is that designers tend to rely quite heavily on extrinsic motivators. And in the process, inevitably damage our intrinsic motivations. This is highly problematic in social spaces, since social interactions tend to be intrinsically motivated and involve nuances unique to each individual relationship. Whoops.
In this era of modern computation, there is no reason why we can’t be far more targeted and contextual with our incentives. By tracking where each person is on their personal journey through their game progression, through their acquisition of friends, through their micro actions we can create personal models for what they might desire.
Stop designing for populations of average players and start designing for the intrinsic motivations of the individual player. Even small shifts in this direction, such as facilitating activities based off the state of a player’s direct friend network, can have large positive impacts on engagement.
Scope of metrics and their impact as extrinsic motivators
Social metrics such as a ‘Like count’ can quickly turn into extrinsic motivators if you aren’t careful. Carefully scope how your metrics are revealed to minimize negative impacts.
Internal: In general, most social metrics should be Internal, metrics that are only shared with the internal development team. This eliminates the dangerous feedback loop in which a person attempts to influence their own metrics.
Private: Slightly riskier is a Private metric where you share a person’s information with just that person. This creates a feedback loop but it is limited to only that individual and they have full control over what they do with the information. This is especially important for sensitive information around reputation or facts that could be damaging to share without a foundation of trust.
Group: One layer out from the individual are trusted friend circles or affiliation networks. We start to see social metrics generating politics, censure and other group dynamics. These are intense feedback systems that can result in unexpected results. This is known colloquially as drama. At this scope, we also start to see new intrinsic motivators based off status start to arise. Status-driven motivators can start to offset some of the motivational crowding. Of course this only works for high status individuals in the group. Low status people who don’t see their public metrics move respond as if they’ve been shunned. This negative outcome is exacerbated by social anxiety.
Public: The most dangerous metrics are public ones that are shared broadly. We get all the drama of group interactions, but we also get in-group and out-group competition. Various tribal groups use sensitive information to engage in hate mobs, griefing and other forms of abuse. Popular status seekers, especially those with narcissistic tendencies, thrive in these spaces. Use public social metrics with immense care.
Questions to keep in mind
There’s no clear fix for this issue. Instead, I try to keep myself honest by asking several questions periodically.
Are you applying an extrinsic motivator to something that players would do of their own volition? Many times we apply rewards to activities out of habit. Pause for a moment. Does this social interaction really need a flashing reward screen with a loot drop of crafting materials or currency?
Can you build the context within which intrinsic motivation can take over? Instead of mechanically telling players to do activity A for reward B, you can instead provide a space to do Activity A and highly visible affordances. Give players a small amount of room to stumble upon the activity. And pursue it if they want. Animal Crossing is a lovely example of this sort of constrained small space and intrinsically motivated activities.
Dark Pattern: Replacement of prosocial values with selfish values
The most likely source of corruption of a prosocial economic system is when it managed by an unreformed capitalist. An executive who believes in the selfish nature of humanity will tend to replace the core prosocial values with processes that are shortsighted and profit motivated.
Economists (and capitalists who love economics) tend toward evil
Those that practice economics — and to a degree modern American capitalism — are heavily invested in an implicit system of self-centered moral values. A well-documented phenomena is that economists behave more selfishly than other professions. They are less fair, less loyal, less cooperative, more prone to deception, and give less to charity. This appears to also impact executives who use economic framing of problems.
In part, this seems to be due to the field of economics attracting selfishly motivated people. But it also appears to be the result of indoctrination. The repetitive doctrine that humans are best modeled as selfish rational optimizers creates a set of selfish social norms that practitioners consciously or subconsciously follow. The act of studying economics makes you a morally worse human-being (by most definitions of morality.)
There is another possible cause for this selfish behavior, which is economists’ high exposure to commercial systems. The presence of currency itself, and the tracking of it and focusing upon it, seems to lead to rationalizations that justify selfish behavior. We see this in particular in games as a dimension of the above dark pattern, reliance on extrinsic motivators. Pure exposure to extrinsic motivation systems, of which accumulation of currency is one, seems to bend human behavior toward norms that justify maximization of that accumulation. It is possible that the persistent high exposure to game currency — and as we have stated, almost all games have currencies and tangible economies of some kind — has the same effect that exposure to economics has on economists.
Values as identity
These values are embedded at the level of personal and tribal identity, and so in groups they become naturally amplified. When challenged, the result is a blunt dismissal of any information that disagrees with the existing world view and a re-entrenchment in existing beliefs. One merely needs to read the responses to some of the studies on selfishness in economics to realize this is not an open-minded, self-reflective group. (My favorite is that claim that economics is perfect, it is merely all other fields of study that mistakenly train up altruistic, prosocial citizens)
A clash of values
When worldviews clash, those with the more power wins. A powerful executive, indoctrinated in the ways of selfish capitalism, is very likely to dismiss the prosocial value at the heart of social system design. A very difficult argument to win. Prosocial design presupposes an altruistic model of human behavior that has long been scrubbed from the selfish predator’s worldview.
We’ve seen this first hand with companies like Zynga, where capitalist managers methodically and deliberately optimized delightful games about creativity and sharing (Farmville) into viral advertising engines that actively degraded relationships. Even in the face of their market crashing, at no point did they stop and question their selfish worldview. Instead they doubled down on burning out more players to maximize revenue extraction.
Be explicit about key prosocial values. State prosocial design goals as key product pillars that are inviolable. Have someone who understands prosocial systems own and enforce these across the entire company.
Tie economic value to the maintenance of prosocial value. Get the profit-minded forces at the studio on board with a win-win partnership. Use facts like intrinsic motivations being 3X as powerful as extrinsic motivations. Or the crowd-out effect of extrinsic motivations damages long term LTV. These can turn the selfish desires of executives into support for prosocial design. Align prosocial design with smart business design.
Don’t put business in control of prosocial systems: Create an organizational firewall between those handling the prosocial systems and those directly driving profits. Acknowledge that your short term focused business teams may not have the values, goals and mindset to properly grow and manage business critical social systems.
Hire prosocial executives that know the value of these systems: Instead of fighting a losing battle with entrenched executives who have a long history of fetisizing selfish economic behavior, seed new teams with strong leadership who already buy into the mission of building prosocial games.
Ethics standards for social systems designers: A longer term dream is to create ethical standards for social systems designers. Perhaps in the future we could build training programs for this deep skill set. And bind trained students to a set of ethical standards. There would need to be some form of censure as well if lines were crossed.
In conclusion, we have described:
All games with resources have economies.
Economies that do not consider their end aesthetic outcome devolve into antisocial patterns. Specifically toxicity and loneliness.
Real world economics often undervalues or ignores prosocial behavior. It is challenging to apply directly to games.
There are a handful of prosocial economic patterns we can use as designers.
Economies, being motivational systems, are inherently subject to exploitation and dark patterns.
Summary of Patterns
Prosocial mechanical and economic patterns identified in this paper include:
Measuring the Unmeasured
Measuring trust (quantifying social capital)
Positive sum resources
Integrating social metrics with business success
This paper is intended as an initial exploration in the domain of prosocial economic game system design. Much further work is needed to explore, codify, and test these patterns and ones that may be discovered after.
Patterns that we identified but have not built out in this paper include: 1) group leveling, 2) friendship resource (differentiated resources), 3) incentivizing generosity, 4) nurture play, and 5) expressive orthogonality through fashion.
Further areas of interest uncovered by our preliminary exploration include:
Prosocial economic patterns: Further expanding within the macro-patterns of prosocial design patterns.
Positive sum design: Expanding the design patterns for non-scarce (positive sum) resource mechanics (game design without scarcity).
Public goods design: Expanding design patterns around managing public goods, especially via decision mechanisms
Therapy: Leveraging disordered personality behavioral archetypes and corresponding treatments for CBT-based (as one example protocol) game progression systems.
Managing social progression systems: Scaffolding social skill development, and differentiating social skill development from meaningful relationship cultivation.
Transfer to non-game environments: Transferring skills and social capital experienced in game environments outside the game environment (crossing the membrane).
Dark patterns: Further exploring, and codifying, dark design patterns in extrinsic motivational systems, and their consequences;
Education: An open protocol of transparency and education regarding game-based motivational systems and economy design.
Ethics: Ethical rules for social systems designers as well as institutions who help promote those rules.
Appendix I: Towards an action-based framework for mitigating loneliness
A widely used instrument for detecting loneliness is the Roberts UCLA Loneliness Scale. First developed in 1978, it is estimated to have been used in 80% of scientific research studying loneliness, and has been found valid by multiple meta-analyses — so it makes a good starting point.
The original 20-factor Loneliness Scale has been condensed into smaller sets including the RULS-8, RULS-6, and RULS-3. We are primarily referencing the 1996 20 point scale, and distill from that scale some concepts sometimes referred to as “dimensions” of loneliness. These dimensions have been studied in medical research, but for our purposes we are proposing a conceptual framework of loneliness dimensions most relevant to game behavior:
Exposure: Relating to social vulnerability, exposure is feeling unsafe because one is alone. This often relates to the involuntary nature of the exposure, as contrasted with, for instance, solitude, which is a positive feeling of isolation rooted in its deliberate and voluntary nature. Exposure is a broad category of loneliness relating to index measures “I lack companionship”, “I do not feel part of a group of friends”.
Ostracization: An experience of social punishment, ostracization is the feeling of being “left out”, and especially being “shut out”, of one’s valued social group.
Shyness: A withdrawn state resulting from feelings of social isolation. The feeling that it is very risky to disclose oneself to others; a feeling that others will not understand you. Relates to “I am unhappy being so withdrawn”.
Unfit/Outsiderness: A feeling that one is around others but does not belong there. Belonging is a broad concept (discussed below), but “unfitness” relates to being in the presence of others with whom one does not feel connected. Relates to measures “People are around me but not with me”. Importantly emphasizes the presence of others who are not of one’s belonging group, with the absence of one’s belonging group.
Loneliness causes stress in humans broadly, relating to feelings of vulnerability, and can also provoke scarcity mindset, in which a host of negative outcomes occur. Scarcity mindset is a stress-induced “tunnel visioned” state that causes short term thinking associated with long term net negative outcomes.
Kinds of loneliness
As a creative empathy exercise, it can be helpful to identify distinctive, separate feelings of loneliness for which there aren’t English words:
I have friends but I can’t count on any of them
I have close family but they don’t understand me
There are things I can’t share with my friends
There are things I can’t share with my family
There are things I can’t share with my spouse
I have good friends but our group doesn’t have purpose
I feel lonely and socially exhausted around my friends
These limited examples illustrate some of the complexities of loneliness, which represents a rich artistic space rife with subtlety and inner conflict.
Structurally, it can be helpful to think of two large categories of loneliness:
Emotional loneliness: Lack of an attachment figure
Social loneliness: Lack of social connection; social vulnerability; social isolation
It is important to note that not all loneliness is purely social or purely emotional; these are two separate dynamics that combine to produce the emergent sensation of loneliness. Emotional loneliness in particular is especially tractable in digital/fictional experiences; reading a book or taking care of a fictional animal can assuage emotional loneliness, even though these are solitary activities.
Amongst the more complex category of social loneliness, we can identify sub-categories as well:
Affinity (sharing interests)
Recognition (feeling known deeply by others)
Belonging (feeling accepted): These can be split into Acceptance (feeling wanted and not judged) and Usefulness (filling a distinct need/role in one’s group)
Companionship (feeling the presence of other social creatures)
When we are talking about prosocial game design, we are, in part, talking about game design systems that address the social pain of loneliness. By dividing loneliness up into its distinct constituent categories, we can more accurately aim experiences at assuaging specific target areas.
What game designers need to know about loneliness
From a game design standpoint, there are some important high level takeaways:
There are multiple types of loneliness; it is not a single phenomenon;
Loneliness and isolation are distinct psychological problems;
Loneliness can be clustered within orthogonal sub-categories that must be independently addressed;
Loneliness is best thought of as a specific kind of social pain.
Prosocial design has a lot of interesting tools for tackling social loneliness. We have fewer tools for tackling emotional loneliness though this is a fascinating area of further investigation.
Appendix II: The economics design lens
Economics is one of many potential lenses, or perspectives for understanding a game systems. As a designer, it is critical you can swap out lenses for examining a problem as needed.
For example, you can take a system like player chat and look at it via different lenses and learn something new from each.
Seen through an economics lens: Use the tools of economics to assign values to relationships and track the time payments back and forth between chat agents.
Seen through a psychological lens: However, we could just as easily look at chat from a psychological perspective and gain a set of insights that are impossible to capture with a purely economics lens. The emotional tone of a snarky response for example can be challenging to model with our simplistic set of tokens and transforms.
So what is the economics lens good for? It helps to think of the lens of economics in game design as having a couple basic superpowers. These end up also being its core weaknesses.
Economic super power: Analysis and balancing
Almost any game with a heavy systems focus benefits from using economics to balance or tune the systems to achieve a specific aesthetic outcome. There are several key steps in this process that build upon one another.
Definition: The economics of a system become visible the moment you start defining the exact tokens, source, sinks, etc. What you find out depends entirely on the quality of your definitions. And poor definitions result in weak insight.
Economic analysis: Once you’ve definite the components of the economic system, we can start interrogating why something is happening. The type of analysis you can do is limited to answering questions about resource flows and transformations. There are huge swaths of the gameplay experience that are hidden or only observable through secondary effects. For example, economic analysis can say little about a beautiful experience, but it can track the price and availability of that experience.
Balancing: Finally, an economic lens allows us to ask what-if scenarios, adjust our various defined economic components and then analyze and observe the results. You are always balancing in order to some overall aesthetic goal (In the Mechanics Dynamics Aesthetics sense of the term).
Small errors accumulate
Now, the clever reader will notice that the balancing step is built upon an unreliable stack. If your definitions are incomplete, your analysis will be flawed. If your analysis is flawed, the initial balance ideas will be impossible to verify. This is particularly challenging when your changes alter the very nature of the virtual world you a measuring. There less in common here with natural science than might be hoped. The iterative act of balancing an economic system in a virtual space can quickly turns into feedback loops where small errors accumulate.
Add in poorly modeled humans as key decision drivers and you can very easily design something that is a bit of a mess. Economies in games are often prone to inexplicable and unexpected exponential failures. We call these disasters by different names (ex: Mudflation, grindy, OP) but they are often failures of economic balancing.
The predominance of toy-like economies
So we punt and build toy-like economies that are trivially understandable (as is the case with most single player games). Or we build systems that are stable short term and a spiralling disaster only if left unmanaged (most multiplayer games). For multiplayer systems, we continuously micromanage them into some rough stability using god-like powers to shift the virtual world’s physics if things get too far off.
The bigger lesson here is that in practice, economic tools are essential yet unreliable design tools. Especially at scale. So we build systems that compensate and can be balanced despite the flaws in our tools.
Economic super power: Efficiently generating value through trade
Perhaps the single most meaningful insight that economics has added to the world is that trade generates material value for society at scale. The orthodoxy of economics may have poisoned a richer discussion of the topic, but kudos for the acknowledgement the historical practice and clarifying why trade is important.
Trade in games is mostly studied in the context of multiplayer games with player-to-player exchange of virtual items. For a primer, you should read Virtual Economies: Analysis and Design by Lehdonvirta and Castronova. Though designers have learned many lessons over hundreds of MMOs, it still remains a niche field of practice. In this modern era, many hyper focused, metrics-driven teams try to stamp out trade entirely due to the unmanageable chaos it creates. Once you introduce capitalism into your toy economy you’ve opened Pandora’s box of design challenges, both economic and culture.
The basics of trade
The basics go back to Adam Smith.
Person 1 has access to resource A. But they know they really need resource B.
Person 2 has access to resource B. But they know they really need resource A.
So they trade with one another! Now they both have what they actually want.
The magical bit: This was a positive sum exchange. Player 1 is happier and so is Player 2. Value has been generated from trade.
A drive for more efficient trade
This sort of basic barter certainly works, but the logistics are complicated to arrange. So we introduce an intermediate currency and use that to value both resource A and B. Now each person can just set a price for goods they are willing to buy or sell. As long as there’s a cheap way of sharing prices, any person can sell their low value goods to someone who values them more. And then take that excess money to buy the stuff they really want.
So why is this so interesting?
Prices are set locally. The local agents determine what they value, so in theory you can just put a bunch of independent agents that know their own needs together in a common trade area and you’ll start to see market dynamics. The setting and sharing of an agent’s prices are a decoupling mechanism that allows the system to create somewhat scale free networks.
With relatively little managerial oversight, a vast number of people can trade with one another efficiently. You do need some institutional protection or bad actors can start to sap profits through theft and extortion.
Each iterative trade in turn generates enormous value across many of those participating in the market.
That excess value is now possible to redirect into things like culture, leisure, research and moving beyond mere survival.
This process, when the right conditions exist, can be explosive. Huge amounts of material and human resources gain explicit value and are efficiently send zipping around in complex, somewhat self-organizing system that radically transforms everyone and everything involved. Capitalism, writ large, has according to some metrics, resulted in some of the greatest increases in human health and stability history has ever known.
The inevitability of trade
Trade has a degree of inexorable social physics to its emergence. Most large scale societies develop it in one form or another; though rarely to the degree of modern capitalism. We see this capitalist explosion in multiplayer games all the time. The basic requirements for barter seem to be:
Players can exchange goods,
Those goods are differentiated and randomly distributed
Players can negotiate relative value with one another.
Once barter is in place and the society is stable enough to create community standards, players develop an emergent currency (usually some easily tradable item with a stable supply) This is then used to facilitate efficient trade networks. In mere weeks or months there are merchant classes, black markets, trade, commodities, trust networks and more.
Another perspective (a lens!) on economics is that it is a memetic virus that transforms a society and distorts it to fit the functional needs of the virus as well as fostering the cultural values that help the virus spread and thrive.
Game developer superpower: Economic design tools available to game developers
Many of the practical issues that weigh upon real-world economics impact game developers less. Game developers benefit from the following factors:
Large human populations: A successful online game has many thousands of players. We can run experiments with real people without as much reliance on bad models or small samples.
Sources of new players: We have access to new players who are more of a clean slate for testing out new ideas.
Rich data collection: Our analytics can capture any economic or social interaction inside the game. Including rich historical streams of individuals or populations.
Control over most laws of nature: We have full control over sources, sinks, transforms and any associated incentives. We can change the world to fit the model almost as easily as changing the model to fit the world. Players, math and time are still out of our immediate control.
Less politics: Online games are benign dictatorships with voluntary membership. Though there are some checks and balances on performing radical economic experiments, there immense leeway to make changes.
Appendix III: What does economics say about altruism?
Most economics theory is based off the idea of a rational, self-serving actor. Economic is not wholly ignorant of altruism. It merely is treated as a series of side theories that are not broadly integrated into mainstream economic models or policies. It is worth mining these theories to see if any of them are applicable to the design of prosocial economies.
What is altruism?
In economics, altruism can be defined as investment in public goods. These are shared resources or investments that benefit multiple people, not an individual owner.
Note that altruism and prosocial behavior in trusted relationships are not exactly the same thing. Altruism does not require trust, merely a shared public good. Though shared relationships at the heart of prosocial systems are almost always a public good within the local context of the relationship.
Onto the theories. We’ll start out with the earliest and most wrong theories and then progress to ones that slowly incorporate more experimental support.
If people are rational actors, when it comes to public goods, selfish people should act as free riders. Assuming most people are selfish, this would result in public goods being under provided for because most people free ride on the irrational contributions of a few. Examples of this include
However, people free ride less than expected. They are not purely homo economicus, the selfish man. Cases where they over invest according to self-interest theories include
Contributing to open source software
Theory: Incentivized prosocial behavior
Not willing to let go of the belief that people are inherently selfish, a variation of the self-interest theory is that people contribute to a public good are in fact getting paid. It is just in the form of non-obvious currency such as prestige. In practice, this doesn’t hold up since people donate charities anonymously.
Theory: Pure and impure altruism
We now get into outcome-based prosocial preferences. What if people inherently enjoy seeing the well being of others, so they contribute to public goods? Imagine we gain internal utility (a ‘warm glow’) by helping others, so helping is intrinsically rewarding.
This also doesn’t match observed results. First, even when others are doing well and don’t benefit, people still donate. Second, such an intrinsic motivator would be a stable source of motivation. No matter what we should keep donating if there is continued need. But prosocial behavior decays with repetition. And people have this distinct tendency to punish the behavior of others. Which is a bit inconsistent with a purely altruistic motivation.
Theory: Inequality Aversion
What if we just hate inequality? Imagine that one’s relative standing in the leaderboard of income distributions drives people to reward those less well off and punish those more well off.
This one doesn’t explain a lot of nuances about when and how people punish and reward others. Especially across different cultural contexts.
Theory: Reciprocity and Conditional Cooperation
Okay, what if who we are interacting with matters? Now the theories start to include some basic social psychology like reciprocity in their human models. And some interesting findings start popping up.
Norm enforcement is intrinsically motivated: Expensive punishment of free riders is behavior that people perform even in the face of repetition. Almost all extrinsically motivated behavior drop off with repetition. This is a key finding since it suggests that enforcement of social norms is an intrinsically motivated behavior.
Altruism depends on perceived laziness: If someone sees a recipient as lazy, they tend to reduce donations to them.
Reciprocity drives behavior: If you give a gift, the other party will often give one back. However, intentions also matter. Why someone does something impacts how the other party reciprocates. This is a big effect that also continues with repetition.
These observations also lead to the prediction that if more people act prosocially, an individual will be more likely to act prosocially. For example, one’s donation depends on the donation of one’s reference group. A 10% increase in donations by the reference group results in a 2-3% increase by the individual. So people are conditionally altruistic based off the social norms of the group.
Theory: Self-identity theories
A person ends up identifying with a reference group. And they’ll be more prosocial if two factors are true
The reference group thinks the action is good
The action is a valuable signal of the person’s good traits as determined by the social norms of the group.
Theory: Frame effects matters
Now we start moving away from universal models of human behavior and begin to dig into the question of how context (aka the institutional environment) impact what someone decides to do. I think of this as economists discovering the importance of level design. There are a large number of studies on ‘frame effects’.
Do you have knowledge of free riding by others
Do free rider know they are being observed and by whom
Can you punish free riders
How strongly can you punish free riders?
How were resources earned?
Is the recipient a charity?
Is the recipient a close friend or relative? A general group or a specific person?
Is this your ingroup or an outgroup?
Are the bad circumstances you are alleviating due to bad luck or poor choices?
Did you form an agreement with the other party? Even if the agreement has no binding value, people rarely break them.
Additionally, the type of communication you have with the other benefactors of the public good matter. At this point we are starting to get really close to friendship formation and intensity as an accelerant for trust accumulation.
Do you get to talk to the other person?
Was it face-to-face? If so that results in a strong impact on altruistic behavior. .
Was it via a computer? If so there’s a much weaker impact on altruistic behavior.
Framing is another name for much of what we do as game designers as we set up contexts for players activities. There’s a wonderful exploration of reframing economic activity using game worlds in the book Stealing Worlds by Karl Schroeder.
Theory: Monetary incentives in the world of frame effects
Finally we roll all the way back around to extrinsic motivators. But this time we are looking at ways that the system designer can create frame effects that alter an individual’s behavior.
If the systems makes an action cheaper or easier or slightly incentivized, the intervention can increase prosocial behavior.
However if you increase benefit too much extrinsic motivation ‘crowds out’ intrinsic and behavior drops.
If monetary giving goes up, so does giving of time (complementary goods)
Reliance on extrinsic motivators selects for selfish people.
Theory: Heterogeneous populations
At some point in all of this, someone raised their hand and says, “But what if different people engage in different strategies?” Individuals are heterogeneous; some tend to use one pattern of behavior while others use other patterns. A community is an ecosystem of agents, who depending on local conditions, take on different social roles.
In some tests: 23-30% of the population is always acts selfishly. No matter what. But 50% operate conditionally and are likely to behave altruistically if the right conditions exist.
Presence of a reciprocal person causes other conditional people to reciprocate. Thus shifting the whole dynamic.
Maybe teaching ethics helps create less selfish individuals. Economics hasn’t studied this yet. (Students going into economics are more likely to be egotists already)
Amy Jo Kim, Chief Executive Officer at Shufflebrain
Crystin Cox, Principal Program Manager at Microsoft
Daniel Cook, Chief Creative Officer at Spry Fox
Erin Hoffman-John, Lead Prototyper at Google
Isaiah Cartwright, Game Director at ArenaNet
Kyle Brink, Director of Production at ArenaNet
Link Hughes, Game Designer at ArenaNet
Many of the problems associated with making an MMO, a Massively Multiplayer Online game, come in large part from the very first term: “Massively”. An MMO is notably tricky to build due to technical issues involving server scaling, as well as design issues involving scaling economics, politics, level design, pacing, persistence, and progression. A rule of thumb is that development costs grow exponentially as the number of players increases, but for many years, there’s been an unquestioned assumption that bigger player numbers are inherently better and therefore worth pursuing.
Yet we see clear counterexamples. Many early MUDs (Multi-User Dungeons) involved populations of dozens-to-thousands of people and still have vibrant communities to this day . Multiplayer Minecraft is wildly successful, despite its reliance on relatively small, instanced servers. And many modern hit games, like Fortnite, are online games that successfully limit their focus to matches of 100 or less.
What are the critical design lessons from these smaller online games—and how can current research and understanding of social psychology help make sense of those lessons? We combined our decades of experience designing social systems for online games and a deep dive into current academic research to arrive at a set of best practices and common pitfalls.
What we’ll cover in this paper:
What we can borrow from social psychology
An overview of friendship formation
Dunbar’s Layers and the constraints they place on social systems design
Social group and the constraints they also introduce
Big design insights
Opportunities for fulfilling the social motivations of players
Borrowing from social psychology
When researching what it meant to make human-scale systems, we found several key concepts from social psychology. Each provides a set of constraints for social design. Social game design operates within the physical and mental constraints of the human animal, so it pays to understand these constraints and build them into our designs.
A friendship is a single social bond between two people. Friendship formation is a distinct process involving proximity, similarity, reciprocity, and disclosure.
An individual has a highly structured distribution of relationship bonds. People tend to have a maximum of 150 total friendships , including 50 good friendships, which include 15 best friendships, which, in turn, include 5 intimate friendships. This web of relationships can be modeled as an egocentric network with the individual at the center. This paper focuses primarily on the implications of Dunbar’s Layers for human-scale social design in online games.
A social group of is a collection of people brought together for a shared task or interest. Groups contain multiple overlapping individual networks. The performance of the group, as a whole, is dependent on how the friendship bonds across the entire group are leveraged to accomplish the shared activity.
At the most basic level, human-scale game design is about creating strong relationship bonds between individuals. Most game populations will start out with weakly-bonded individuals. You’ll need to create activities, incentives, spaces, and social structures that actively build friendship in order to enable even the most basic of trust-based activities.
This section is a brief overview. For more detailed discussion on this topic see the 2016 Project Horseshoe paper on game design for building friendships.
The basics of growing friendships
Friendship formation requires 4 key ingredients:
Proximity. Being close together to one another encourages frequent serendipitous interactions.
Similarity. Players will generally be more likely to become friends if they perceive one another to be similar.
Reciprocity. Players must engage in escalating back-and-forth interactions in order to negotiate shared social norms.
Disclosure. At higher levels of friendship, there needs to be an opportunity for safe, consensual, intimate sharing of weaknesses.
You can take any two players, put them together in matches for hundreds of hours, and if the above criteria are not met, they are unlikely to become friends. Naively tossing bodies at one another is not efficient social design.
The micro-design of social systems is all about reciprocation loops
As a designer, you specifically have to build opportunities for consensual reciprocity into your game loops. These look like the following:
Opening. A person performs an opening action that a second person observes. This action has a cost in terms of time, investment, skill, and other resources. For example, Player 1 asks a question in open chat, which costs time and social capital.
Opportunity. An opportunity is created for the second person to respond. For example, Player 2 sees the question and can answer in the same chat.
Response. The second person performs a responding action that acknowledges the first person. This also has a cost. For example, Player 2 offers an answer to the question in chat, which also costs time and social capital.
Acknowledgement. The first person acknowledges the second’s response and the loop is now complete. For example, Player 1 thanks Player 2 for answering their question in open chat.
Escalation. Either during the Response or the Acknowledgement stages of the loop, a person can escalate by opening up a new loop or prompting additional response. This is an opt-in act. For example, Player 1 asks for additional details.
Rejection. Either during the Response or the Acknowledgement stage, a person can either not respond or respond inappropriately, which also collapses the loop. For example, Player 2 mocks Player 1 instead of answering their questions.
Link loops together in an escalating structure
Friendship is a long-term process. Each reciprocation loop may take seconds initially, but you need thousands of linked loops to build a robust friendship.
Create low-cost loops with low rejection costs for early relations.
Create higher-cost loops for later term relationships.
Build space inside the later loops for expression and definition of the personal relationship between two players.
For example, friendships in an MMO tend to start out with parallel play, where two people simply see one another’s name while fighting monsters in the same area. This then escalates to helping one another; a heal spell, an emote of celebration, a dropped item. The two players may start chatting in order to take down harder monsters; they may also friend one another and start talking more about who they are and what they are interested in.
At each stage, interactions take increasing time and effort. And involve richer communication. Each micro-loop is not very expensive, but over long-term repetition of many such loops, the relationship accumulates meaningful amounts of trust.
Design these systems with the same rigor, care, and eye for economic balance that you’d put towards a combat or progression system.
Design for consent
Almost every stage of these reciprocation loops involves consent. Each party must consent to both starting, continuing, and escalating the relationship. At any point, it is totally fine for one or both parties to pull away, either to slow down or move onto some other relationship opportunity.
In the context of Dunbar’s Layers, there’s a limit on the number of people an individual can have in their lives. The process of building friendship is also the active process of curating relationships that are healthy and mutually satisfying. When players actively and enthusiastically consent to engage in your reciprocation loops, you’ll find that the relationships you build in your game are more authentic, last longer, and ultimately provide more value to your players.
An individual organizes their friendships by strength of their one-to-one bonds. They have close friends they turn to in times of crisis and more casual friends, with whom they interact with less frequently. Social psychology has been studying these friendship networks for decades. One of the more reproducible findings is the existence of strong limits on the number and strength of bonds an individual can have with other humans.
Robin Dunbar is an anthropologist who, in the 1980s, posited that a human can have up to 150 meaningful relationships, based off his investigations into primate social brain structures . When others attempted to verify this prediction, they found that “Dunbar’s Number” kept coming up in long-lasting groups in the real world. It’s been replicated across a huge number of domains including businesses, religious organizations, military groups, and, of course, MMO guilds.
Multiple layers, not a single number
However, as researchers dug further into the data, they noticed additional stable clustering at lower numbers of connections. These smaller clusters were part of a person’s total of 150 relationships, but involved much stronger bonds.
Visualization of Dunbar’s Layers. Each block represents time to build one relationship in that layer.
Dunbar’s Layers, as these smaller clusters are known, are generally organized as follows:
1.5 people: The intimate couple or the individual.
5 people: Intimate friends or family. People you can call in a crisis.
15 people: Best friends. People who you can ask for sympathy.
50 people: Good friends. The majority of regular social contacts and, by extension, all of one’s emotional and economic support .
150 people: Casual friends or acquaintances.
Note that each layer is cumulative and contains the previous layers, so your best friend layer contains your intimate friends layer as well. A common confusion is to think you have 5 intimate friends AND an additional 15 best friends, etc., but those 5 intimate friends are part of your 15 best friends budget.
These numbers are averages and, in reality, describe tight ranges. In practice, different people have different degrees of social needs and relationship-building capacity. For example, many men average 3-4 relationships that they would consider intimate friends or family, while many women average 7-9 such relationships. Some people, known as “super-connectors,” have upward of 200–250 meaningful friendships.
With larger data sets, we’ve discovered these relationships layers also extend past actual friends.
500 people: Nodding acquaintances.
1500 people: You recognize their face, but that’s it. 2000 faces seems to be the absolute maximum that a human can recognize and when you learn a new face, you drop one of the other faces you’ve memorized.
Implications of Dunbar’s Layers
On first glance, Dunbar’s Layers are a mere curiosity. However, they fundamentally shape how people socialize. The following are aspects of Dunbar’s Layers worth knowing about before you attempt to use them in a design.
Dunbar’s Layers are egocentric networks
Visualizing the innermost Dunbar’s Layers as an egocentric network. Note all connections are from the perspective of a single individual.
An ideal way to visualize Dunbar’s Layers is as a network of connections, not as separate layers, per se. In research, this is known as an “egocentric network.”
Put a given individual at the center of a nodal network.
Then map out bonds going directly to that individual. You’ll end up with an average of 150 meaningful relationships connected to the individual.
Some bonds between the individual and their friends will be stronger than others. These bonds map onto Dunbar’s Layers. For example, a person will have an average of five strong bonds.
There are several different ways egocentric networks can be used in analysis of individual relationships:
Dyads: The relationship between any two individuals involves two connections, not one. Each person has their own perception of the connection’s strength. It is possible—and, in fact, common —for these perceptions to be unequal.
Triads: Friendship networks can be analyzed by looking for triads—groupings of three people with at least two relationships between them. The strongest network structure is a “Triadic Closure,” wherein all three three individuals share mutual friendship bonds of equal strength.
Strong Ties: When a person has a direct relationship with another person, it is known as a strong bond. Strong bonds are key to meaningful relationships, support networks and overall happiness.
Weak Ties: When a person’s friend has a relationship with another person, but the original person does not, it is known as a weak bond. Weak ties are critical to connecting independent social groups and are particularly important for the functioning of large scale economic and informational systems. Weak ties also populate the 500 and 1500 person layers. When we start discussing social groups, weak ties become a very important concept.
Super-connectors: Some individuals have substantially more than the the typical number of connections. Known as “super-connectors,” they end up acting as hubs that connect disparate friend networks together.
Close friendships have a strong influence on quality of life
Overall, having a deep friend network has an immensely positive impact on your health and happiness.
High quality, high intensity relationships are positively correlated with increased life satisfaction .
Depression is lower overall in individuals with rich friend networks .
On the flip side, toxic relationships have an outsized negative impact on mental and physical health. Something to think about when we deal with trolls in our games .
High trust relationships take time and the right context
Building friendships takes many hours of interaction.
The time required to build a single friendship bond :
Casual Friend: 40-60 hours
Good Friend : 90-110 hours
Best Friend: >200 hours
If you meet with someone for 1 hour each week, it will take roughly a year before you consider one another even casual friends. Friendship formation is not a cheap activity.
Maintaining relationships takes less effort. Three key variables here are kinship, gender and frequency of interaction. Kin bonds (bonds with family members) require less maintenance than non-family friendship bonds and do not seem affected by distance. Men tend to affirm bonds by participating in activities together, while women tend to talk with another. Higher strength bonds needs more frequent renewal than lower strength bonds.
Casual friends meet up at least once a year.
Good friends meetup up once every 6 months.
Best friends meet up once a month.
Intimate friends meet up at least once a week.
You can’t beat the system
One way of thinking about the constraints suggested by Dunbar’s Layers is to imagine you have a budget of cognitive resources that can be spent on relationships. The physical limits of your human brain mean that you only have enough mental budget for a total of roughly 150 relationships.
Humans have developed a few tools that have expanded our ability to organize into groups well past our primate cousins—most notably language—but also large-scale systems of government and economics. In the early 2000s, people assumed that new technologies like online social networks could help break past Dunbar’s Number; by offloading the cost of remembering our friendships to a computer, we could live richer, more social lives, with strong relationships to even more people.
We now have copious data that this is not the case. Studies suggest that there’s still a limited budget of cognitive resources at play and even in online platforms we see the exact same distribution of relationships .
If anything, social networks damage our relationships. By making it possible for us to cheaply form superficial relationships (and invest our limited energy in maintaining them), such systems divert cognitive resources from smaller, intimate groups out towards larger, less-intimate groups. The result is that key relationships with best friends and loved ones suffer. And, unfortunately, it is the strength of these high-trust relationships that are most predictive of mental health and overall happiness .
What is a social group?
A social group is a set of individuals labeled as being in a group. This is inherently a fuzzy concept, since the true structure thereof is an overlapping network of egocentric networks, partially-negotiated social norms, and ever-shifting relationship bonds.
There are three dominant perspectives on what makes a group.
Social Identity perspective: “I feel like I’m part of a group.” An individual can self-identify if they are part of a group. By doing so, they start practicing the social norms of the identified group. This is the perspective that gives birth to either imposter syndrome or a feeling of belonging.
Self-categorization perspective: “I feel like you are part of a group.” Someone looking at the behavior of other people can identify if others are behaving as part of a group. By doing so, they treat those people as if they operate using shared social norms. This is the perspective that gives birth to stereotypes.
Social cohesion perspective: “We act according to shared social norms.” A set of people that act in similar manner across a variety of social variables is a group. Those variables include:
Shared goals. Are we working towards the same purpose?
Roles. Who does what?
Status relationships. Who has power?
Norms. How do we work together?
Sanctions. What happens when norms are violated?
Additional factors that can be used to determine group cohesion include:
Group size. How many people are in the group?
Group trust. How strong are the bonds between individuals in the group?
Group stability. Does the group come together for a short period of time or is it a stable, persistent entity?
The social cohesion perspective proves the most design insight, so we’ll be referencing it for the rest of this discussion.
Common groups sizes roughly align with Dunbar’s Layers. However, these are not identical concepts. Social groups can contain friends of varying trust levels. You could have a small group composed entirely of strangers. whereas a 5-person intimate friends layer is, by definition, an individual’s closest set of friends.
Small friend groups
These are some of the most common task-oriented groups to form. Non-kin, task-focused groups of these sizes often dissipate when the task is complete. Small groups are, however, able to attain the highest strength of social bonds, usually focused on key family relationships.
Pair. 2 people
Small Group. 5 ± 2 people
Medium group. 15 ± 6 people
Large social groups
These are the largest-possible friend groups. Example groups at these sizes include a guild, shard, or map in an MMO, a mid-sized company, or a social organization in a university.
Band. 50 ± 18 people
Clans. 150 ± 50 people
Huge impersonal groups
These larger groups are composed of smaller friend-based sub-groups. However, due to their size being larger than Dunbar’s Number, it is impossible for them to engage in very high-trust activities without additional systems like hierarchy, reliance on weak ties, or codified rules.
Mega-bands. 500 ± 150 people
Tribes. 1500 ± 500 people
Group trust, much like friendship, forms according to a process that imposes constraints on any social design. When we matchmake a set of random players together, we first get a low-intimacy, low-trust group of strangers. We then need to take that group through a period of social norm formation and relationship building. This process creates a rich, highly predictive social contract between individuals, which enables people to depend on one another in dynamic group activities.
Storming: The group attempts to make use of disparate norms for interacting with one another. This dissonance causes conflict. This process is very similar to the reciprocation loops that occur in friendship formation
Norming: The group negotiates common norms that this group will operate by
Performing: The group is able to perform higher-dependency tasks by leveraging their newly developers rules for interacting.
This relates to Dunbar’s Layers in a few key ways:
Existing strong bonds can facilitate group norming. If there are existing friends, they’ve already negotiated a set of common norms between them. This is a foundation to build upon when deciding the group’s shared goal and social contract.
Small groups need to build fewer bonds in order to perform at high levels. You can think of the norming process as one where people negotiate some minimum level of triadic bonds between all members of the group. With smaller groups, there are fewer connections, so the process goes more quickly.
Larger groups naturally have fewer intimate bonds. When dealing with people in the outer layers of our network, we rely more strongly on official rules, rigid social norms, and other forms of bureaucracy. People stop trusting the individual and instead lean upon a system of governance. This is less efficient, in general, due to the cost of maintaining the system, but lets more people participate.
Tips for building group trust
Mentoring. People often obtain high levels of competence through interaction with a coach or mentor. Finding ways to incentivize groups to adopt lower-skill members in order to train them up will benefit both group cohesion and general communal friendliness.
Slower integration. If facilities are not provided for subdividing groups at this layer in to smaller groupings, then every new member must be inducted by introducing them into a central communication channel. This greatly reduces the chances for the new member’s retention in the group, as they must form relationships with everyone at once, rather than being adopted by a segment of the organization and then extending their relationship network outward from that solid foundation. The best groups at this size and above have clear 15-ish person clusters, which are an ideal size for integrating a new member.
Groups vary substantially in how long they last. There are two distinct types of groups worth looking for when designing your group systems:
Primary groups. Long-lasting groups of family and friends. They tend to have strong bonds and a shared sense of purpose. People usually only belong to a few primary groups corresponding with their inner Dunbar’s Layers.
Secondary groups. Temporary, task-focused groups. These can be large or small. People often belong to many secondary groups corresponding to their outer Dunbar’s layers. It is important to allow people to join (and leave) multiple secondary groups, as they need.
Large group stability
Even through group size and Dunbar’s Layers are very different concepts, they do seem to be related. Small groups are stable at around 5 people, primarily due to their heavily reliance on long-lasting family relationships. Large group sizes tend to stabilize around the 50, 150, 500, and 1500 values found in Dunbar’s Layers.
This works in two directions:
Growth: Groups below 150 tend to grow to that size.
Fission: Groups above 150 tend to fragment into sub-groups of 150 or less.
Stable friend groups
Groups at 50 and 150 find long term stability, often measured in years, by benefiting from peer pressure (norm reward and censure), without the need for complex rules and hierarchy. The stronger the sense of shared purpose, the more robust the group. There’s more research to be done here, but this seems to be the maximum group size where, due to the limits of Dunbar’s Layers, you can rely on unaugmented human nature to self-organize into stable groups.
Stable non-friend groups
Stable groups at 500 and 1500 are far rarer because they require the addition of some from of hierarchy in order to be sustainable. Usually this involves appointing a small group of 4-5 decision makers who represent other 50 to 150 member sub-groups. These decision makers represent ‘weak ties’ between groups.
Weak ties are key to the stability of 500 and 1500 player groups. They let a group of 50-150 reach out to other groups and quickly gain access to resources, opportunities, and information. Studies show having a diverse set of weak ties — particularly in a large community of uncaring strangers — increases life satisfaction.
Weak ties are not universally good for game developers.
Scope creep. The economic and political systems necessary to make very large groups function are often some of the most complex features in a game. To support weak ties in your game is to accept a certain level of scope creep.
Over emphasis on weak ties can hurt strong ties. Weak ties are also not a replacement for strong ties. Social groups involving mostly weak ties are poor at providing emotional support as well as transferring and enforcing group norms. Many critiques of strongly capitalist, technocratic or libertarian dystopias center on how a overreliance on weak ties (via large-scale trade, algorithmic replacement of reciprocation loops, and other scaleable-yet-dehumanized systems) leads to an accidental erosion of strong ties.
If anything, modern MMOs suffer from too many weak ties and not enough emphasis on building and supporting strong ties. Perhaps because MUDs and early online games were historically rich with strong bonds, MMO designers simply assumed they’d get those for free. They didn’t realize their desire to build a big game—which historically has been conflated with popularity—was antithetical to the magical social connections that made early online games attractive in the first place.
Shared goals for different group sizes
Shared goals are the single strongest predictor of group cohesion. Groups with more group pride and stronger task commitment have strong shared goals. They are most likely to perform well at high-trust tasks, and have high retention, longevity, and increased sense of member well-being.
Group pride and identity
Members with strong group pride feel strong allegiance to the group, are happy with what the group accomplishes, and promote the group identity to others. Group pride is expressed in the same fashion across different group sizes, but identity becomes more formalized as group size increases.
Weak identity. Small friend groups may not have an official identity, and many of their positive feelings come from mutual support.
Official identity. Large social groups have official identities and a strong sense of membership. When people are part of a high-performance group, they feel like they are part of something bigger than themselves, which can lead to a sense of awe.
Stereotype-based identities. At the huge impersonal scale, we see strong tribal identities and stereotypes. People build simple cartoon models of how other people should respond to interactions. Splitting people into in-group members and out-group members occurs relatively quickly, using only superficial information.
Task commitment is about shared activities that contribute to a common goal. Group pride answers, “Who are we and do I belong?” Task commitment, by contrast, answers, “What are we accomplishing by working together?”
Tactical tasks. Small secondary groups understand their purpose in terms of short term tactical tasks. This could be completing a small project or finishing an ad-hoc raid together. Small primary groups are usually focused on supporting one another.
Trying to sustain the group. Large social groups are focused on bigger topics like long-term survival or sustaining a community that upholds shared beliefs. Group vs group superiority is an interesting task at this scale, especially for groups composed largely of young men.
Part of an ecosystem. Huge impersonal groups are brought together by convenience. They share a common set of codified practices involving trade, language, and practices that help their smaller friend groups accomplish desired goals. The task commitment present at this level usually involves maintenance of support systems such as political or economic structures.
Tips for increasing shared goals
Share goals, not just shared rewards. Many game designers assume that if there is a shared reward, people will naturally align their activities. This might work if humans were hyper-rational, profit-maximizing automatons, but they are not. Instead, players benefit from clearly-stated goals and examples of how they might work together.
Public and private spaces. Large social groups are composed of sub-groups that require private space to reinforce vision and social norms as well as create opportunities for group bonding. They also need public space to display and reinforce the group’s overall identity.
Group vs. group content. Conflict with other groups is a common method of providing a shared purpose. Meaningful rivalries can play out over the course of months or years. Games with PvP content can create very rich social histories if they can operate at this scale.
Positive goals involving growth and support. Though it is easy to rely on competition in order to give your group a purpose, history is rich with high-longevity groups, usually in the form of religious communities, that exist to preserve a positive way of life. Consider how your game can be a positive refuge from the broader world. Many players will find this to be a worthy goal to dedicated their time toward.
Roles for different group sizes
Every group needs to agree on roles within society. These are composed of appropriate division of labor and division of resources.
Division of labor
Specialization increases with group size.
Overlapping roles. In small friend groups, there’s substantial overlap in roles, with a single individual performing many different activities on an as-needed basis. Cross-training and a lack of specialization is quite common. On high-trust tasks, there’s heavy interdependency and the loss of any individual is sorely felt by everyone.
Specialization. In large social groups, we start to see specialization where individuals take on specific roles. Secondary groups focused on specialized tasks are common and people belong to multiple of them. An individual may train in several roles and perform one role for each secondary group.
Jobs become identities. Huge impersonal groups see the emergence of jobs and classes. A person has one dominant job they do in a hyper-specialized economy, which becomes their formal identity within a broader, rule-based society. They are a crafter, or a teacher, or a doctor, and nothing else.
Division of resources
Economic complexity increases with group size.
Communal sharing. With small friend groups, resources are often communal in nature with substantial gifting and untracked sharing. Social currency and interpersonal trust are more important to transactions than currency.
Value-based barter. In large social groups more formalized 1-to-1 trade in the form of barter appears. There may be a local currency and individuals keep close track that each trade is of equitable value.
Complex economic networks. In huge impersonal groups, both labor and resources are traded within an economic network with markets and auctions. Trade is strongly depersonalized, with every interaction based off a standardized currency. This network is heavily dependent on weak ties and super-connectors—people who can maintain more than 150 meaningful connections—play an outsized role in keeping various sub-groups together.
Status relationships for different group sizes
Status and hierarchy start out relatively undefined in smaller groups and grow in complexity with group size.
Leaders become more important and less personal in larger groups.
Context-specific leader. Small friend groups often end up electing a de facto leader, consciously or subconsciously, who is best-suited for the task at hand. Groups this size should be encouraged to designate a leader/organizer who can help keep the members focused on their shared purpose.
Cult of personality. Large social groups require leaders. Synchronization of activity becomes immensely difficult without a central authority wrangling the various sub-groups to move in an aligned direction. At this size, leadership is largely a “cult of personality,” driven by personal relationships instead of institutional power.
Symbolic leaders. Huge impersonal groups use ceremonial leadership, where the leader is a concrete, personified symbol of shared purpose, allegiance, and/or cultural values. There are a variety of related techniques including the use of celebrity, figurehead leaders, religious characters, or heroic individuals. These establish a type of group known as a reference group, which individuals look to when determining which social norms to emulate.
Hierarchy becomes increasingly necessary as group size increases.
Fluid. Small friend group organization is fluid and often depends on the best person for the task at hand stepping up.
Activity related sub-groups. Large social groups show visible hierarchy composed of a few primary groups and a number of task-focused secondary groups. We begin to see multiple 5 and 15 person groups operating inside groups of this larger layer.
Complex hierarchies: Huge impersonal groups have complex official hierarchies. Groups need official political and economic relationships in order to function.
Tips for supporting status
Tools for status signaling. The ability to signal hierarchy and status help larger organizations function. Titles, karma points, and visual flare are all systems that allow status to be earned and displayed.
Official reputation tracking. For huge impersonal groups, we see the emergence of strong anonymity, reputation starts to be more important than actual skill competence and parity. At the 500 person layer, freeloaders and bad actors can more easily slip through the cracks, so official means of keeping tabs on someone’s reputation benefits group cohesion.
Social norm formation at different group sizes
Norm formation in social groups involves how a group determines the rules they operate by and how they communicate those rules.
Rule formation becomes increasingly formalized as group size increases.
Personally negotiated norms. Small friend groups negotiate rules on a one-to-one basis, usually through small group discussions. Often behavior is determined on a case-by-case basis depending on the person and the context.
Key decision makers. Large social groups follow the behavior of high-status individuals or leaders. One or more smaller, high-status groups make decision through consensus-building and then then share those decisions with lower-status individuals. In more equitable groups, simple voting systems appear.
Public rules. Huge impersonal groups use official legislative systems for setting or revising laws. They usually have formalized community feedback mechanisms. At this stage we see strong rule of law. In more organized groups, explicit rules become prevalent. By requiring that people work in a very specific, codified fashion, you remove uncertainty and increase the group’s ability to function. You’ve replaced the slow process of negotiating social norms, through in-person reciprocation loops, with rules that simply tell you how you should act. However, this comes with substantial downsides. These rules are inherently less flexible. They need to be written up, conveyed, and enforced. If the situation for such a group changes slightly, the existing rules may actually reduce efficiency. And there’s no real trust—all parties execute on a pattern and hope it works.
Communication shifts from reciprocation loops to broadcast as group size increases.
Personal conversation. Small friend group members will communicate frequently and in depth with all other members of the group. This communication is likely to be equally spread between all members and structured as peer communication.
Tiered communication channels. Large social groups have multiple tiered communication channels. There generally needs to be an open, shared channel; a one-way broadcasting channel from leadership; and a number of sub-group channels for specific primary and secondary groups.
Broadcast communication. In huge impersonal groups, personal communication simply cannot reach all the people in the many sub-groups, so these groups must use broadcast technologies to send one message to many people cheaply. This, in turn, enables propaganda, where various parties use broadcast media to push unquestioned messages that promote new truths. There’s no consent loop for someone to provide a contradictory response. This is useful for spreading new social norms about how one should behave or for emphasizing group bonds, but cartoon symbols of complex systems end up being easier to spread than deep understanding.
Conflicts and sanctions at different group sizes
What happens when norms are violated?
Small friend group
Personal disagreements. Small groups are constantly negotiating norms. Norm violations are typically confronted in small group conversation, one-on-one or with the whole group, and are essentially arguments.
Withdrawal from the group. In extreme cases, members of small groups will stop talking to someone with whom they have a personal disagreement, or the group will distance themselves from an individual by lowering the frequency of interaction with them.
Large social group
Cliques and bullies. Groups this size can form into abusive groups of bullies. Designing for groups this size means added community management. Groups of this size rely on vision-based leadership, which can allow hate groups and other fear-based organizations to fester.
Huge impersonal group
Demonizing outgroups. It is common for very large groups to explicitly label those who are enemies of the tribe. This is less about attacking the outgroup and more about focusing the larger group on a larger shared goal. The downside to this is it usually relies on fear, which short-circuits more thoughtful and constructive group coordination patterns.
Law enforcement. Tracking down those who break the laws and determining the best way to change their behavior is a feature of very large groups.
Economic scams. Groups emerge that profit from preying on people who want to get an economic edge. Various black market scams start to be common, like account and credit card theft. Outside groups target the community.
Organized griefing. Though individual griefers exist in smaller groups, within a larger population, a griefing tribe can satisfy all of an individual’s social needs. Griefing becomes the social norm for such people and there’s no leverage at any point in the network to deprogram a griefer. This can lead to all-out wars, where one group attempts to destroy, alienate, or otherwise expel a rival group.
Account manipulation. With a large number of strangers, it’s hard to track who is coming or going. In online games, people create extra accounts and use them for botting, muling, multi-boxing, and other techniques that are otherwise easily trackable in more intimate settings.
Internal corruption. As with nations, if a game becomes big enough, it is easy for moderators and community management to surreptitiously misuse their powers.
Game Design Insights
Considering the constraints imposed by friendships, Dunbar’s Layers, and social groups, it is worth exploring game design that is centered around natural human social scales. Human-scale design is social design that targets the 5, 15, 50 and 150 person egocentric networks and associated groups. It explicitly avoids player systems involving 500 or more players.
If you can build a human-scale game that enables a player to spend quality time with good friends, you’ll likely improve the quality of their life. While if you break these hard limits, you actively damage your game’s social systems. These social psychology models should do more than just inform our evaluation of game systems—they should be actively shaping the way we approach design.
Such an approach focuses on smaller, more intimate social design as the core of a game. It is less concerned with big numbers and infinitely scalable systems, and more interested in fostering trust and connection between players. This perspective led us to some fundamental insights concerning how we approach online game design.
Don’t build a big world first
A common pattern when designing an MMO is:
First, imagine a big world
Then, figure out what to fill it with
Finally, create all the systems necessary to support all the stuff you’ve dreamed up
As a result, the final systems are often surprisingly complex. You’ve jumped directly into designing systems that need to handle the many issues associated with 500+ groups (i.e., your player population). Immediately, you are faced with the key problem that your world is just a large, empty area where a player sporadically meets strangers they don’t trust. As conflict inevitably arises from these low-trust interactions, the dev team toils to add a vast amount of bureaucracy to manage the poor player experience. It can feel like patching unending leaks in a poorly-placed dam.
In the best cases, like EVE Online, players create their own systems of crude governance to shore up the faulty social design. But for the majority of games, we see outcomes like The Sims Online, where mob-style groups grief new players and chase them from the game.
From a social design perspective, this process sets the team up with the hardest possible design challenges, essentially creating a lot of extra problems that then need to be solved. Focusing on designing for human-scale suggests a different approach:
Define social activities. First, imagine activities/content/context for players to enjoy together. For example, you might prototype cooperative raid mechanics for a PvE MMO.
Map out group sizes and trust level. Then, figure out what group sizes and levels of friendship best fit those activities. High-trust activities should be reserved for small groups of close friends in the 5, 15 and 50 layers. Low-trust activities can work for groups up to 150 in size, but not beyond. In fact, explicitly remove activities that involve more than 150 people. For our MMO example, you take your raid prototype and map out variations of the raid that are suited for high-trust small groups, low-trust small groups, and low-trust large groups.
Build appropriate social support systems. Build systems that support the right activity for the right group size. With the MMO, you realize that your high-trust small group encounter needs high-bandwidth communication channels to execute, so you add voice chat or rich emotes—two possible communication channels that support and enable group performance.
Scale the activity based off quality and quantity of friends available. Finally, organize the activities/content so that player groups can organically scale up and down. In the MMO example, a single player might be present and you’ll want to serve them up low-dependency, small-group content. But if a stranger appears, consider how the game might switch to a low-trust activity with parallel play? If several friends appear, how would the activity allow them to opt-in to a higher-trust (and higher-reward!) challenge?
This approach has the advantage of more closely mapping to how humans have grouped historically: in nested layers of families, tribes, villages, etc. Sticking closer to the natural shape of social grouping will make your group activities feel more familiar and facilitate social bonding. It will also allow you to apply lessons and best practices from psychology and anthropology more directly.
Social design drives retention and engagement
When game designers think of retention, we often first consider User Experience (UX). Using the logic of UX, if a developer builds a complicated core interaction that is difficult for a player to understand, most players will churn out early on. Such games should have poor early retention and struggle with new player acquisition.
However, the game industry has many counterexamples. Dwarf Fortress, Go Pets, and Dofus are three games renowned for their poor user experiences. They have weak tutorials, byzantine gameplay loops, and a general lack of traditional first-time user experience polish. By all traditional UX values, they should be failures, yet they are not.
While these games have poor UX, they also have strong social design. For example, Dofus is a game that is specifically popular in France. Its developers tried to expand its reach to other countries with limited success, for many of the aforementioned reasons.
What made France special for Dofus?
Cultural event. When Dofus first launched, French-language MMOs were rare and early adopters were blown away by the novel experience.
Basic virality failed. Players actively proselytized the game to friends, but their friends weren’t able to play as the game was too difficult to learn.
High-cost transmission to close friends. So players went over to their friends’ houses, helped them install the game, and spent hours teaching them, in person, the nuances of how to play.
This was not intentional, but the result was that Dofus ended up being played predominantly by friends, many of whom were already part of each others’ 50, 15, and 5 person layers. This allowed players to build groups stocked up with high-trust compatriots and overcome the high-trust activities in the game. Succeeding at those challenging activities in groups of trusted friends gave the game incredibly high engagement.
A virtuous cycle occurs where strongly-bonded friends make a game their homebase—a safe, intimate space for acting out their friendship. In turn, those players recruit more of their friend networks into the game.
We’ve observed a similar process in other poor-UX, high-retention game examples. To be clear—poor UX is not the root driver for these games’ avid, high-trust communities. Instead, it is one of many pragmatic reasons for players to bring the inner circles of their friend networks into a game.
The reverse of the same basic process that drove the success of Dofus highlights problems with early Facebook-style virality. Such “social network games” would obsessively incentivize players to send out invites to as many people as possible. Two results occurred:
Mismatched reciprocation loop costs. Incentivized by these games, players made a low-cost overture to a friend or associate (an invitation) that required a high-cost response (registering for, and playing, the game). This is a huge no-no when acting out reciprocation loops; it actively damages a relationship by suggesting you are ready to extract value from your friend instead of building toward future shared need. That is to say, it annoys your friends and makes them question the value your relationship.
Dilution of community trust. Second, it brings low-trust people into the game. Because Facebook didn’t care about Dunbar’s Layers, especially in the early days of the social gaming boom, many users had social graphs with hundreds of “friends,” many of whom were no better than strangers. The low-cost overture to join was little better than a random spam ad and brought many of those random players into the game, diluting the level of trust for the community already inside the game. In one fell swoop, this greedy practice hurt retention, engagement, and future growth.
All of these examples highlight the basic truth that social design is deeply powerful, but is often not a first-order consideration for designers.
Use proper terminology
A very common confusion that came up many times during our discussions was the difference between friends, Dunbar’s Layers, group size, and concurrency (the number of players simultaneously logged in). These are all four distinctly different concepts, yet it is common for social designers to use them interchangeably.
Much of this is the fault of our existing terminology. When we talk about multiplayer games, a common shorthand is to say, “It’s a 16-player game.” We all know that this means there are 16 concurrent players in a match or room, but we often erroneously assume that this also means they are all friends and/or that they are all part of the same social group.
Both of these errors are a naive misunderstanding.
Concurrent players can be spread across multiple types of social groups. Some of them might be members of groups that are antagonistic to other sub-groups in the game. Some are members of multiple groups. Some form small sub-groups while others form large sub-groups.
They can have a mix of friendship bonds. Some of them may be friends. Most are likely total strangers.
In general, having 16 people online together says almost nothing about whether or not they are in a group, or what the strength of their relationships might be. It is tempting to fall back on old, inexact language, but your game will suffer. Instead, teach your design teams about friendship formation, constraints on types of friendship, trade-offs involved at different groups sizes, and the logistics of social play.
Use Dunbar’s Layers to determine the level of collaboration your audience will support
The structure of Dunbar’s Layers gives us insight into how many friends of a given trust level you can expect a player to have online at any particular time. There are logistical implications for matchmaking, events, and more.
At the most basic level, the logistics of Dunbar’s Layers help you predict the outcome of the following example:
You design a high-trust activity that requires 100 people.
But we know from Dunbar’s Layers that any human being will only have a maximum of 15 people in their life that have this particular level of trust.
You’ve created a logistics mismatch that will results in inevitable failure. And you didn’t even have to build the game, launch it into the market, and watch it fail. You just saved your team millions of dollars and years of their life!
However, we can gain more detailed insights. Here’s how you calculate the exact portion of a player’s friend graph you can actually address with your game. First, you’ll need a few pieces of information:
Share of social time
Distribution of friends
Share of Social Time
Share of Social Time is the percentage of a player’s total time spent socializing that is spent inside your game. This corresponds roughly to the percentage of a player’s social graph that is active in the game. If a player spends 50% of their social time in a game, we’d expect roughly 50% of their friend network is also in the game.
There are a couple ways of calculating this. Conservatively, we know from time-usage studies that the average American has approximately 5 hours of leisure time per day . From this perspective, Share of Social Time equals Hours per Day Spent In Game / 5 hours.
However, less conservatively, we know that people tend to spend approximately 0.65 hours per day actually socializing. This is likely an underestimate since the time-usage studies don’t measure time spent socializing at work. Nor do they consider time spent in playing games  as socializing.
For the following calculations, we’ll use the conservative definition of Share of Social Time. For comparison, the heaviest players of Fortnite, around 8% of the player population, spend 3+ hours playing per day. That’s roughly 60% (or more) of an average American’s total leisure time.
Concurrency ratio is the ratio of monthly active players (MAU) to those currently online. Since synchronous activities require people to be present, it does us no good if you have friends in a game, but they aren’t actually playing.
A highly-social MMO will have a concurrency ratio of 10:1, so for every 10 MAU you’ll have 1 of those players online. An international phenomenon like Fortnite enjoys a 20:1 ratio, while many web-games are as low as 150:1 or 250:1.
Distribution of friends
Dunbar’s Layers suggests that our relationships map onto a very specific frequency distribution of friends.
Chart 1: Percentage of friend network layers present in the game
This distribution holds true only if we make several assumptions:
Long-term engagement. First, our game is a long-term activity which has been going on long enough that inner layers like intimate friends or best friends have grown in the game or have integrated pre-existing, external friendships. If a game is new or people have been playing for less than 200 total hours , you’ll see this distribution shift towards casual friends and strangers.
Sufficiently large cohort. The total population of monthly active players is at least 1500.
Support for all layers. If your game doesn’t have all appropriate social mechanisms for any given layer—such as the need at the 5 person layer for private locations/communication to facilitate safe disclosure—that layer will be less represented.
We can use Share of Social Time, Concurrency, and distribution of friends to calculate some useful information about our game.
Let’s say you have a highly engaging MMO:
Share of Social Time: 50%
Currency ratio: 10:1
How many friends will be in the player’s friend list? Given the standard distribution of friends, 50% of that player’s social network will be present in your game. With a total of 150 friends that means there will be 75 friends playing the game.
How many friends will be online right now? Of those 75 friends in the game, due to the concurrency ratio, only 10% (7.5 friends) will be on at any point in time, on average.
What type of friends will be online right now? Using the distribution of friends in various layers from the chart above and multiplying them by the total friends online, we can expect the following distribution of friends:
Casual friends: 5
Good friends: 1.8
Best friends: 0.5
Intimate friends: 0.3
This sort of calculation puts much harder constraints on the types of activities that we can build into our game. Note that this is a best-case scenario. A highly-social MMO with great concurrency, and a player with a fully-engaged friend network. In this best-case situation you are lucky to get a single good friend playing alongside you. You will however get a few casual friends.
This suggests that the core activity of even highly-social games with long-term, highly-invested players should predominantly be target low-to-moderate trust activities involving 5-7 players.
What does this distribution look like at different cohort sizes? Using the same logic, you can see what friend distributions would look like at various fixed populations of active players.
Chart 2: Max and Average number of friends an individual will have for various cohort sizes in a game with 50% share of social time and 10:1 concurrency.
Due to the logistics of concurrency ratios and Share of Social Time, we max out the number of friends online at around 1500 people in a cohort. Simply having bigger cohorts doesn’t improve friend concurrency.
How can we improve these numbers?
The previous calculations are just an average of the sort of friends you can expect online. By shiftings a few variables around, we can create much higher densities of friends.
Events. A timed event or a scheduled boss raid spikes the number of people online and can dramatically reduce the concurrency ratio. If you can drop the concurrency ratio to 2:1 with an event, then you have upwards of 12 friends and 4 good friends playing. This shift is one reason why events like boss raids can be high-trust events.
Asynchronous Activities. Activities that people can do when others are offline allow for more people to be involved. Some asynchronous activities can reduce the concurrency ratio to the equivalent of 1. These systems have the downside of dramatically slowing down reciprocation loops by reducing communication bandwidth, so building out a full friendship network may take longer for players.
Recruitment. Given the low engagement of the innermost friendship layers due to simple logistics, it is unwise to rely on close friends naively playing the game together. Invest in systems that actively encourage players to play with loved ones. Give them tools for scheduling these activities.
Relationship design as systems design
By translating fuzzy social psychology concepts into more mechanical concepts, we can start treating social design as a form of systems design. (Some may find the term ‘social systems design’ more palatable than ‘social game design’ after dealing with the horrors of Facebook.)
In particular, social design benefits from using the internal economy perspective, where relationships are modeled as resources and transformations on those resources.
Each relationship between two individuals is a pool. A pool is a container that accumulates resource tokens.
Successfully completed reciprocation loops is a source that produces a resource called social capital that accumulates in each relationship pool.
Rejected or unequal reciprocation loops are a sink that depletes social capital. As does distance and lack of contact over time.
Dunbar’s Layers act as a cap on the maximum number of each level of relationship you might have. When a relationship pool fills up in one of the outer layers, it may transform into a new pool in one of the inner layers. However if the inner layers are full, one must give. If any of the layers are empty, the player seeks actions that fill them.
This paints the process as rather cold and transactional. In practice, this type of design drives intense emotions. Losses of social capital yield strong negative emotions, while gains generate positive emotions. Rate of lose or gain will dramatically intensify the emotional response. If your goal is to make players laugh, cry, or otherwise experience the peak of what it means to be human, build strong social systems.
Minimize designs that require huge impersonal groups
When we develop a game that involves group sizes of 500 and 1500 people, we’ve created populations beyond the human brain’s ability to understand other people through personal relationships. Our players know nothing about most other individuals, as they are incapable of building a large-enough social network to understand the whole. Instead, they must rely heavily on rules and heuristics to govern their interactions, and we, as game designers, are on on the hook to provide those structures.
By simply upping the size of our community, we’ve introduced an immense design challenge. We now need to build systems to manage crime, corruption, economic complexity, classism, racism, and more. Suddenly, our games exhibit most of the ills of modern society and the burden is fully upon us to solve them. If we don’t conscientiously address these issues, our community collapses into a hellish online dystopia.
If you care about maximizing social impact while minimizing scope:
Consider building communities of 50–150 players. This will maximally leverage strong bonds for retention and engagement.
Use instancing to ensure that your game can support a massive population even though each community is self-contained. Games like Minecraft; Don’t Starve Together; old, instanced MUDs; and numerous other small community games suggest this strategy can be both financially successful and fulfill social design goals.
If you want to create larger communities, try limiting yourself to cohorts of 500–1500. There are no other systems larger than these values that are meaningful on a relationship level, and by creating larger populations, you dilute and harm existing social bonds.
When creating groups of 500–1500, leverage your instanced groups of 50 and 150. Create a few low-scope systems that allow weak ties between strongly-bonded friend groups. Trade networks and information exchange will be among the highest-value systems to invest in.
Opportunity: Serving Player Motivations
Games that thrive are almost always ones that satisfy a strong audience motivation. This is no different for social games and social features. Dunbar’s Layers, in particular, give us a structure for understanding the player’s social motivations.
The Belongingness motivation
“The belongingness hypothesis proposes two main features. First, people need constant, positive, personal interactions with other people. Second, people need to know that their bond is stable, there is mutual concern, and that this attachment will continue.”
You can think of the various relationship layers as a slots in a list. Everyone has space for about 5 intimate friends, 10 best friends, 35 goods friends and 100 casual friends. If those slots are filled with healthy, mutually-beneficial relationships, a person is reasonably happy.
However if any of those slots are empty, people have a strong desire to fill them in. When they don’t have those slots filled they tend to be unhappy, and, in response, will seek the company of others using several key strategies:
Deepen bonds with existing friends. This is done in order to fill inner layers of the friendship network.
Meet new people. This is done in order to fill outer layers.
Become a member of a group. Often belongingness will be combined with a desire for affiliation. By becoming part of a social group, it becomes substantially easier to both meet new people and quickly deepen friendships. Think of group membership as a bonding multiplier. It is easy to get caught up in group affiliation as an end, by itself, but remember that, ultimately, people join groups not for the sake of the group, but to fill gaps in their primary friend network.
The desire to form relationships waxes and wanes
Life events are predictive of gaps in a person’s friendship network. As new people show up in a person’s life, there’s less time for activities that require making new friends.
Entering a new intimate relationship or marriage. This fills an inner-layer slot. There’s also the inevitable shifting and merging of your two friend groups.
Having a child. This also fills an inner-layer slot. All that time spent in parenting groups often shifts friendships from your single friends over to other parents with kids just like you.
Getting a new job. This can fill any number of slots in several layers as you form new work relationships.
What loneliness looks like in a thinned-out network
There are also numerous events that thin out a person’s network.
Becoming unemployed. You lose work relationships.
Retiring. This is similar to becoming unemployed, but often you lose professional associations as well.
Breaking-up or divorce. One of the more intense losses of an inner, highly-intimate bond. As well as a weakening of all the shared relationships (closed triadic relationships in your networks).
Moving. Shifting many high-intimacy friends into outer layers. Can break existing friendships and free up slots. Research suggests it generally doesn’t destroy intimate family bonds.
Kids moving out. When kids go off to college, most parents end up losing key members of their inner circle.
Becoming elderly. There’s a slow erosion of existing friend networks as people move or die. Elderly are also are less mobile and thus struggle to meet new people.
In particular, there seem to be three major periods in which loneliness spikes: Late 20s, mid 50s and late 80s. During these times one study reported as many as 75% of people report being lonely. These values hold across genders. Providing these individuals with tools for building healthy relationships would be immensely beneficial to society.
Two social game design opportunities
All of this suggests opportunities for social game design to improve the lives of our players.
Games for friends. High-trust games should target those with free time and strong, existing friend networks. The design focus is on bringing those friends into the game.
Games that help make friends: Games that deliberately try to convert strangers into better friends should target groups that have gaps in their social network. For example, one demographic might be lonely 50-65 year old men who are seeing an erosion of their social network due to unemployment, kids moving out, and fewer opportunities to find new friends. Make a game that is the modern version of a Masonic Lodge.
Both opportunities could be served by the same game, but be sure to sort incoming players based on their needs and direct them into activities that satisfy those identified needs.
The big idea
Key discoveries in social psychology place hard limits on the types of social games we can build.
Friendship research shows meaningful in-game relationships require conditions such as proximity, similarity, reciprocity, and disclosure
Dunbar’s Layers research shows that players have hard limits on the number of meaningful relationships in their life. These friendship are organized into layers of increasing size and decreasing intimacy.
Social group research shows the need for increasingly complex support structure as group size grows
These are the physics that social designers must understand and build into their designs.
Many past designs ignored Dunbar’s Layers and naively assumed “more is better.” They ignore friendship formation and assume “it just happens.” They ignore social groups and arbitrarily mash players together.
In reality, these assumptions are actively harmful and cause the following:
Fewer in-game friendships. A flood of strangers swamp the reciprocation and proximity mechanisms that generate friends. Poor identity, persistence, reciprocity, and consent systems mean these strangers never convert into friends, so there are fewer meaningful relationships in the game.
Increased toxicity. Large groups of strangers naturally breed toxic sub-groups. Players engage in violent rejection of out-groups in order to protect their experience and intergroup conflict becomes the cultural norm. Such communities are hard to reform and poison long-term retention.
Scope creep. The additional systems necessary to manage large groups of strangers substantially increase the scope of your game.
What players need
If players have not filled all the slots in their primary friend network, they suffer. And, in response, they are intrinsically motivated to deepen their existing relationships or build relationships with new people. Striving for belongingness is one of the strongest human motivations. They will naturally seek out activities that help them make friends and belong to something bigger than themselves.
If your games help build relationships for the player in any of their inner layers, you’ll accomplish a couple key benefits:
Increase retention and engagement. Your game becomes the place where people attain their desires. Since you provide immense value, they make the game a key part of their lives.
Improve the lives of your players. They’ll experience less depression, better health, and have more robustness in the face of negative life events.
If we take all the insights gleaned from research into group psychology, examples from online game design, examination of Dunbar’s Layers and social motivation—all of it into consideration, we can arrive at several, strong best practices:
Build games for smaller cohorts. The base activities should target small, collaborative groups. Large groups of close friends are rare or, in many cases, mathematically impossible.
Cluster players into persistent, high-density cohorts. So they have repeat interactions with the same players. The more reciprocation loops that are completed, the stronger the friendships. Big, empty spaces are not a positive feature.
Encourage high-concurrency events or asynchronous activities. Logistics favor players being around to interact with their friends. Having friends playing the same game doesn’t matter if you never see them.
Aim for long-term engagement. Build a game where players are engaged for hundreds of hours, so they have enough time to build deeper friendships. It takes at least 50 hours of interactions to form a basic friendship.
Attract existing friends, if possible. Existing friends from the strongest foundation for your game community, especially when first launching your game. Put people into safe, guild-like structures and encourage them to bring in their friends.
Design for climbing the trust spectrum. When introducing strangers into your game, build low-trust activities that scale into high-trust activities. Start with parallel or single-player gameplay and allow players to opt-in to higher-dependency activities. If players start forming strong friendships in game, support them. Bring those relationships into safe places with tools for enabling consent, support, and disclosure.
As ethical game designers, we should strive towards some higher purpose beyond merely extracting money, time, and energy from our players. Building friendships and providing lonely people with human connections are goals worthy of our highest-quality work.
If you are working on a multiplayer game, ask yourself how your designs help build social capital with and among your players. If you encounter people who believe that “more is better” when it comes to building social systems, we recommend you send them this report. There’s a new wave of social game design inspired by lessons from social psychology and we are immensely excited to be part of it.
 Dunbar’s Layers. “Generally speaking, humans each have one to two special friends, five intimate friends, 15 best friends, 50 good friends, 150 “just” friends and 500 acquaintances. Our relationships form a series of expanding circles of increasing size and decreasing intensity and quality of the relationship.”
Woodward A (2017) With a Little Help from My Friends. Scientific American. Retrieved December 27, 2018, from https://www.scientificamerican.com/article/with-a-little-help-from-my-friends/
 Dunbar’s Number. “The figure of 150 seems to represent the maximum number of individuals with whom we can have a genuinely social relationship, the kind of relationship that goes with knowing who they are and how they relate to us. Putting it another way, it’s the number of people you would not feel embarrassed about joining uninvited for a drink if you happened to bump into them in a bar.”
Dunbar R (1998) Of Brains and Groups and Evolution. In Grooming, Gossip, and the Evolution of Language (pp. 80-105). Retrieved December 26, 2018, from https://books.google.com.ar/books?id=nN5DFNT-6ToC&pg=PA77&redir_esc=y
 Unequal dyadic bonds. “When analyzing self-reported relationship surveys from several experiments, we find that the vast majority of friendships are expected to be reciprocal, while in reality, only about half of them are indeed reciprocal.”
Almaatouq A, Radaelli L, Pentland A, Shmueli E (2016) Are You Your Friends’ Friend? Poor Perception of Friendship Ties Limits the Ability to Promote Behavioral Change. PLoS ONE 11(3): e0151588. http://dx.plos.org/10.1371/journal.pone.0151588
 Loneliness impacts longevity. “…individuals with adequate social relationships have a 50% greater likelihood of survival compared to those with poor or insufficient social relationships. The magnitude of this effect is comparable with quitting smoking and it exceeds many well-known risk factors for mortality (e.g., obesity, physical inactivity).”
Holt-Lunstad J, Smith T, Layton J (2010) Social Relationships and Mortality Risk: A Meta-analytic Review. PLoS Med 7(7): e1000316. https://dx.plos.org/10.1371/journal.pmed.1000316
 Friendship impacts life satisfaction. “…the results indicate that both having/meeting friends and good-quality friendship relations are important to an overall life satisfaction.”
Amati V, Meggiolaro S, Rivellini G, Zaccarin S (2018) Social relations and life satisfaction: the role of friends. Genus, 74(1), 7. https://genus.springeropen.com/articles/10.1186/s41118-018-0032-z
 Friendship reduces depression. “People who have close friends and confidants, friendly neighbors and supportive co-workers are less likely to experience sadness, loneliness, low self-esteem and problems with eating and sleeping. Indeed, a common finding from research on the correlates of life satisfaction is that subjective well-being is best predicted by the breadth and depth of one’s social connections.”
Helliwell J, Putnam R (2004) The social context of well-being. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 359(1449). https://royalsocietypublishing.org/doi/10.1098/rstb.2004.1522
 Social media doesn’t expand our friendship capacity. “The fact that social networks remain about the same size despite the communication opportunities provided by social media suggests that the constraints that limit face-to-face networks are not fully circumvented by online environments. Instead, it seems that online social networks remain subject to the same cognitive demands of maintaining relationships that limit offline friendships.”
Dunbar R (2016) Do online social media cut through the constraints that limit the size of offline social networks? Royal Society Open Science, 3(1). https://royalsocietypublishing.org/doi/10.1098/rsos.150292
 Intimate relationships best predict health. “…the presence of an intimate relationship (as opposed to a broader social network) [has] the greatest effect on explaining variance in depressed mood.”
Roberts S, Arrow H, Gowlett J, Lehmann J, Dunbar R (2014) Close Social Relationships: An Evolutionary Perspective. In R Dunbar, C Gamble, J Gowlett (Eds.), Lucy to Language: The Benchmark Papers (pp. 151-180). Oxford: Oxford University Press.
 How investment shapes social graph distribution. “the strength of a tie is a (probably linear) combination of the amount of time, the emotional intensity, the intimacy (mutual confiding) and the reciprocal services which characterize the tie”
Granovetter M (1973) The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360-1380. Retrieved December 28, 2018 from https://www.jstor.org/stable/2776392
 Available leisure time. The average American woman spends roughly five hours per day on leisure activities (35 hours per week), while the average American man spends about 5.5 hours per day (38.5 hours per week).
Bureau of Labor Statistics, U.S. Department of Labor (2018) American Time Use Survey — 2017 Results. Press Release for the Bureau of Labor Statistics. Retrieved December 20, 2018, from https://www.bls.gov/news.release/pdf/atus.pdf
 Why are people socializing in games? “On the face of it, this may seem like a sad state of affairs. It could even be read as dystopian: people are escaping real life to be in virtual worlds. People often find community within gaming worlds, and may get a heightened sense of shared experience from competing against or teaming up with people across the world who share their interests. In some cases, these connections might even be more valuable than, say, gossiping with a neighbor.”
Kopf D (2018) Americans are socializing less and playing more games. Quartz. Retrieved December 28, 2018, from https://qz.com/1320344/americans-are-socializing-less-and-playing-more-games/
Depp C, Palmer B, Glorioso D, Daly R, Liu J, Tu X, Kim H, Tarr P, Yamada Y (2018) Serious loneliness spans the adult lifespan but there is a silver lining: Feeling alone linked to psychological and physical ills, but wisdom may be a protective factor [Press release]. Eureka Alert. Retrieved December 28, 2018, from https://www.eurekalert.org/pub_releases/2018-12/uoc–sls121218.php
Williams, D. (2007). The impact of time online: Social capital and cyberbalkanization. CyberPsychology & Behavior, 10(3), 398–406.
Coziness is a common aesthetic in popular games such as Animal Crossing or Stardew Valley, yet it rarely discussed within design circles. Our group of designers did a deep dive to understand:
What is ‘Cozy’?
How do we make our games more cozy?
What we found during our exploration:
Coziness is an ingredient that can applied to a wide variety of both casual and core genres.
Coziness can help your game appeal to broader audiences.
Coziness helps retention by giving players control over pacing while still maintaining engagement during periods of rest.
Coziness is a subversively humanizing design practice in a society built on monetizing base animal needs.
1. What is Cozy?
Definition of Coziness
Coziness itself refers to how strongly a game evokes the fantasy of safety, abundance, and softness.
Safety: A cozy game has an absence of danger and risk. In a cozy game, nothing is high-risk, and there is no impending loss or threat. Familiarity, reliability, and one’s ability to be vulnerable and expressive without negative ramification all augment the feeling of safety. To maximize safety, activities should be voluntary and opt-in so that players never feel the threat of coercion.
Abundance: A cozy game has a sense of abundance. Lower level Maslow needs (food, shelter) are met or being met, providing space to work on higher needs (deeper relationships, appreciation of beauty, self actualization, nurturing, belonging). Nothing is lacking, pressing or imminent.
Softness: Cozy games use strong aesthetic signals that tell players they are in a low stress environment full of abundance and safety. These are gentle and comforting stimulus, where players have a lower state of arousal but can still be highly engaged and present. There’s often an intimacy of space and emotion, with a slower tempo pace and manageable scope (spatially, emotionally, and otherwise). Soft stimuli implies authenticity, sincerity, and humanity.
Two models helps us understand how coziness yields meaningful gameplay.
First, we see play as a form of safe practice: People play because it allows them to experiment with a particular set of skills and activities that would otherwise be expensive or impossible in the real world. The opportunity to fight off attackers might not exist in a person’s day-to-day desk job, but a game lets them practice those skills safely and easily.
Second, we see games as a means of satisfying unmet needs: The human animal is motivated to fulfill various needs. For example, when we get hungry, we are motivated to find food and eat. Players seek to fulfill their emotional and psychological needs within games. Each game genre taps into a highly specific set of motivations. For example a survival game such as ‘Don’t Starve’ is very upfront about the fact that it, mechanically and thematically, let’s player explore the planning and tactical issues around getting food.
This ties back into play as practice. In Don’t Starve, you obviously are not being rewarded with actual food. But we are still immensely motivated to practice that associated skills if we are subconsciously worried about survival.
So as a designer, it is incredibly important to understand what motivations your players are pursuing and how your game design helps them practice mastery related to their needs. This design process is at the heart of making an engaging game.
Cozy games help player practice fulfilling higher order needs: Cozy games also fulfill player needs. However, unlike a game like Don’t Starve which focuses on base needs like starvation, cozy games creates spaces for higher order needs like mastery, self-reflection and connectedness.
Consider Maslow’s Hierarchy of Needs. At the bottom are pressing needs like thirst, hunger and safety. When these are present, they immediately grab the limited attention of the player and deprioritize those higher order needs. It is impossible to have a quiet conversation on a difficult subject while being attacked by a bear.
Maslow’s hierarchy of needs as it relates to coziness
Cozy games give players space to deal with emotional and social maintenance and growth. Players don’t need to worry about the high stress, immediate trials of mere survival and can instead put their attention towards the delicate work of becoming a better person.
Covey’s Time Management Grid, 7 Habits of Highly Effective People.
We can think of this also from the perspective of time and attention management. In Covey’s time management, tasks can be categorized along two axis: Urgent to Not Urgent and Important to Not Important. When people manage their time, there’s a natural tendency (in alignment with Maslow’s Hierarchy) to focus on Urgent tasks. Games in particular excel at filling the player’s time with Urgent but not very Important activities. Cozy games are designed to focus the player on Not Urgent yet Important tasks that are unfortunately deprioritized.
Admittedly, Maslow’s Hierarchy of Needs and Covey’s Time Management Grid are older motivational models, but the same key insight can be recontextualized in terms of the newer Self Determination Theory (SDT). SDT proposes that people thrive when they are able to pursue intrinsic motivations such as Autonomy, Competence and Relatedness. They stop thriving when they are presented with extrinsic motivators that suck up their attention. Not surprisingly, many of the factors identified by Maslow and Covey are powerful extrinsic motivators that disrupt a player’s healthy prioritization of needs.
The process of negating coziness: Because many common game mechanics are derived from satisfying lower order needs, it is very easy to accidentally disrupt the player’s feeling of coziness. If a system brings about a strong lower order need, the player’s attention will immediately shift to deal with the more pressing issue. High priority, low order needs get dealt with first; that’s just how humans prioritize. When this happens, coziness evaporates.
Factors that negate coziness include:
Extrinsic reward: Almost any form of extrinsic reward generates a pressing transactional short-term need.
Danger, fear, threat: Any sense of impending danger triggers biological responses in the player. Their sympathetic nervous system kicks, adrenaline floods the body, and memory suffers. Often times, cozy spaces are presented as reprieve or refuge from these dangers.
Responsibility: Responsibility requires emotional labor: the effort to plan, think, and execute on a plan to resolve something. Being responsible generates high priority need/expectation. Examples include any form of mandatory maintenance, needy pets, companions, or entities that require constant, non-optional care.
Unpleasant distractions: Distractions such as notifications, sudden noises or nagging remove a player’s autonomy over their focus. Their agency around being able to explore and appreciate a game in their own way is lost. Distractions also demand attention, generating a need.
Intense stimulus: Anything sudden, disproportionately bright or loud, or invasive/proximal can diminish the feeling of coziness.
Distance: Vast spaces eliminate a sense of safety by being unknowable. However, it is still possible to create very subtle or natural thresholds that establish a cozy space within the context of something broad: like a campfire in the middle of a wood.
Phobia sources: Anything commonly associated with a phobia, such as spiders, guns, or knives, can suggest harm or threat. This can be mitigated by context: for instance, the presence of a knife in order to cook or perform other nurturing activities, especially if it cannot be used for violence.
Non-consensual social presence: Anything non-consensual removes a player’s feeling of safety, but this is especially relevant in social situations. An uninvited presence can feel threatening, or just suggest an unsought expectation of interaction, reciprocation, or responsibility.
Confinement: Many small spaces are considered cozy since you can quickly inspect them to see if you are safe. But, this sort of coziness requires choice, and in turn inescapable small spaces can instead be seen as claustrophobic and controlling. A prison cell is generally not cozy.
Deception, betrayal, lies, insincerity: These forms of social masking create doubt and apprehension about social interaction, turning them from fulfilling and need-satisfying experiences into threatening and dangerous ones.
Opulence, pretentiousness, “fanciness”: Most cozy spaces veer somewhat more mundane than pretentious or opulent. On the one hand, fanciness can often create social comparison pressure, or come off as insincere, diminishing social safety. On the other hand, most opulence lacks the familiarity that often contributes to a feeling of safety.
An example of negating coziness: Consider the omnipresent pop-up, especially as it is used in a normally cozy franchise like Animal Crossing: Pocket Camp. When a notification intrudes on your gaming experience, it uses intense stimuli (noise, vibration, movement, colors) to generate a need that must be dealt with. You have a new responsibility to deal with the message by either investigating it further or manually dismissing it. It is almost always non-consensual since you never explicitly agreed to have your life interrupted by that pop-up in this particular moment. The theoretical opt-in that occurs at the systems level is more rote than intentional. If unwanted, a notification becomes a distraction. In order to extrinsically motivate the user to act as desired, notifications often use the promise of rewards, the threat of a lost opportunity, and marketing spin to deceive the user into interacting.
It is absolutely possible to design consensual notifications that provide cozy value to the player, but most do not and will slowly poison a cozy atmosphere.
Using contrast to enhance coziness: These same negating elements can be used to enhance coziness if they are safely outside the player’s defined cozy space (spatial, emotional, etc) by providing contrast and juxtaposition. For example, cold rain against a window emphasizes the warmth of a reading nook without threatening to disrupt it. If that same cold rain was blowing through a broken window, the scene would no longer be cozy.
Cozy-Adjacent: Overlapping But Not-Cozy
Coziness overlaps with several different aesthetics and themes, but has a unique identity separate from the following:
Cute: While cuteness resonates with the safety aspect of coziness, as well as the desire to nurture/satisfy needs, many threatening and needy things can be cute without providing coziness.
Childlike: In a similar vein, childlike games are often safe, but can have very high levels of stimulus and often lack the ability to focus on higher level needs.
Small World: Many small world games have a very manageable scope and smallness that generates a cozy feel, but small worlds can also be threatening, needy, or intense.
Romance: Cozy spaces often facilitate intimacy and a deepening of emotional connection, but romance opens a field to any number of aggressive or risky social encounters.
Home: Homes are familiar, but often stressful or full of responsibility, which negates coziness.
Party: While generating a cozy connective social tissue between players, parties are often high stimulus and high intensity, negating coziness.
Politeness: When politeness is thoughtful and kind, it can be cozy, but politeness can also be taken to an extreme, becoming insincere or passive-aggressive, which is anti-cozy.
Wealth: While wealth allows for the satisfaction of basic needs, it is not in and of itself cozy, and culturally can also come with societal expectation/responsibility of accumulating additional wealth.
2. Why Make Cozy Games
There’s an inherent joy to making games that help players explore their higher order needs. It feels good to help others.
However, there are also distinctly practical benefits.
Create blue ocean games for untapped psychographics
Increase retention by minimizing churn
Attract a better community
Blue ocean products for unmet player motivations
Games are a product that serves a player need. By uncovering unmet player motivations we can invent new product categories or broaden the appeal of existing designs.
Old motivational models: Many older game designs use pop-science motivational paradigms that are biased towards western, individualistic, and masculine perspectives. In many case, the underlying psychological models were derived by either sampling only young college aged men, animal experiments, or by actively throwing out data from groups that didn’t fit a particular hypothesis. From a business perspective, they fail to robustly describe motivations of women, people from non-western countries, older adults, or people with children.
Fight or flight: Derived predominantly from electroshock tests on young male rats, this theory says that when our sympathetic nervous system kicks in due to a perceived threat, we will either attack or run away. Though this reaction does exist, humans seem to have a far richer set of behavioral responses not captured in this theory.
Zero sum economics: In this model, resources are highly limited and if I take a resource, you lose that resource. There’s a long history of match-based competitive games such as Chess or Soccer derived from zero-sum systems. However, economics as a whole is based primarily on trade transactions that generate value for both parties. Even more concerning, most relationship-based transactions, the basis of friendship and human culture, are non-zero sum.
Gamers as competitors: For many years, game definitions in stuffy text books included clauses that stated games were inherently about competition. The assumption was that people who enjoyed games predominantly enjoyed competition. We know from Nick Yee and company’s work that competition as a motivator peaks in young men around 19-20 and then falls off gradually. By age 30, it is one of smaller motivational forces.
Gamers are best motivated by extrinsic motivators: Pop game design sometimes talks about players as coerced robots who respond to automatically to variably reinforced dribbles of extrinsic rewards. Again, these experiments were done on highly stressed out animal subjects. When similar experiments are done on low-stress, happy humans, we get a much wider range of responses; many addictive tendencies go away. In materially and emotionally plentiful environments, rote or self-destructive behavior is replaced by enriching pro-social human behavior.
Newer models: Newer models such as Tend and Befriend or Self Determination Theory describe a broader, more diverse set of player behaviors and motivations. We are also realizing that not all people go through life as if they are rats reacting to electric shocks. Contemporary psychology is rediscovering the benefits of rich, contemplative environment that lets humans thrive.
Tend and Befriend: This theory suggests many humans are motivated to bond with one another for safety and strength. We want to spend time preparing together against the uncertain future. We care for those that are weak or injured and find this just as important as hedonistically caring for yourself. These motivations are the exact opposite of dog-eat-dog, fend-for-yourself gameplay.
Self determination Theory: As we covered above, people are intrinsically motivated to pursue Autonomy, Competence and Relatedness. It turns out people are much happier, much more willing to stick around, and much more willing to invest themselves when they aren’t coerced via extrinsic rewards.
Quantic Foundry’s motivation profiles: After survey over 300,000 game players, this group found six main motivational categories such as Action, Social, Mastery, Achievement, Immersion and Creativity.
Cozy designs are a natural way to address these newly uncovered needs. In particular they seems to do the following:
Create non-coercive spaces with a strong focus on intrinsic motivations. Not surprisingly cozy games all tend to have extremely strong player agency.
Allow for players to pursue quieter forms of connectedness and personal mastery.
Players are willing to play beyond satisfying their core motivations. If mechanics fall on a spectrum of motivating to neutral to demotivating, most players will happily enjoy a game compromised of motivating and neutral mechanics. Demotivating mechanics (or aesthetics) are the most likely to disengage players and cause churn.
Coziness reduces these drop-off points via an absence of harsh, demanding, and needy motives. These worlds inherently are low stress, low disappointment, and therefore less likely to have explicit churn triggers.
Improve community relations
Players who seek out comfy games are not usually looking for conflict or stressful interactions. This may seem self evident, but it has a huge impact on community relations.
An anecdote shreds some light. Spry Fox ran two games with two very different communities. Realm of the Mad God was a permadeath MMO and Alphabear is a cozy, cute word game. The players in Realm of the Mad God met most typical MMO player stereotypes, with a tendency to become quite angry with both developers and one another. Much of this was due to the structure of the game, which created high stress moments of desperate survival and crushing loss. Alphabear on the other hand was mostly composed of highly literate, polite players who wanted nothing more than to play their game, collect cute bears and share witty comments.
There’s a simple process at play here:
Mechanics generate emotions: The game mechanics you design create certain types of player situations that match various motivational needs and in turn trigger emotional reactions.
Emotions attract players: Players that want specific emotions seek out games that produce them.
Social norms spread: In multiplayer contexts, players will watch one another and adopt shared social norms based off how people are reacting to the game. If the game tend to encourage anti-social, high stress behavior that is what players will model.
Developers reap what they sow: They’ll in turn use those same social norms when interacting with the developer.
In short, if you build a high stress game where people are encouraged to act like assholes, you’ll get a community of assholes who think that it is entirely normal to abuse others, including the developer of their favorite game.
If you build a safe environment that actively promotes prosocial behavior, your community will be much more pleasant. Players of cozy games likely score highly on conscientiousness and agreeableness. Cozy games attract nice people.
The Sad Exception of The Sims Online: The Sims Online was a potentially cozy game dominated by a community of sociopaths. Thematically, it had elements of coziness with pleasant house in friendly neighborhoods. However, these went only skin deep. In an attempt to make a ‘realistic’ simulation, many resources including housing were zero sum in nature. This enabled mafia-esque gangs to enforce coercive social structures like protection rackets. Very quickly the place became anti-cozy; a virtual dystopia. Coziness needs to exist at the systems level in order to have social ramifications.
3. General Cozy Design Principles
We’ve discussed what cozy games are and why you might want to build them. Now we’ll cover design tools that help you build them. These high level principles provide direction and framing for designing a cozy or cozier game.
Cozy is an adjective
As you approach cozy design, remember that coziness is an aesthetic goal, a flavor that can be applied to any underlying type of game. Some mechanics are emotionally more in tune with coziness, but any game can be made more cozy. This also means that there is no single defining genre that is “coziness”. (We have a whole chapter below about integrating cozy moments into traditionally non-cozy genres.)
Coziness is player dependent
Coziness depends on where the player is coming from when they interact with your game. You can encourage coziness, but you can’t force it on a player.
The coziest space will not feel cozy if a player enters it with pressing external needs. For example, a violently angry teen may find a little village full of happy birds infuriating since it does nothing for their need to exert force
A cozy social structure can still be hostile if players want to engage in a conflicting forms of expression. For example, a player who sees their universe as inherently about competition may find a game with meditative gardening oppressive or boring.
Cozy design become less about forcing an ideal utopian state and more about facilitating these feelings as best as possible given a wide spread of player motivations and emotional states.
Coziness thrives on authenticity
The closer coziness gets to a real world situation with real people and honest human pleasures, the stronger the impact. A real mug of tea is cozier than an image of a mug of tea. Real safety is cozier than reading about someone who is safe.
Digital games face a number of challenges here. It will be quite some time before we gain the tactile or olfactory feedback often associated with cozy objects and situations; the warmth of the coffee, the spray of the ocean, the sweet texture of a fresh-picked raspberry, the touch of crisp sheets in a warm bed in a cool room.
Yet there are numerous areas where games can still be authentic.
In multiplayer games, you are in fact interacting with real humans and you can build real relationships with them.
Opportunities for introspection lead to real personal insight.
Complex leadership or organizational skills can transfer to other real world situations.
So games do some pieces of coziness well and others poorly. Focus on their strengths.
4. Patterns of Cozy Aesthetics
Once we get past the general tips for designing coziness, there are a number of highly specific design patterns for each domain of game design. Over the following sections, we’ll cover aesthetics, content, mechanics, character, narrative and social system.
What are cozy aesthetics?: For the first sub-topic we’ll tackle how the aesthetics (visual, audio and/or tactile output) of a game element can create a feeling of coziness that is separate from (and therefore may be improved or reduced by) gameplay.
Most cozy aesthetic elements are sensory cues tha:
Are familiar to the player due to past experience, nostalgic or shared cultural language.
Intentionally evoke images of safety, softness, and contentedness.
Often contrast a shared refuge from a less pleasant external environment.
Historically, aesthetics of safety and softness have been marketed towards children, but cozy sensory cues can be more powerful for adults. Memories are like batteries of emotion. Over decades of living, an adult builds a rich history with otherwise mundane objects and environments, storing away personal and cultural meaning
Like the other elements of coziness, these aesthetics may be applied either across an entire game or within a non-cozy game as a “pocket” of aesthetic coziness. Cozy moments in any game can help reset or reframe the player’s mindset, as exemplified by the visuals and music of the game resume sequence in Stardew Valley (argued by Jeff Ramos of Polygon to be a key element in the game’s pastoral fantasy).
Ingredients of cozy aesthetics:
Abundance: Although visual or audio “clutter” is not recommended, a theme of plenty and generousness assists in player calm and security. Visually, providing evidence of an abundance of food, drink, joy, and/or warmth is common in cozy spaces across games such as in taverns, kitchens, cafes, and bedrooms.
Dragon’s Crown offers cozy cooking with an abundance of ingredients
Smooth Transitions: Gentle gradients between states, colors, or environments within the cozy area. Thresholds, however, between cozy & uncomfortable or even dangerous spaces may be more satisfying if distinct when seen and/or crossed, such as coming in from a snowstorm into a log cabin, or ducking into a cave behind a waterfall.
Hearthstone offers the metaphor of a small, carved wooden box from which you play the game, with smooth transitions between different play modes.
Protection & Support: Clear signals of strong safety and comfort, from the environment and characters around the player, signal that this is a safe place in which to explore higher-order needs. For example, a dog or cat that is soundly asleep or a guardian character that is relaxing indicates no danger is present, even outside the player’s senses.
Undertale uses warm tones, focused interiors, and the presence of a relaxed guardian character to indicate this space is safe and cozy.
Focus: Elimination of interruptions, pressures or sources of unwanted distraction, allows the player to feel a place is knowable and thereby becomes familiar and comfortable. In visual terms, this means a sense of enclosure and intimate framing. It is highly likely that “interior” spaces in early role-playing games eliminated exteriors for technological restrictions, but this focus continues to be used in modern cozy games, from Animal Crossing to Terraria.
The Zelda series often offers cozy house interiors, literally blocking any sense of an outside world that could interfere.
Mundanity: A fundamentally familiar and knowable setting or place will be cozier than the unfamiliar, alien, exotic, or fantastical, if only because it takes longer for the player to ascertain if the space offers true safety and abundance. Hammocks, tea rooms, and pantries, for example, are cozier than otherwise-beautiful and enclosed locations like palaces, zoos, or penthouse suites.
Refuge & Escape: if there is an “outside” to this space, it is contrasted in its discomfort or danger. Shelters from storms, roofs from rain or harsh sun, or even a garden inside a bustling city make a place of everyday self-care.
One of the earliest promotional images of Hyper Light Drifter are of the drifter relaxing next to a campfire while monsters look on. The eyes at a distance make the fire feel even cozier.
Human-centric: The comfort and ease of humans in this space or system is apparent. The scale of the objects, architecture, and other creatures are comfortable for humans. Things which are too small or too large intrude on coziness with feelings of unbelonging, claustrophobia or agoraphobia.
Terraria’s room requirements and mechanics encourage cozy placements of lighting and doors to keep out threats and protect allies.
Welcome: When the player is explicitly positioned as a welcomed entity, this gives them the freedom and safety to express themselves. This welcome does not imply responsibility or pressure on them as a hero or other job to perform, but rather welcomes them as a person, to join whatever activities are available, or to be alone, as they wish. Bartenders often greet newcomers with a welcome, whether the tavern is digital or physical, to encourage a longer and more leisurely visit.
Seasons: The visual passing of seasons is heavily connoted with coziness in their familiarity and rituals, often of community and abundance. Autumn and winter are especially rich in potential, with a good harvest and refuge from cold weather causing potential any interior space to become cozy.
Ritual: Facilitating repeated, meaningful actions can create familiarity and contentedness.
Harvest Moon was a popular cozy title that offered a mundane, ritual refuge in pastoral life, with clearly demarcated seasons to signify both economic and community activities.
Cozy visuals include:
Colors: Warm, gentle color palette (yellows, oranges), without high-intensity contrast or hues.
Light: Warm-toned lighting of clear origin and low ambient, which allows for soft shadows. If the source of light is intense, such as the sun, bright lamp or stoked fire, it’s best to soften the beams in some way (i.e. dappled, partially obscured, or gently shaded).
The Witcher 3 uses warm, yellow tones in its lighting and materials to make their taverns feel even more welcoming.
Natural materials: wood, stone, fur, moss, cotton, wool, water, living plants. These materials are familiar, implying either sturdy, ancestral safety or physical comfort. These materials can be harder to find in science fiction worlds, making them more likely to feel sterile, unwelcoming, and uncomfortable. Hand-made materials and rustic objects, which imply they were crafted and/or preserved with care and attention.
Yoshi’s Woolly World makes the entire world feel touchable, soft, and lovingly crafted.
Space: Closer, intimate, more enclosed spaces. Outdoor spaces should obscure the distant horizon partially in some way, through geometry, fog, or darkness.
Contrast: Ideally, provide a window or reminder of an external non-cozy space you are taking refuge from, such as rain, snow, etc.
What Remains of Edith Finch offers many intimate spaces to explore, but none so cozy as the sun-dappled grandmother’s room, with evidence of leisure time and abundance.
Cozy audio is continuous, soft, and non-intrusive, with an element of familiarity. The sources of both music and audio should ideally be diegetic to allow the players to connect concretely or even intimately with those sources.
Ambient, possibly dynamic or gently unpredictable. Ex. Playstation background music, jazz.
Gentle acoustic, organic/human-centered music – score and performance
Humanity in music can also be increased through small, subtle imperfections, such as recorded aspirations, fingering mistakes, etc.
Any hint of external threat or danger should be muted and distant
Ideally all sounds should have an identifiable concrete, diagetic source.
Waterfall, rivers, rain
White noise can be used to help with difficulty sleeping, as the varying texture ‘washes out’ individual noises and becomes easily ignored. This effect can also be achieved by steady hums or noises, such as from fans or machinery.
Cozy locations are centered on leisure, practicality, ritual, history, and familiarity. Cozy content allows for privacy and creative expression, physically dividing spaces into nooks and alcoves and providing means for people to spend companionable, low-intensity time with others or in solitude. It can be helpful to also reference historical or other deeply familiar touchstones, to make the space more immediately knowable. Places where players can decorate can become cozy as it suits the player’s taste and expression, and players may seek out cozy environments as a way of changing pace in contrast to more demanding environments.
Cozy place examples:
Sociable yet private, discrete 3rd spaces separate from responsibility or ‘work’: bars, cafes, retreats, libraries, cabins, gardens
Transition spaces without danger or obligation: trains, backseat of a car, slow-moving spacecraft
Unpretentious community gathering & ritual spaces: farmer’s markets, kitchens, chapels
Places that fill basic needs, including food, rest, warmth, and opt-in sociability. They should include visible places to comfortably sit, eat, drink, and view beauty.
Places that support low-demand companionship, such as those with calm pets, or passive NPC-watching.
Spaces can become cozy once danger is no longer present: an arena where a boss fight used to be can become a cozy playground for celebration and bonding, or a cozy environment can be a goal for exploring part of a map.
Places with enclosed, strongly seasonal identities will also evoke coziness
Food and drink themselves can be cozy: a frothy mug of beer is more cozy than a alchemical potion; items that can be shared or suggest plenty (a slice of cake, a bunch of grapes, sacks of flour) reinforce a sense of sharing, abundance, and generosity
Things that are cute but low-intensity can be cozy: elaborate costumes and skins may be too laden with status or opulence, but simpler or understated styles can feel less threatening or attention-seeking.
Even cold and hard objects (typewriters, tea sets) can invoke cozy feelings of intimacy or nostalgia, if lovingly hand-crafted (“artisanal”) or loaded with familial or historical meaning.
Domestic objects can signal coziness, the more mundane the better: wagons, mailboxes, a porch swing, a pair of boots, a raincoat.
5. Patterns of Cozy Mechanics
Beneath the aesthetics of a game, its underlying mechanics may seem at first neutral or benign with regard to coziness. However, a fundamental mechanic or motivation can engender a positive or negative sense of coziness, and contribute to the overall tone and feel of the game.
Intrinsically rewarding activities
For something to be cozy, it has to be, in and of itself, satisfying — not satisfying because it contributes to some other purpose. When the reward of an activity outweighs its gentle momentary pleasure, the activity can become extrinsic and lose its cozy appeal.
Compulsory mechanics often detract from a game’s coziness. Since coziness is an opt-in affordance, any player activity driven by extrinsic motivation – either as requisite responsibility or threat-response, or as an artificial reward – tends to evoke an un-cozy experience.
In Animal Crossing, the sounds of shaking trees to get fruit is inherently pleasurable even after thousands of repetitions.
In Zelda, the cooking process of tossing the ingredients in the hot pot and waiting to see if they’ll be a success is inherently pleasant.
Breadth of optional activities
The cozy experience depends on high player agency. You need to chose to do a task of your own volution. Giving players a wide number of possible tasks and then not forcing them to do any of them lets them take responsibility for their actions.
In Animal Crossing: New Leaf there are numerous activities such as fishing, decorating, gardening, clothing creation, fetch quests, etc. But all of these can be ignored with no ill results. The same pattern is used in Stardew Valley and Harvest Moon.
We see something similar in less cozy games like Zelda: Breath of the Wild. Cozy activities are harvesting and cooking are never required to progress.
In Destiny 2, there’s a soccer ball just sitting there. If you kick it into a goal, a scoreboard increments. But nothing tells you to play soccer. There’s no official start or stop to a match. This ends up being a cozy moment of opt-in social fun.
Some player activities can achieve a sense of coziness due to their familiarity. Repeated low risk tasks allow the player to relax.
Safe: The activity is known to be safe and will not cause stress or danger.
Known: The activity is constrained. It will not suddenly eat up an unexpected amount of time, labor or resources.
Relaxed: The activity is low mental cost. It occupies the hands, but frees the mind to work on other more subtle concerns.
A mundane act of organization or tidying
A walk down a familiar path.
Searching or collecting for diamonds, berries or fossils — though not under duress.
Even a busy environment or activity, if exceedingly familiar, can provide a sense of coziness. Like the ritual of going to a gently buzzing coffee shop to write.
For some players, a self-appointed task — harvest and replant the crops — can be relaxing in a safe, soft, and satisfying way.
There are many mundane objects from the cozy items list above are associated with low risk activities.
Fishing with a fishing rod
Reading a book
Putting on cozy socks (and wiggling your toes)
Bundling up in a quilt your mother made you. Or sewing a quilt for a child you love.
Brewing tea in a chipped tea set given to you by your grandmother.
Drinking steaming coffee from that strange handcrafted mug you got visiting your aunt in Maine.
Typing on a clacky typewriter in a warm wood paneled study (with flakes of snow outside)
The challenge of emergent extrinsic rewards
Some mechanics may start off as cozy, but later become reduced or compromised as players acclimate to gameplay systems and (consciously or subconsciously) seek to min/max them. A quaint trading bazaar or relaxing spawn point in an MMO can rapidly lose its charm as players queue up for their turn at an activity, exchanging intimate intrinsic experiential rewards for (ultimately shallow) extrinsic payouts.
We recommend tracking player behavior and identifying when extrinsic rewards start to take over. Often a simple obfuscation of feedback is enough to dampen the feedback loop. If that doesn’t work, take a look at the economic rewards and balance them such that comfier behavior dominates.
The challenge of cozy monetization
Coziness can be weaponized. Because it establishes intimacy and vulnerability, it can be used to lower barriers to purchase.
For example, a timeshare sales process offers a participant a free meal or cash reward in an comfy, gorgeous setting. In return, they leverage this atmosphere of generosity to encourage the mark to complete the reciprocation loop and purchase a very expensive timeshare.
Many standard monetization practices damage coziness. Social comparison creates social anxiety for some players. Time pressure on sales and event generates a fear of missing out. Heavily promoted item rarity makes players feel a strong sense of scarcity.
The best practice here is twofold
Service existing needs. If you can, sell products within the game that address real existing player needs. You’ll be selling something that results in a meaningful addition to the player’s life outside the game. Minimize artificially generating needs and then cynically making merchandise to fill that need. Don’t be the doctor who poisons their patient and then sells them the cure.
Balance for honesty and coziness. Some scarcity and social comparison is okay if done in moderation. It can provide contrast to other cozy elements. Tom Nook in Animal Crossing: New Leaf traffics in most of the crass aspects of capitalism. Yet because he is an opt-in component of a much larger game, it ends up being okay. When Animal Crossing: Pocket Camp makes this experience the whole game, coziness is lost.
6. Patterns of Cozy Narrative
When coziness is the central mode of a game’s narrative, it tends to exhibit certain qualities:
Low-pressure – Even if the stakes are high, anxiety is low.
Low-intensity – Cozy stories unfold at a place of the player’s choosing with little urgency.
Ensemble – Stories of a “chosen one” that emphasize the exceptionalism of the player are at odds with authenticity.
Non-violent – Conflicts are ephemeral and a path to understanding.
Intimate – A moderate size number of players to build familiarity
Down-to-earth – Humble and grounded. Find wonder and contentment in the familiar.
Emphasis on ritual – seasons, holidays, day/night, harvest cycles. Stories that lazily drift along the river of time
Episodic – The sum of experiences is greater than any one story
The Atelier games frame the fantasy around career, community, and cozy objects/spaces
Intermezzo refers to a musical score that occurs between other major musical movements. Coziness can also offer respite in an otherwise intense narrative:
Safety in the storm – Dark Souls campfire
The calm before the storm – Ellie’s guitar or giraffes in The Last of Us
A place to call home – The private rooms of the Normandy in Mass Effect
Denouement -after needs are met, climax achieved, explicit opt-in room to relax and “do nothing”
Dragon Age: Companions take time to reflect and unwind after adventures.
Cozy narrative archetypes
We see common narrative patterns show up repeatedly.
“It Takes a Village” – Communities banding together for the common good
“Homecoming” – A return to familiar faces and a gentle reflection on time
“Immigrant’s Story” – Starting a new life in a new place with a fresh start
“Pastoral Escape” – Consciously choosing to leave the troubles of modern life for something simpler
“Honest Labor” – the celebration of dedication to a craft
“After Hours” – A focus on the small moments and relationships that happen in between work and adversity
Night in the Woods: Narrative leans into cozy tropes to explore complex themes
7. Patterns of cozy characters
Non-player characters in a cozy game should exemplify or facilitate the cozy virtues of safety, softness and satisfied needs. This can manifest through the character’s role, their aesthetics, and the affordances of interaction offered the player.
Tend and befriend
Cozy characters embody the tend and befriend response, offering players a support and respite from outside stress. They are often nurturers, providing affection, shelter, food, companionship, and acceptance. More simply, characters reassure the player that they are loved. This can manifest with roles traditionally roles traditionally associated with cozy places – bartenders, innkeepers, librarians, farmers, grandmothers, spouse, etc. They can do the heavy lifting of emotional labor for the player.
Cozy characters can assist the player in her goals. The coziness of these gestures is amplified when the acts are non-transactional. In the cozy fantasy, we help each other because it is the nice thing to do. Favors and gifts are cozy; obligation and neediness are not.
Characters might be designed to be recipients of nurturing gestures by the player. Taken to the extreme, this can include literal pets or characters who fulfill the same function of a pet, whose function in the game world is to adopted and cared for. Conversely, curmudgeons and even pariahs have an important place in cozy games, offering the player the ability to signal empathy. These antisocial characters give a community authenticity; like a patchwork quilt, mismatched scraps add to the charm.
Ignis in Final Fantasy XV taking pride and pleasure in cooking for the party
Characters in Stardew Valley sending you recipes in the mail to show gratitude
Cranky villagers in Animal crossing keeping things grounded
FFXV’s companions are confident in what they bring to the team and look out for each other.
Intimacy, authenticity and autonomy
Within a cozy space, character interactions should allow for vulnerability and intimacy. The intrinsic reward for engaging with cozy characters is a sense of belonging in the community, possibly, but not necessarily, building to friendship or romance. Gestures of trust, like sharing a secret or inviting a player into a private space, are especially powerful at making the player feel welcome.
In crafting cozy characters, authenticity is more important than complexity. Simple interactions should reinforce that the characters have their own inner lives separate from the player’s agenda. Brevity is a virtue as it puts less pressure on the player to know everything about a character.
Cozy relationships are founded on consent. What makes grumpy characters tolerable and even charming is the opt-in nature of engaging with them. It is comforting to know that within a community, life goes on independent of the player’s agency.
Going out for Ramen with Ryuji in Persona 5
KK Slider passing through town
Oscar the Grouch
Persona 5 builds intimacy with its cast through mundane activities.
Visual character design
Characters can leverage cozy aesthetics, much like places.
Posture and animations that emphasize relaxation and contentment can model a cozy mood.
A soothing voice, like that of Bob Ross, can put the player at ease.
Soft and cuddly appearance that invites hugging, like a Totoro
Cozy context allows otherwise threatening authority figures, like a boss, a cop, or royalty, to expose their humanity. Anyone from a criminal, to a demon, to a king to a town drunk can be cozy when the let their guard down. Coziness is a shortcut to empathy.
In Howl’s Moving Castle, Calcifer is grumpy, judgmental, and initially fearsome, but moments of vulnerability make him a beloved member of the household.
8. Patterns of cozy social mechanics
One of the key higher-level needs is forming connections with others. While NPCs do offer an avenue for players to practice forming relationships, our current weak simulations will never replace real relationships with real people. For this, we need to examine the cozy systems of multiplayer games.
Challenge of cozy interactions online
Virtual environments present unique challenges to the facilitation of coziness. Online is arguably inherently dehumanizing.
Strangers: Due to the logistical challenges of getting friends together in the same time, place and game, online game players tend to be strangers. We don’t know or trust most strangers and are generally act in a guarded fashion around them. This immediately puts safety on the back burner.
Lack of persistent identity: When players know they’ll never see another person again, they may lower their inhibitions to pushing the spatial, moral, or legal boundaries of others. You need to build robustly pro-social systems or else players immediately devolve into a Lord of the Flies-style wasteland of griefers and populist mobs. Witness Twitter.
Low bandwidth communication: Most of the information present in real-world human interaction is either inaccurate or simply not simulated in games. Facial expressions, tone of voice, even conversational pacing is lost. Troublesome behavior like insincerity, perceived or real, ruin the coziness of a player’s experience.
Use cozy norms to attract a better community
The current dominant multiplayer design pattern uses limited resources, high stakes, and hazardous worlds to drive competitive behavior between players. The optimal strategy in these environments is to see other strangers as enemies who must be avoided or destroyed. It is a recapitulation of Fight or Flight motivations.
The cozy alternative is to implement abundance, safety, and reprieve to foster cooperative and trustful interactions. The resulting pro-social environment can shift players attitudes positively towards other players. Instead of destruction, we signal mutual support. Instead of othering, we showcase the formation of coherent social groups. You’ll see these steps occur:
The game promotes social norms that promise and encourage trust.
This results in fewer failed reciprocation loops. Players let their guard down.
Players that reciprocate tend to escalate the depth of their relationship.
Over time, comfy spaces yield stronger friendships.
Social norms to aim for: When designing a cozy community, ask yourself what social systems and signals you’ve put in place that encourage the following community norms. Focus on the positive things you can do vs the things you shouldn’t do.
Politeness: We are nice to one another.
Consent: Ask for consent. It is okay if someone opts out.
Help one another: If someone needs help, the community will lend a hand.
Protection from threat: If there’s a threat, the community is a safe haven.
Emotional support: Sometimes people have a bad day. The community is willing to lend a shoulder to cry on.
Celebration of relationships: It is wonderful when people meet and wonderful when they become better friends. The community supports this.
Mend, Don’t End: People make mistakes, sometimes people get hurt. As a community we will try to mend things when we get upset.
Tools for creating norms: There are systems worth adding to facilitate social norms. You don’t need to just accept what the community brings. You can shape it.
Code of conduct: Get players to agree to how you want them to behave in the game. This works.
Feedback systems that immediately target a behavior: Make systems that target a behavior, not ones that label a person as bad, evil or ruined. Reputation flags or banservers end up creating culture where bad behavior is acceptable. Instead, notify players in a timely fashion that they’ve done something against the norms and let them know what the infraction was and how they might improve.
Gameplay scenarios that enforce norms: If being generous is a goal, create quests that result in being generous to others. There’s a risk with use overly strong extrinsic rewards, but simply signposting the activity is often enough.
Beware of importing norms: Often you’ll import arbitrary norms from the default culture and these can accidently poison the cozy atmosphere. There are many of these related to gender, race, age and class. Even traditions such as RPG Alignments can be problematic. For example, in D&D it is possible to have a Chaotic Evil character. But when that player roleplays that norm, the rest of the community suffers.
Note on cozy in competitive games: While online competitive games can hurt friendship formation, there’s still room in team-based games for cozy moments. Think about creating warm and welcoming spaces for the team members when they aren’t fighting. Give them a place to work on deepening their relationships with one another.
Escalating layers of opt-in interactions
Permission setting is perhaps the most important tool for prevent a social coziness calamity. It is too easy to accidentally for someone for force communication on another person, holding them hostage to an interaction they don’t desire.
Call and Response interactions: A player chooses to broadcast a no-pressure initiation to a group (best if larger than one other person). Other players can choose to acknowledge the call and respond, but are not burdened with expectation.
Layers of Investment: In this “Social Onion” model for permission setting, a player starts at a level of non-interaction. At their own discretion, the player may then opt-in to increasingly risky layers of interaction with individuals or the public one layer at a time.
1 on 1 interactions can come later: There an obligation of both attention and intimacy that occurs in a 1 on 1 social situation. Even the act of listening is a form of emotional labor. You may want to structure your comfy inactions so this is optional and only the default for people who have opted into a higher engagement relationship (such as declaring mutual friendship).
Use invitations to escalate a relationship: Demand and requests can generate an unpleasant obligation to respond. Create ways for players to kindly invite another person to a space or activity. This is a very warm and welcoming opening and creates a safe opportunity to opt-out. Group invites are good for new relationships. Individual invites are good for medium to high intimacy relationships.
Small cooperative groups can facilitate escalation: Encourage dense, frequent interactions between small groups of players. By forming players into persistent cohorts (via guilds or matchmaking) players will bump into one another regularly when they play. This incrementally creates familiarity, recognition, sense of shared experience (all cozy factors). Some members of the group will naturally opt-in to deep relationships.
Opt-in permissive communication channels: Trust come late in a relationship. As a result early transgressive humor can be quite hurtful. But later, once players know one another, humor becomes a signal of trust. We can joke and be silly and not be censored. We can share intimate and scary details about ourselves without risk of rejection.So where early communication methods are locked down, small group or friend-to-friend channel need to be more more permissive. Or else cozy trust will not flourish.
Blocklists: When your best attempts at creating mutual opt-in interactions fail, blocking communications is a necessary evil. But it would be preferable to avoid disruptions to the player’s experience altogether. Whitelists and de-escalating barriers may be more natural and effective.
Low risk social interactions can feel cozy. When you nod to a smiling passerby or wave to a friend, you are fulfilling your social needs in manner that doesn’t take much effort and is unlikely to be rejected.
Tools for low cost reciprocity:
Positive Emotes: Have a curated emote system that focuses on positive signals such as smiling or waving. Allow congratulating, nodding in affirmation and encouraging.
Grumpy emotes: Negative emotes are still useful, but you can treat them in a melodramatic cartoon fashion that takes the sting out. A cute little character stomping about is a lot more palatable than one that screams loudly or teabags your avatar.
Automate subconscious social interactions. Characters can turn to face a player as they walk by or tilt their head in acknowledgement. As you get closer, the other avatars can pay more attention. If you talk, other avatars can automatically turn to listen. This mimics what we do in real life.
Streamlined UI: Make the interface for emoting accessible and easy to use. If you bury social verbs in menus, they’ll never happen.
Signal social context automatically: For example, a scene can shift to low light intimate colors if two people are chatting but shift to bright colors if lots of people are talking.
Central to all these tools is the design exercising of imagining you exist in a space where you are known and accepted and asking some simple questions:
What social interactions would occur?
How can you work those into the animations and communication options for your character?
Limits to cozy emotes: If the game mechanics are poisonous, ‘nice’ emotes can become polluted. Emotes in Hearthstone (a PvP game) on the surface are pleasant, polite interactions. However, the community quickly figured out how to make them into biting insults. For example ‘Hello’ is used to brag when delivering a particularly deadly combo attack.
Also be aware that emotes are good for ritualistic social maintenance, but not for intimate disclosure or deep relationship building. In fact, a superficial emote used on a good friend may feel dismissive.
Let conversations ramble
Consensual conversation is a naturally high agency, high creativity activity that builds strong social connections. Online communication is often used in games to help players coordinate get things done. But cozy conversation tends to occupy more of a social maintenance space. That is, chatting about nothing in particular with a friend is more cozy than trying to make a decision in a meeting.
Create moments or spaces in your game where players can communicate without much emphasis on purpose of meaning.
Let players linger in rooms or areas where the purpose is fulfilled rather than giving them the boot.
Have group harvesting or crafting moments where players are engaged enough to stay in the area, but the activity is low intensity enough that they can still chat and follow a conversation.
Champion Trains in Guild Wars 2
For example, an unintentionally high-retention activity known as “Champion Trains” emerged in Guild Wars 2 when players complete easy loops of boss monsters to kill repeatedly. There were better rewards elsewhere; but a big draw was casual social interaction with the community. Because these groups are easy to coordinate, chats often featured relatively meaningless and rambling topics.
Gifting is one of the more powerful social signals of abundance and caring. A gift tends to mean the giver’s basic needs are met and they want to support others. Gifting is often an intrinsically motivated gesture where it is gauche to expect a gift back. This perceived honesty acts as jet fuel for the reciprocity engine that drives deeper friendships.
However, not all gifts are created equal.
Person interaction associated with the gift: Direct interpersonal interactions mixed with small personal gifts are the most cozy. The gift augments the existing warmth in the relationship, but ultimately the face-to-face interactions and long history are the source of meaning.
Care delivering the gift: Gifts become less cozy when when they are received by a courier or heaven forbid, a utilitarian menu.
Effort sourcing gift: Gifts also are less cozy if there is little care given in selecting or producing the gift. Social games had buttons where you could spam friends with endlessly duplicated boosters of little value. Players soon learned that these were mostly meaningless. On the other hand, the game Triple Town only gave out gifts if you had scored well in games that could last weeks or months. And you could only give those gifts to a single person. Each one was precious and valued.
Safety in numbers
We can create cozy economic situations that encourage players to bond together in order to keep out a hostile world. This technique again taps into tend and befriend psychology.
For example, in Everquest, player would settle down around a camping spot to rest and recharge. At any moment a train of high level monsters might smash into the group wreaking havoc. However, the space felt cozy since you were together with other player who you knew would leap to your mutual defense at the appearance of danger. Together, the players feel safe.
Fishing in PvP zone Alterac Valley in World of Warcraft
The comradery of fishing together in the PvP areas of World of Warcraft
Crafting at the campfire in Don’t Starve together during the night.
Cluster of traders sitting in the dangerous wilderness in Realm of the Mad God
Use cozy feedback to make up for low social bandwidth
Address the low fidelity level of virtual social interaction head on. Design channels of feedback that help players clarify the context of situations and communications.
Trust Heuristics and Settings: Move beyond binary expressions of trust and permission, such as friend or not friend. Alert players to how they are progressing along multiple dimensions of a potential new relationship, such as common friends, interests or skills. Automatically color a chat room based on how many players are present, or how intimate the chat is.
Opt-in to social risk level: Allow certain levels of automatic permission based on the player’s social-risk preferences.
Feedback Requests: Give players a non-threatening method to ask for feedback and find out how they’re doing. Private feedback channels allow people to make adjustments without being shamed.
Apology Channels – Offer players the ability to atone for mistakes. Sincerity is key, consider enforcing a delay or an ability to immediately say “I’ll think about this” and let the apology come later.
Google Hangouts experimented with allowing users to collaboratively dress up message windows. Would this change the tone of your conversation?
Drawbacks of Social Coziness
Forcing cozy causes it to fall apart
Remember that more isn’t always more. Cozy social interaction is a trust-based process and the nature of trust is fickle.
Trust is earned slowly and then quickly lost, often collapsing relationships when it all falls apart.
Vulnerability is difficult enough to reach in the real world and it may take longer to reach that state in an online game.
Some players may simply be wary or incapable of forming cozy virtual connections with others.
When There’s Comfort in Solitude
Games that facilitate a high degree of social coziness run the risk of eventually isolating players from one another. As players form deeper relationships and tightly-knit groups, they may lose a sense of that game’s greater community as a whole. In some cases, various gaming communities are so cozy that they’ve grown indifferent or hostile to newcomers and outsiders. Monitor the density of your game’s population carefully, and be sure to facilitate new connections between players.
Looking Ahead: More Sofas, Less Lobbies
Outlook on the future of social game interactions should be optimistic. Anecdotes of poor behavior that pollute the online gaming space may (at least in part) be a case of how function follows form. Though initially useful for clarity, many conventions of online spaces and interfaces are aging poorly. As audiences become more sophisticated, so too should the mechanics by which they interact online. Coziness can be a useful evaluation lens on how a social mechanic might be upgraded or replaced.
For example, the term “lobby” is often used in gaming to describe the pre-game flow of activity. Consider what types of interactions you’ve had with other people in lobbies. Now decide if that’s really how people should meet and interact in your game. This is applicable to developers of cozy worlds and perhaps doubly-so for developers looking to build social retention into any type of game.
The developers of Halo 2 re-imagined the matchmaking lobby as a virtual sofa. At the time, staying with the same group from match to match was a big innovation. A very cozy move for a decidedly un-cozy game.
9. Augmenting Non-cozy Games
You don’t need to make your entire game uniformly cozy to gain the benefits of cozy design. Many traditional games satisfy cozy needs by including separate, safe cozy spaces.
Here are several patterns you can use to integrate coziness into your game.
A refuge in an otherwise intense game
Think of creating a cozy sandwich for your high stress game. On the inside are the meaty moments of action. And on the outside are comforting moments of coziness.
The cozy sandwich
In the hardcore hit Dark Souls, gameplay is built around an accumulation of stress. The further you are from the safety of a previous bonfire (save point), the more at-risk you are for permanently losing your accumulated resources.This is not cozy.
Yet, the bonfire locations in Dark Souls manages to have several cozy qualities:
They remove all immediate danger (no monsters, no aggro overlap with something outside the space, no dangerous surfaces), which gives a moment of safety to an otherwise intense and dangerous game
They provide an ability to spend your currency, lessening the risk of losing it, which also ties to safety (no impending loss or threat)
The audio, lighting, and level design feedback is leveraged to create an intimate space with soothing qualities (the crackling of the fire, the lessening or elimination of intense sounds, the warm glow of the fire, the closeness of walls). These are linked to the quality of softness by providing comforting feedback and an opportunity of a lowered state of arousal.
The bonfire also by giving you access to your storage, offering abundance of resources when the more frequent gameplay experience of adventuring features resource scarcity (limited pockets, stack limits). There’s a moment for tinkering and rearranging.
These cozy qualities improve pacing throughout the game, and form the basis of the central loop of the game:
Desire: You start in a place of safety, but also suffer from scarcity.
Adventure: Motivated by your lack of resources and a need to progress, you move further into danger, collect more vulnerable resources, and overcome a large risky obstacle.
Respite: Finally, you set your burden down to reset the loop and save your progress. This is the moment of coziness.
This loop keeps the game digestible and the wins incremental and continuous rather than one large all-or-nothing encounter.
Persistent small groups in multiplayer games
Call of Duty (among other games) will team up the player with a small group of other player and persist that group throughout matches. This social structure has several cozy qualities, but the specifics of the group makeup could make that grouping feel more or less cozy.
Spending continual time together with a set of shared goals promotes familiarity and reliability between the participants.
The matchmaking process is opt-in, so these connections aren’t thrust upon you in an uncomfortable way. You can alway opt out if the situations starts to feel emotionally unsafe.
These shared experiences with a more intimate might open conversation and expression possibilities inappropriate for a more open, anonymous venue. This freedom ties to one’s ability to be vulnerable and expressive without negative ramification. Obviously, if the group is hostile toward these overtures, then this potential breaks down.
Though the game might feel inherently non-cozy, these moments of social coziness help to form lasting bonds and promote strangers to more meaningful relationships where deeper communication and social safely exists.
Build cozy connections with non-player characters
Characters can also function a cozy moments in otherwise non-cozy games. This satisfies the need to connect with others in a safe fashion. Cozy NPCs are often facilitators, and can be connected to cozy locations. Here are some examples:
Ness’ dad and mom (Earthbound)
The act of saving in many games usually asks the player to pause for a moment, and in this case, that opportunity is taken to deepen your relationship with your father, get some hints, and sometimes even get a few bucks in your account. This interaction features softness, where intimacy of emotion is a break from the moments of combat or other exploration pressures.
Shopkeepers in River City Ransom
The shopkeeper experience in River City Ransom achieves multiple cozy objectives. The cities (where these often appear) are safe and free from opponents, and the shop itself gives a tiny window into a confined, cozy scene between the player and the vendor. The ‘free smile’ has no gameplay progression implications other than to reinforce the safe nature of this space.
This type of space (and other shops) provides a loop closure that forms the backbone of integral gameplay systems (currency acquisition, ability expansion). The cozy qualities of this space afford the player a moment of respite from the compulsions of the other gameplay spaces.
Shopkeeper in The Legend of Zelda
This shares some elements with the River City Ransom shop, but this shop allows the player free movement in a safe space. There is a break in the music, signaling a shift from gameplay to rest space. The walls are closer in than a normal screen, providing an intimacy of space, the colors are warm, and there are bonfires to contribute to the warmth of the space despite the low resolution of the scene.
Chef in Odin Sphere
Moments of character advancement are slowed down for Odin Sphere, and the focus becomes on the act of preparing and consuming food.
Bastion/Stanley Parable narrators
These characters form a comforting backdrop during the play experience. In Bastion, though the character is fighting and in a high state of arousal, the narrator exudes a calming voice, and has authentic and human qualities that help form a cozy connection throughout the game session. By the end of the game, the character is familiar and the relationship between him and the player is substantial.
10. Cozy Development Practices
We’ve been talking about building cozy games, but cozy practices can also be applied to the process of building a game. Or for that matter running any company. A cozy environment tends to have the following benefits:
Emotional safety leads to honest communication and genuine collaboration.
Abundance leads to a willingness to experiment without fear of loss.
Retention of key personnel. Many developers prefer being in a cozy space, or having access to one. Once you’ve experienced a cozy workplace, it is hard to leave.
Foundations: Consent and social norms
How your development team operates depends in large part on the social norms you’ve established. Consider:
What social norms does your team hold?
How are they established, reinforced and signaled in your team?
Many cozy practices are easy to implement if you are clear in the beginning about what’s acceptable. You need to structure and establish boundaries. Consider working with your team to create genuine, sincere codes of conduct or value statements. Be sure to include the following cozy concepts:
Abundance: What are your clear structures of support if something bad happens?
Safe consent: How can employees opt-in (or opt-out) of risky opportunities?
Softness: How do you create quiet spaces for social connection and self improvement?
Cozy spaces and environments
A space for each type of task: Collaborative design work can be held more effectively in smaller or enclosed spaces. Are your 1:1s held in rooms where both participants feel comfortable and can trust that their conversation is private? Are teams able to take conversations to separate areas where there is less outside noise or bother?
Coziness can be tricky to implement in a workspace, however. Too small a space can be intimidating or claustrophobic, and dim lights can just make it hard to function. It may also be unwelcome if the designer is not yet ready for higher-level work and needs to pursue needs for safety, hunger, thirst, and/or sleep.
Opportunities to escape to a cozy spot: Allowing individuals to choose to go to a cozy environment when desired — say, for brainstorming on an interesting new possibility — can help people offer, develop, and exchange ideas when they otherwise might be drowned out.
For example, Daniel Cook has a coffee shop he escapes to whenever he needs to do writing.
There is food and coffee which removes any hunger or thirst.
The baristas know his name and (most of them) smile when he walks in. This is a space of safe social connection.
In the back of the shop is a quiet area with a warm, bright fireplace.
The decor is dark wood and stone with light music trickling in from the front room.
Outside, it is often raining gently. Or it is gloriously sunny. Or the fall leaves just take your breath away.
No one tells him to go. No one tells him to leave. Writing in the cafe is both a opt-in choice and a comforting ritual he’s been doing for years.
Cozy time: Time can also carry aspects of coziness. Some creative folks give themselves guaranteed unstructured time when they aren’t available for meetings or aren’t working on anything specific, which allows for reflection, inspiration or even just feeling unpressured for a spell. Unscheduled time and personal projects can reap the benefits of coziness as a person’s mind finally has permission to open up and consider new possibilities.
Crunch is not cozy
Consider the extent to which we encourage people to volunteer for extra work, and how such volunteerism is actually pressured. Crunch can result from extrinsic social pressure. Or an internal creative drive. Both still contribute to burnout, increased bug counts, and frustration. When it happens, burnout explicitly blocks coziness since exhaustion prevents team members from moving up the Maslovian hierarchy of needs.
A solution is to increase opportunities for self care.
Permit opt-in work schedules. People who can work from home (often a safe, quiet space) or within a flexible range of hours report less stress, higher job satisfaction and higher productivity.
Explicitly offer sabbaticals, “mental health days”, and even the ability to take a break or pause a meeting can help reinforce the value of consensual participation.
Don’t make impossible schedules that force overtime. This reduces developer agency and long term leads to bad decisions and team churn.
Cozy trust and secrecy
Secrecy and trust are complicated issues in an office. It is crucial to have people you can confide in about doubts or concerns. However, the social dynamics of secrecy can result in decidedly anti-cozy patterns. An employee may not wish to report an issue to their boss for fear of the messenger being shot. Or they may prefer to communicate only through gossip. Or they might form cliques where others feel left out. These are all defensive behaviors intended to preserve personal safety.
The response is to create safety such that there is less need for defensive behavior.
Separate the role of manager and mentor (a senior developer not in the direct chain of management) to introduce a confidant who can be trusted and to remove strange power dynamics
Actively police interactions were people are punished for being open and trusting. Encourage those that share unpleasant facts.
Create opportunities for groups from different cliques to spend repeatedly time with one another. Trust is built upon relationships that form via repeat, positive interactions.
One of the most fruitful avenues for encouraging more coziness in design practice is by cozifying feedback processes, because it makes people feel safe and increases trust.
If you can do so earnestly, consider these guidelines for maximally cozy feedback:
Be gentle and considerate: remember that most people want to be good and want to receive feedback, and are probably aware of the issue in some context, but it’s hard to switch contexts without raising defenses.
Be clear: Ambiguity creates more pressure, and a generalized threat. Identifying specific behaviors, instead of identities, is similarly less threatening.
Respect wishes: Respect requests on both sides for privacy, patience, and even outright secrecy, in the pursuit of improved trust.
Be timely: Providing processes for immediate repair can restore a positive tone and return control to the person receiving feedback.
Giving feedback cozily would also, presumably, lead to longer-lasting behavioral changes, as the motivation is intrinsic.
If your working environment thrives on interpersonal conflict, anti-cozy patterns will predominate, and it may be very difficult to create a space, much less a culture, that can be reliably cozy.
A conflict-driven culture may reach a successful local maxima, but there is a cost.
Though fans of conflict may find this surprising, openness actually suffers as non-combative people put up protective walls.
People who are unable to function in this kind of environment will either fail to perform (“He was quiet and didn’t volunteer many ideas”) or leave.
Conflict stirs feelings of constant stress and anxiety so people never end up work on the Not Urgent but Highly Important tasks of self improvement.
Many forms of conflict enforce tribal norms resulting in uniformity of both people and ideas. This is particularly poisonous to the ideal of building a diverse workforce.
Changing a conflict-focus culture takes a dedicated and determined effort with vocal leadership support. If you have, or want to have, a diverse team that includes people with different backgrounds and different motivations, it may take some explicit signaling and welcoming in order to build the trust required for people to feel cozy and earnestly engage.
The challenge of too much coziness
Lastly, it is possible to go overboard or cross boundaries in attempting to establish coziness.
Forcing Intimacy: Intimacy requires both parties to feel comfortable, and pressure is inimical to it. Remember that social cues such as call-and-response can help gauge willingness to proceed, and ensure that opting-out of coziness is low-pressure too.
Lack of dissent: There can be an escape into coziness where people are not willing to address difficult topics for fear of upsetting a pleasure situation. Hedonic coziness is a lesser state that lacks the psychological safety necessary for open and honest conversations. Be sure people can say what they need to say and if not, you need to do some work and have some clear conversations about how to work together better.
Not being honest about the stakes or impact of a power differential: Consider the impossibility of hosting a truly cozy job interviews. One participant (the interviewee) cannot feel safe when the course of their life is at stake. Although elements of welcoming and pleasantness can help mitigate other discomforts, coziness shouldn’t be used to manipulate and lull candidates into a false trust.
We encourage developers to build cozy games. If you’ve made it this far, you’ve seen there is a deep well of emotionally resonant design patterns you can use to make almost any game cozier. And on a purely pragmatic level, broadening your game’s appeal means more sales for the same effort.
However, as we went through this process, we also started to see coziness can be treated a positive philosophy for driving meaningful change in the world.
Coziness as a radical philosophy
In a time of increasing divisiveness, othering, and rampant fear and sensationalism, we propose that coziness – in that it provides safety, softness, and the satisfaction of needs – is in fact a powerful and necessary subversion of current culture.
In that coziness sees one’s needs provided for, it is anti-capitalist, and supports the comfort and care of all people.
In that coziness enables us to express our whole selves, without ramification, it is healing and validating in a hyper-critical world.
In that coziness encourages the positive resolution of conflict, it is deeply mending to our societal divides.
In that coziness elevates the softer, gentler aspects of life, it calms a threat-weary population and brings relief from fear.
In that coziness creates spaces of plenty, it provides focus amongst chaos and allows us to embrace our highest level and most human pursuits.
In that coziness offers us spaces of choice and support, it allows us to explore our underlying, intrinsic motivations.
Coziness is healing, validating, collaborative, and kind. Coziness is relief and refuge and gentle opportunity. In a harsh, demanding ecosystem of cynically generated needs and unending urgency, coziness creates comfort, and freedom, and a path to a better world.
A cozy invitation
The following is an Invitation to radically cozy game-making, which you may send (edited at will) to colleagues:
Dear designer whom I care for,
I wish for you that game-making be a refuge from the storm. I take joy from the games you make, and I hope you feel fulfilled when you make them. As a colleague, I want you to feel safe to express your inner self, to take creative risks in your craft. As a friend, I wish that you can escape the ever-present hurry and pressure of our industry and world, into a restful, healthy practice.
If you feel comfortable, I invite you to make a game that reflects those moments in your life that were meaningful, where you were content and cared for. I invite you to make a game that offers moments for players to reflect and be at ease. You don’t have to show it to me; you don’t have to share it with anyone. But I would like to be a companion in the journey towards cozier games, and I think others would, too, if you would have us.
It’s difficult and slow and I’m probably asking a lot from you. But if you try and fall short of your expectations, please know that I will still support and celebrate you. I care about you, and your work is but a small part of what makes you wonderful.
Good luck, if and when you’re ready,
— (your signature)
Thank you so much for reading,
Chelsea, Daniel, Jake, Dan, Tanya, Squirrel and Anthony
In November of 2016, a small group of veteran game designers got together in a remote portion of Texas for a think tank called Project Horseshoe. Our workgroup dug deep into how design can help build meaningful relationships within games. You can read the other reports here: https://www.projecthorseshoe.com/reports/
Our group consisted of:
Daniel Cook, Spry Fox
Yuri Bialoskursky, Electronic Arts
Bill Fulton, Microsoft
Michael Fitch, Betterrealities.com
Joel Gonzales, Wargaming.net
In many online multiplayer games, players enter as strangers and remain strangers. Due to a variety of unquestioned logistics, economic and social signalling choices, other human beings end up being treated as interchangeable, disposable or abusable. We can do better.
When we throw players into a virtual world without understanding the cascading outcomes of default human psychology, we are little better than an unethical mad scientist replicating Lord of the Flies. As game designers, we’ve been building destructive dehumanizing systems. We should take responsibility for the bullying, harassment and wasted human interactions that inevitably results.
Let’s instead design games that help strangers form positive pro-social relationships. New tools
There’s a mature body of research going back to the 1950s concerning how to create systems and situations that facilitate positive relationship building between strangers. Given the right context, people will naturally will become acquaintances. And a smaller number will become friends.
Much of this research focuses on describing how friendship forms in observed communities. Or how an individual might go about developing friendships. We propose intentionally using these psychological insights in a highly scalable online game designs to engineer potentially millions of healthy player relationships. Many games accidentally separate players and decrease the chance of meaningful human contact. What if we design our games to be more socially meaningful?
We can’t force two people to become friends, nor should we want to. But we are in a unique position to build systems that create fertile ground for friendships to blossom. And by carefully nurturing positive relationships, we can simultaneously avoid naively birthing poisonous cesspools that actively fosters hate.
This paper cover a simple design checklist based off well supported models of friendship formation. Put it into practice and you will create games that build stronger player relationships and stronger communities. In addition to making the world a better place, your games will likely have better retention and improved monetization because you are creating value for your players that speaks to their deeply human psychological needs.
To build friendships, your game should facilitate four key factors. When these are present, friendships tend to form.
Proximity: Put players in serendipitous situations where they regularly encounter other players. Allow them to recognize one another across multiple play sessions.
Similarity: Create shared identities, values, contexts, and goals that ease alignment and connection.
Reciprocity: Enable exchanges (not necessarily material) that are bi-directional with benefits to both parties. With repetition, this builds relationships.
Disclosure: Further grow trust in the relationship through disclosing vulnerability, testing boundaries, etc.
What sort of friendships does this model cover?
We define a friend as another person with whom you have a mutually beneficial long term relationship based off trust and shared values.
There’s a spectrum of friendship ranging from acquaintance to best friend. Different cultures have very different definition for what it means to be a ‘friend’. Americans for example, tend to call relatively distant acquaintances ‘friends’ while a country like Germany may reserve the term for two of three closest relationship. In this paper, we treat friendship as a spectrum that ranges from stranger all the way up to deep intimate friendship.
In particular, we focus on the transition from stranger to acquaintance. This is the step that most often falters in modern game designs.
What types of games can use this friendship model?
For the purposes of this paper, we are interested in a specific domain:
Online: Players are not in the same physical space.
Mediated: A computer mediates all interactions between the players. Rich in person channel of communication like one might find in a board game or sport are not available.
Synchronous: Players are interacting in real time via keyboard, mouse, mic, controller, voice, emote, etc.
Other types of games benefit as well, but they have their own complexities that are outside the scope of this essay. Local multiplayer taps into high bandwidth interpersonal communication and often occurs between existing acquaintances. Asynchronous multiplayer relies heavily on a strong reciprocation loop to compensate for a weak sense of proximity.
What is Proximity
The first factor to consider is Proximity. Social proximity is the likelihood of players seeing and having the opportunity to interact with one another in a game space. This space can be virtual like a chat room. Or it can be spatial like a room in a game match.
Think of proximity in terms of simple logistics. If players can’t see one another they can’t initiate the reciprocation loops and any friendship is impossible. Without proximity, friendship is impossible. In some sense this is an obvious requirement, yet in many games we create strong barriers to simply being together.
Concepts for Proximity
A high density game is one with a low amount of distance between players so they are likely to bump into one another. A low density game is one with a large amount of distance between players. Often we design in terms of ‘number of players’ and independently think about ‘size of map’. However, density, the ratio of these two factors, is often the key attribute to balance.
Frequent serendipitous meetings
Due to high density, people are likely to ‘randomly’ bump into one another repeatedly. This creates exposure and familiarity between strangers. Meeting the same person again and again feels like magical fate, but it is primarily the outcome of well designed statistics and logistics. Crossing class, race and age boundaries
The single most effective method of creating friends that cross traditional social boundaries is to put two people together in close proximity. People form friendships with those that are nearby and if their choices are limited, they’ll form choices with those that would not be their instinctive (often biased) choice.
Connection to other requirements
Reciprocation: Being the in same space yields parallel play. This eventually leads to low cost reciprocation loops between players
Similarity: Being in the same place lets players observe similarity. Note that in studies of friendship formation, being in close proximity is a stronger predictor of friendship formation than being alike. However, the impact of distance falls off quickly and once players start rarely being close together, they will start forming friendships predominantly based off similarity.
Other players need to be identifiable. If you see someone a second time, would you know it? Names, unique clothing, identifying animation or abilities all help players understand that they are seeing the game person again and again.
A consistent persistent space that players can join and then later rejoin provides a means for players to find and associate with players that they deem worthy of friendship. There are many variations of this:
Dedicated servers: Something like Minecraft has a vast number of player run servers. These create memorable locations tied to permanent communities.
MMO shards: A player is associated with a particular long lived world instance. This creates a cohort of players that advance through the content together and then to run into one another frequently.
Persistent sessions: In match based games, you can keep players together when the next match begins.
Chat rooms: A common chat room or group where players seen names also acts as a persistent space even though it is completely abstract in nature.
You can increase density by taking players that are spread across time and incentivize them to all show up during the same time. Many games suffer from low player density because concurrent players are spread across multiple time periods. A play session may only be 30 minutes (or less in the case of mobile) so even if you have 1000 players who play on a server, your concurrent player number would be less than 10. Think of your game in terms of concentrating player density across time.
Shared events help this situation by asking those 1000 players to all show up at 8pm on a Saturday night. Suddenly, your sparse world is full of players.
A shared event that reliably occurs every week at exactly the same time helps create that repeated interaction that is common with persistent spaces. Having a clearly published schedule of recurring events is a great method of increasing density and serendipity.
If you’ve got a matchmaking system, it can give priority to those that you’ve played with previously. Or if you have an MMO shard, the game can seed it with those that are started at roughly the same time period. The result is a group of players that are moving through the content together.
Opt-in persistent social groupings like a guild or a clan are another self selected space for those that are further along in their friendships. It is often a greater commitment to join these groups, but the result is frequent interactions in denser social spaces.
Guild halls create a small space for guild members to run into one another more frequently.
Guild chat focuses conversation between guild members
Guild targeted boss events provide focused group activities.
Often we create instances or servers, fill them up with a cohort of players and then fail to remove the room when players inevitably churn out. This leads to a large number of low density servers and weaker friendship opportunities. Elastic instancing has the stated goal of maintaining an optimal density of players.
On demand server creation: New instances are only created if the concurrency is high enough. When new players start playing, we fill them into any open slots on current servers. When there isn’t enough room, we create a new server.
Server merging: If the population of server drops below some optimal threshold there is a mechanism for merging server population. This takes a huge variety of forms based off the game type. This is easier in non-persistent game since you can merge servers when each match ends. This is more difficult in persistent games.
Hubs and choke points
Players move around in many games. If you create a location they need to return to or move through on a frequent basis, they tend to run into other people more often. Think about your game in terms of how players flow through it. Hubs are central areas in a hub and spoke system that players must pass through. Often utilities like stores or guild features are located in or right off of a hub area. Choke points are similar to hubs in that players flow in from a broader lower density area or set of areas through a narrow location on their way to somewhere else.
Lack of identification
Many games weaken identifying signals. For example, true friendship is impossible in a game like Journey because the identity signals are intentionally weakened. People swap in and out of a given game session without the player realizing that their partner has changed. Some MMOs have a fixed set of class art. You are a wizard or a fighter and all wizards look the game. This short circuits the player’s ability to identify their friend.
Similar to lack of identification, some systems allow users to change their identifying characteristics on a regular basis. If the primary method you use to know if you’ve played with someone is their user name, and the system allows for freedom to cheaply change that user name, other players will not be able to track changes across time.
An important psychological considerations for persistent spaces is that players should have a strong belief that they will have future interactions with the people that they see. There’s research that suggests we have two sets of social norms: One for real people and another for ‘disposable people’; those that we’ll never seen again. These behaviors may be very close in the polite individual, but they can end up being negative and dismissive. If players see others a disposable due to proximity being low and repeat encounters uncommon, they’ll tend to act worse towards strangers. This leads to a downward spiral for the much of the community.
Large group sizes
Very large group sizes greater than Dunbar’s number (80 to 150) are difficult to comprehend. The overall principle is that systems need to operate at human scales (numbers and quantities comprehensible by a biological human.) We are ultimately building systems for people and homo sapiens have strong cognitive and physical limitation that we need to take into account if we want to produce the experience we desire.
Don’t fall for the engineering or marketing mindset that says bigger numbers are better. A guild system that allows for 100,000 members is functionally worse in most cases than a guild that is capped at 150 because you’ve likely reduced social density, created swaths of disposable people and generally built a system that doesn’t fit with human biological constraints.
Very dense group sizes
With dense crowds of people it is difficult to see a person, difficult to identify a person and difficult to track a person. In Realm of the Mad God, we had dozens of people piled up on top of one another. This made for a great feeling of being in a crowd, but it was hard to actually see your friends.
Many gameplay modes
When games create many gameplay modes, they create more game play surface area over which players are spread. This makes any sort of match making more difficult and results in lower concurrency for each mode.
A better pattern here is to rotate through the game modes so that everyone is playing the same mode at each point. Or tie modes to timed events. Players still get some variety, but the populations aren’t split up.
Frequent splitting of groups
In match made games, the match ends and players may be thrown into the matchmaking again. As a result they are matched with completely new players and thus any burgeoning relationship is extinguished.
Separating friends by skill
Games with a heavy skill component may match players in different skill categories. Or players that were in a cohort together are split because one player proves to be highly skillful.
There are many forms of gameplay that are enjoyable to mixed skill players. Cooperative games, team vs team games, party games, games of chance or discovery, build or creative games all work. Try making one of those.
Separating friends by progress
Games that focus on leveling and power acquisition often have very large power differentials between players. If two friendly players want to play together, they may not be able to because either the newbie is so weak as to be useless to the higher level player or the higher level player gets so little reward from helping out the newbie that the friendship is the waste of their time.
Barriers to repeated play sessions
A difficult aspect for many match-based games is to get the same people to play together again a second session. This point came up repeatedly as the key challenge in forming friendships within a typical multiplayer console title. Treat putting players together for multiple matches and multiple sessions as a critical design goal.
If we automate or manipulate these proximity processes too heavily, players may feel that their friendships are artificial and therefore less valuable. Heavy handed matchmaking, ‘friend suggestions’ or automated reciprocation loops cause players to imagine that their relationship is formed for purely utilitarian (or nefarious) reasons and trust in other players drops to some utilitarian level. To be fair, this is mostly a theoretical concern since as of this writing most games encourage friendships lightly or not at all. But as our techniques become more broadly practiced, this issue is worth watching out for.
What is Similarity?
The second factor to pay attention to in our friendship formation model is similarity. Similarity is how closely we share various aspects of our personality and background with another person. The more similar one person is to another, the more likely a friendship will be initiated. On first sort, we judge another person based off their visible traits, their affiliation with known social groups and any values we infer based off our stereotypes. With increased contact, we also filter by communication style and personality (see OCEAN).
If social proximity is the barest logistical necessity to form a friendship, similarity is the the criteria by which we decide who we will invest in further out of all currently available options. We can only invest in so many friendships so we filter out dissimilar people. Without similarity, friendship is possible but unlikely.
If you believe in the value of multiculturalism or other philosophies that celebrate human variety, this topic may raise an eyebrow. However, people’s reliance on similarity to filter others is one of the more strongly reported effects across decades of study. However, we see this as a tool to create rich social tapestries from complementary backgrounds and not some preordained reinforcement of the current social order. Designers in virtual environments have immense control over what players see as similar. We should use that power to mold the societies we desire.
Concepts for Similarity
Similarity lowers the cost of social negotiation
Shared contexts, values, and identities bring along with them social assumptions, common language/vocabulary, and models for interaction. It is easier and quicker to negotiation a cooperative, mutually beneficial set of norms if two entities share a strong common base.
Perceived similarity matters more than actual similarity
Humans are remarkably poor judges of others. So they tend to rely on superficial details to determine if someone is actually similar. In studies, this perceived similarity is a greater predictor of long term friendship than objective measurements of similarity.
Similarity in virtual environments can be generated
In the real world, similarities are often difficult to change. Players bring along much of human history when it comes to various religious, racial and language differences. Surfacing that baggage immediately typically results in players using it to filter out possible friends.
Luckily, in virtual social spaces, the specific simulated cues that each player sees can be intelligently curated, often on a per player basis. The biases of the real world need not damage a first impression inside your game.
Self reports are often highly inaccurate
The traits that people say they look for in a friend are rarely what they actually use to filter out potential candidates. For example, comparable physical attractiveness and comparable intelligence correlates highly with friendship formation attempts. However, polite society looks down on stating that you are friends with someone in large part because they are devastatingly handsome. Much of the machinery of detecting and acting upon similarity is either sub-conscious or impolite to discuss publicly.
The earliest element players latch onto visual similarity. Titles, achievements, badges, equipment, names all work. Think of your similarity signals in terms of depth engagement.
Glanceable: What do people see in the first 200 ms? This is the most impactful location for leveraging visual similarity. Silhouettes, colors, large scale animations are used by players to judge one another.
First session: What do people see in the first play session?
Multi session: What addition signals are revealed via special abilities or viewing the player in unique, uncommon situations.
Faction Identity and Conflict
A shared tribe create a strong feeling of acceptance. All social groups are composed of a core shared identity, a boundary that define who is outside the group and a set of others who are known to be outside the group.
Define a group identity. Determine how one player will be able to quickly display and observer membership. For example, in World of Warcraft, Alliance players all come from a specific set of racial classes. There are dominant color schemes and silhouettes that makes quick, accurate identification easy.
Define the Others, those outside of the group. In World of Warcraft, there is a clear opposing team, the Horde. They are shown in faction specific lore to be less worthy than the player’s current tribe. Differences are accentuated.
Define an expensive boundary for crossing between groups. This acts as an economic wall that encourages any resources to be directed back towards the player’s current tribe. Enforcing systemic costs for interacting with the Other results in increased polarization. If you are in a PvP situation and an enemy group can kill a player, they will naturally seek the safety that comes from belonging to a friendly tribe.
The result of a strong tribal identity with a clearly defined Other often results in strong friendship formation. They clearly understand who they could make friends. And they have clear reasons to build those friendship in the face of an organized adversary that would swamp an unconnected individual.
Note that factions lead to some of the ugliest aspects of human culture. The very aspects that make tribes a powerful organizing forces also result in hideous abuse and bullying of those they dehumanize. At potential fix is to use AI opponents in a PvE conflict. Or in survival type games, use environmental obstacles. Often it is better to Other a digital illusion than a real human being.
A shared experience also triggers similarity. For example, when players go through a boot camp or hazing ritual as an introductory experience, they can refer to that as a common moment. In an MMO, players might go through a particularly difficult dungeon and wear a token from it as a sign of pride. The higher the cost of the hazing, the more long term the resulting self identification. There’s an element of cognitive dissonance at work. A player thinks “If I invested in X, it must have been worth it.”
Hard-fought matches, difficult raids, long periods spent grinding or leveling all can work as shared experiences. Near-wins are more emotionally intense than a clear win. Brutal losses can also create definitive bonding experiences. Many religions use repeated stories of shared persecution that function as a means of increased bonding between members.
Players who are players the same class or role within a game have a potential affinity. Or players that are on the same quest or have a shared public goal. In the game Realm of the Mad God, bonding was often as simple as two players shooting at the same enemy.
Humans look to other humans when determining how they should act. And most of the time, they give greater weight to those members of their community that have high status, aka celebrities or leaders. By highlighting celebrities within your game, you create a template for players to to compare both themselves and strangers to.
Two things will happen. Players will start to conform to the ideals shown by the celebrities. And they’ll see others that conform in a positive light. Essentially the celebrities create a beacon of artificial similarity.
Publicly showcase players that fit the team’s desired ideals. Name, avatar and the emotional reason why they are important are key elements. Interviews, viral clips of how they play and other concrete elements help cement the norm you are trying to promote. Think of it as an advertisement that tells players how to act by giving them an example.
Beware of showcasing only top players on a leaderboard. For example, you might choose to emphasize norms like generosity, self sacrifice or community service. Leaderboard players can accidentally showcase negative traits like aggressively uncontrolled competition.
Give opportunities for other players to mimic dress, class, abilities of highlighted celebrities.
Surface real world similarity can lead to premature disclosure
For example, highlighting that a player is a woman in real life might result in a spike in abuse inside the game. The intent may be to encourage women to find other women, but if it occurs too early in a relationship, the overall impact is negative. See section 4 on Disclosure for more details.
Exclusionary group dynamics can calcify and reinforce the negative consequences of othering. Systems that create homogenous groups in opposition to other groups result in the following issues.
Poorly met internal needs. As social energy is dedicated to maintaining the group cohension, less energy goes towards serving individual needs. This results in churn.
Poor onboarding of new players. The barriers to entrance into the group actually hurt its growth since very few pass the increasingly rigorous purity tests. This result in the group’s membership churn not being replaced so you get declining player populations.
Stagnation. Rigid social structure means the pure group has difficulty adapting to environmental and social changes.
Bullying and abuse of those deemed outside the group.
Our third factor in friendship formation is Reciprocity. Reciprocity is fundamentally about using iterative exchange to negotiate social norms and build trust. If Proximity and Similarity are filters on who becomes friends, Reciprocity is the mechanical engine that make friendship function.
Any reciprocation loop can be analyzed as a simple turn-based game between two players. Use these steps to talk about the reciprocation loops in your game.
Player A moves first
Player A performs an action that targets Player B
This action has cost to Player A: This is an economic cost in either tangible resources or time, attention, or social status.
This action has a benefit to Player B.
Player B observes feedback that result from Player A’s action
Player B updates their mental model of Player A. This includes a summary of historical interactions, aka a relationship.
Player B weighs the benefits of future action and makes a choice on what to do next.
Player B responds
Player B performs an action that targets Player A.
This action has a cost to player B
This action has a benefit to player A
Player A observes feedback that results from Player B’s Action
Player A updates their mental model of Player B
Player B make a choice.
The loop restarts.
That’s a single iteration. Reciprocation loops are typically repeated multiple times, accumulating economic and social capital to both parties. Learn to see them. Walk through them step by step to diagnose exactly where your social loops are failing.
Concepts for reciprocity
Exchange is substantially non-material in nature
The language of reciprocity comes from the world of economics. One might imagine that friend formation is reduced to a merely capitalist construct of exchanging material good and weath. Nothing could be further from the truth.
A valid reciprocation loop could include exchange of any of the following:
Recognition or attention: A shared glance is a reciprocation loop.
Common experience: A shared experience in which both react and see one another react to the same situation is a reciprocation loop.
Conversation: Two people talking is a reciprocation loop.
Complementary roles: A tank and a healer in an MMO exercise a form of economic specialization that costs neither side anything material. This ends up forming a reciprocation loop.
Medium of exchange
In order for reciprocity to function, there must be a medium of exchange, there must be a bidirectional flow between both parties. This covers a huge range of possible interactions.
Chat, Voice, Video
Visual space with movement
Both sides need to feel that they benefit from the relationship, if not short term, at least over the long haul. This need not actually be factually true. See asymmetry below.
Friendships fail when exchanges aren’t appropriately reciprocated
Each time an overture is made to another person and that overture is not returned, the relationship between those two becomes more distant. A failed reciprocation can take multiple forms
Player B ignores the overture. Communication is messy, so if it was low enough cost, Player A may attempt again.
Player B reciprocates, but does so inappropriately. They give too much or too little. They give the wrong sort of response. The negotiation of norms is going poorly and the relationship may end.
Player B explicitly rejects the overture. The relationship momentum falters and may degrade.
Player B uses the overture to harm Player A. Any relationship begins to degrade rapidly.
You need to design unreciprocated exchanges as much as you need to design reciprocated exchanges.
You also need to consider that human find making overtures risky and rejection emotionally painful. We are wired to form friendship and when we are rejected, it is one of the deepest cuts a person can experience. So when you design for failure, consider how to soften those failures. Consider tools like: Kindness-focused language in feedback dialogs, reframing the rejection, deniability or immediately redirecting to other relationship opportunities.
The good news is that lapsed friendships that lack negative exchanges are easier to restart than new friendships. You have a common base of social norms already negotiated. This acts like a foundation of similarity in boosting re-engagement of the reciprocation loop.
Self reporting is biased
This is a tricky thing to ask friends directly about economic aspects of their relationship. Friends tend to downplay any short term or medium term benefits so as not to jeopardize long term relations. Think of it in terms of game theory using the following strategies:
Someone signals short term interest: If one participant signals that they are reciprocating for short term benefit, the other participant may try to optimize for as much benefit to accrue to themselves before the relationship ends. Minor low cost exchanges are now weighted against the relationship endpoint and may not be initiated or returned. With this strategy, friendship quickly collapses.
Both signal long term interest: Alternatively if both participants signal that they are reciprocating for long term benefit, the minor exchanges are worth it when compared against any benefit that may pay off in the future.
Someone falsely signals long term interest: Now if one person is engaging for short term benefit and one for long, it still pays to signal that you are invested long term. Since if you signal your actual short term interest, the relationship enters into a failure cycle and collapse before any benefits accrue.
This dynamic substantially biases any self reporting around friendship.
Symmetry and asymmetry
When friends describe their friendships, they take great pains to couch them as symmetrical. How one person benefits is always equal to how another person benefit. Due to the fact that self reporting on friendship is biased, our model of how friendships should be balanced is highly questionable.
Many social relations and exchanges are in fact inherently asymmetrical but reciprocal (parent/child, student/teacher, etc.). Many friendships are initiated by low status individuals seeking a relationship with a higher status individual.
Escalation of costs as friendship deepens
Friendships start out with very low cost overtures and then as each exchanges is reciprocated, the average cost increases.
This makes sense from an investment perspective. Early on, a person doesn’t know if the stranger will reciprocate. It makes economic sense to invest in many very low cost exchanges in the hopes that one of them will pay off. If most of them fail, it is fine since the cost isn’t high.
Later on, a person has established a history of reciprocation. They can be relatively confident that a long term associate will respond in a predictable fashion even if the exchange is of higher value.
Some friendships eventually falter as the cost of each exchange grows too high. But some will continue escalating to the point where nearly no cost is too much. This is seen in marriages, families, and some long term friendships.
Design a friendship progression curve for your game.
Put less expensive interactions at the beginning. Encourage players to build up skills first in safe spaces. Most League of Legends players play single player or cooperative PvE games first before they risk competitive PvP.
Defer high time and resources commitment interactions later. Raids open up after you’ve been playing the game for while.
Limited number of deep friendships
Since deep friendship come with expensive long term reciprocation loops, most people can only afford a limited number. Dunbar suggest that there are biological limits on how many people we can form relationship with and that these cluster in ever decreasing circles of friends.
Pick which groups you are designing for.
1500 people: People whose face your recognize. These are lowest investment relationships.
500 people: Acquaintances. People whose names you know.
150 people: Casual friends. People you hang out with occasionally. This is this largest typical group size.
50 people: Close friends. The sort you might invite to dinner.
15 people: Confidants. The sort you’d tell intimate details of your live.
5 people: Close support. These are highest investment relationships, but you only get a few.
In a game with spatial positioning of an avatar (like Diablo or any FPS), the mere act of trying to stay close creates an early stage reciprocation loop. A player moves to a location. This is an invitation. Another player responds by moving in the same direction. Such move and response actions are very low cost on the part of the players so they aren’t risking much by engaging. This is the primary form of reciprocation in a stranger-friendly game like Journey.
In games with rotation, players can also face one another. This is another social gesture that forms an early reciprocation loop.
Emotes or signalling
Games often include emotions like a wave or dance animation. One player will start with a gesture and other nearby players will either repeat the gesture or iterate on it with some contextually interesting variation. This may turn into a synchronized dance session or a private emote-based language that was negotiated over multiple play session.
Chat is one of the richest methods of building social reciprocity. By tapping into language, chat enable socializing, humor, information exchange, the establishment and reinforcement of norms.
Beware premature disclosure (see disclosure below)
Exchanging virtual goods allows for a wide range of material economic transactions. Players can become a reliable supplier or a reliable purchaser. In our capitalist culture, this form of relationship is familiar to many and thus players easily fall into the appropriate roles. Trade also opens up gifting and twinking, two practices in which virtual goods are exchanged for status or goodwill but not currency.
Negotiation heavy trade such as barter is often as much about having a conversation that builds a social relationship as it is about economic efficiency. Be aware that highly efficient, low conversation trade systems such as auction houses can actively remove reciprocation loops from a game and thus damage the social foundation of your title.
When players can help out other players, they will often fall into patterns of reciprocal helping one another. One player covering another as they rush a point. In turn that player is healed by the person they helped. This tit-for-tat occurs even when players have symmetrical abilities.
An extension of mutual support is creating specialization for each player. Unique roles that create dependencies result in reciprocation loops. The MMO trinity of Healer, Tank, DPS class naturally clump together in order to increase their overall efficiency.
You can also build asymmetric hierarchies of mutual dependency. The MMO Asheron’s Call implemented a system where new players could declare their allegiance to more experienced players in return for help within the game. This created an improved new user experience and in return, the patrons earned a portion of their vassal’s experience. The patrons could in turn be vassals of higher patrons and everyone had a huge impetus to ensure that those below them did well. (https://asheron.fandom.com/wiki/Allegiance)
In the Xbox version of Shadowrun, players could resurrect dead players and earn a portion of their kills. However if the patron player died, all resurrected vassals would also die.
Face to Face interaction
In many real world studies simply being in the same space isn’t enough. You need to see another person’s face and be able to respond via glance or micro-facial expressions. This is not typically captured in games, but it likely will start to show up as VR and facial scanning technologies become more prevalent.
Zero Sum interactions
When there are limited resources in play, a player is forced to choose between spending a resource on a relationship or keeping it for themselves. Early in a relationship, the perceived cost and sense of loss aversion makes players selfish so they are less likely to initiate experiments. Instead use non-zero sum interactions early on in player relationships and carefully introduce low cost zero sum interactions once players have formed stronger bonds.
Ideally, zero sum interactions are opt-in and used as an explicit means of taking a relationship to a new level. For example, a player might choose to pay a fee to help level up a guild. The currency used for the fee is useful elsewhere for selfish purchases so the player ends up signaling their sacrifice and dedication to the group above their individual needs.
In systems that allow the trade of material goods, be wary of tempting players to scam one another. Players with weak bonds will see an unsecured trade as an opportunity break trade promises by taking something without giving a promised item in return. This reduces trust in the environment and makes it difficult for all players to form friendships.
A better system is a secure trade window that requires both players to confirm their promise and then reliably automates the exchange in the background. Ask yourself: Do the designed methods of player interaction facilitate trust? Do they protect against the bad actor’s worst instincts?
Lack of predictability
The bigger picture here is that good social norms, the foundation of friendships, rely on predictability. We build a model of how another person will interact and then base complex and expensive plans how know that person will reliably react as expected in a given context. When player behavior is encouraged to be random or unreliable, it is much harder for players to form social norms. You perform a gesture toward another player and get something random back instead of converging on a predictable social pattern.
For example, if your guild just killed a huge boss, there might be a social norm that you divide the loot. However, the system is left open ended and instead a single player takes the loot and leaves. The very player freedom that exists at that decision point results in highly unpredictable outcomes. Some games can survive this (games like Eve have various forms of community censure that turn turncoats into economic and social pariahs). Most games treat these moments as incidental social friction not worth polishing out. However, these ‘minor’ issues slowly poisons the whole community’s ability to make deep long term friendships.
Extreme power differentials
In many leveling-based system a large power gap forms between new players and old players. This economically segregates communities and in extreme examples there is nothing that a powerful player needs or wants from a low power player. The game design has created an high economic barrier between two humans that is totally artificial and unnecessary.
A good tool for solving issues like this is to ensure opportunities for economic and social dependency between any player in the game independent of power level. Create ties between players that make it beneficial to become friends. Hunt down and eradicate all systems that force players to dismiss one another as useless.
Over designing for freeloaders
A freeloader is a player that benefits from a community without contributing materially to its success. American culture specifically puts a large amount of effort into reducing freeloaders, often at the expense of community. By policing and punishing the few, the community becomes more selfish and less likely to form positive friendships.
Most community systems, especially ones based on non-zero sum resources, are remarkably stable in the face of moderate free-loading. Players may complain due to their existing cultural biases, but the end result is a stable and happy society. Ask yourself if the freeloaders are actively hurting other players. If they are, see if you can shift the design toward non-zero sum interactions. If they aren’t, feel free to ignore the problem and stop wasting precious design cycles on a non-problem.
High initial interaction costs
If an early interaction is too expensive, players won’t initiate the reciprocation loop. One solution is that the system can pay the cost early on to prime the pump. For example, social games allowed players to give gifts to other player, yet those gifts cost the giver nothing. They were created from thin air.
You do need to be careful as then people can think that they are being paid to be friends. When priming the reciprocation pump, Give the absolutely minimal, non-distorting incentives. Slowly increase what how much the system subsidizes that first interaction and look for behavior shifts at each cost increment.
Our final factor in friendship formation is safe disclosure. As players get deeper into a relationship, the nature of the reciprocation loops change from superficial mirroring to riskier trust building interactions. Key to the creation of deep friendships is the ability to friends to disclose new or secret information to another person.
Games that lack the tools for disclosing personal info between two people will never facilitate deep relationships. They may never even facilitate shallow relationships since players see that there will never be a long term future for any relationship they form in the game. However, disclosure is a highly risky action and teams will often try to cut it from their designs. Sharing information before a relationship is strong enough can result in broken or antagonistic relationships.
Concepts for Disclosure
Risk is inherent to disclosure
When a person discloses personal information to another player, there’s always the chance it will break the relationship. Up until this point, players have typically been performing low cost, non-zero sum interactions that are tightly constrained by game systems or social convention. Personal information brings in external history, gender, age, religion, race, values and other delicate factors that may cause the other player to fail to reciprocate or otherwise pull back from the relationship.
If failure to reciprocate hurts emotionally, failure to reciprocate a personal disclosure hurts substantially more. Disclosure is a laying bare of the soul for many and the fear of rejection is immense. If your game moves relationships to this point, you are making more than mere entertainment. Your game facilitates moments that uplift or scar players in formative ways.
What constitutes disclosure is highly specific to a given person and relationship
The exact contents of a ‘disclosure’ depends on what might threaten a specific relationship at a specific stage with a specific set of participants. So we are dealing with an ill defined concept. However, the participants have a refined sense of what constitutes a disclosure so it is best to let them decide what to disclose and when.
Rich communication tools
The concepts involved in disclosure cover a huge breadth of the human experience. Emotes aren’t enough. To enable relationship building levels of disclosure you need to give players rich communication channels.
There’s substantial discussion within many design teams on whether or not to include chat. The costs are easy to list. Chat that happens too early in a relationship can trigger unpleasant early disclosure and abuse. It can be used to spam players. Children using chat are at risk of being contacted by sexual predators. There are legal and moral considerations. Developers need expensive moderators or filters to manage these downsides. Many modern teams look at this list and run as far away as possible.
However, when you cut out chat, you are gutting your long term community. Players will remain strangers and never form long term bonds with one another. Every relationship is at best like a game of Journey where anonymous people have fleeting encounters that never result in any long term impact or friendship. Remove chat and you remove 95% of all positive social behavior.
When we build human systems, we should be wary of building systems that filter out our humanity.
Luckily there are ways to have your cake and eat it too.
Make the tools for disclosure opt-in: Unlock chat between two people after they both agree they want their relationship to go further.
Give rich tools for opting out: Give players robust tools for filtering and blocking other players that abuse chat.
Quiet Opportunities to Talk
Relaxed environments where players are doing some low intensity activity will naturally result in players chatting with one another. In action games, players are often alway highly engaged with the moment to moment gameplay. There’s no space to chat. As a designer, you need to explicitly carve out these slower moments in your game pacing.
Common examples include
Healing time in MMOs.
Lobbies in FPS
Post match chat
Private chat channels
Guild chat channels
Mechanics that encourage disclosure
Simple interactions derived from party games can provide people with personal information about yourself like humor, status, competence, history, etc. After all, a party game is just a mechanic to encourage personal disclosure.
Disclose or Punish: This is a typical truth or dare type activity. Note that even though it encourages a player to disclose, they always have an out so they aren’t forced to prematurely disclose something.
Safe spaces: Create spaces where anything can be said but where acting on that knowledge outside the safe space is highly inappropriate. Confessionals and psychiatrist sessions follow these rules. In Japan, people are encouraged to go out drinking with their co-workers. It’s an established norm that when a person is drunk, you’re not meant to take what they say personally, which allows people to provide feedback without repercussions.
Open reciprocation loops: If you give something a person and then request voluntary disclosure, they are likely to participate in order to complete the reciprocation loop.
Mechanics that loosen inhibitions
These are less common in computer games, but very common in real-life
Wearing silly outfits: Anything that shifts a player out of their current social context and identity lets them experiment with new roles.
Alcohol: Moderate drinking tends to loosen tongues. Note that this may risk premature disclosure.
Physical interactions: Many icebreaker games involve breaking through personal spatial boundaries. This rapidly creates feelings of intimacy that otherwise might never appear.
Commitment activities: Have a player do something rather difficult or expensive. Then put them in a place where they invalidate their personal and emotional investment unless they disclose.
Group encouragement: If you can get a large group of people apparently disclosing, then an individual will feel more comfortable with disclosure.
System often discloses information about a person before they’re ready to share that information. The designers often think they are being helpful, but in reality they are forcing reciprocation loop to jump to an advanced stage before trust is built. The result is typically highly negative interactions between strangers.
When a player doesn’t have trust built up with another person, they use any subtle clues to activate stereotypes. Stereotypes aren’t inherently bad; they are merely pre-existing schema that are used in the place of actual experience to quickly determine how to act. However, negative stereotypes end up destroying opportunities to create friendships based off personal experiences with another person.
Example of premature disclosure
Showing real name. Real names include a variety of personal information about country, gender, and race. Let players select their own names or autogenerate a name.
Defaulting Voice Chat to ON: Voice can reveal age, gender and native language. Default it to off.
Showing location. Location can show nationality. Don’t show login location.
Purchased items (in F2P): Showing a player has purchased an expensive item. Or that they have only cheap items. Ensure items that can be purchased can also be gotten through other means.
When a user discloses or has information disclosed about themselves, it may in turn expose any dissimilarities they may have their current friends. This can cause the friends to rethink the relationship.
Bringing in non in-game similarities or relying on default real world similarities
Disclosing non in-game or real world similarities and assuming that they will carry the same weight in the game as they do outside of it
So far we’ve considered a lot of theory about how friendship works and how it might be encouraged within a game. However, in modern game development, it is not enough to theorize and then build something. We also need to measure if we’ve achieved our friendship goals. And then when we inevitably realize we’ve missed the mark, we can use the tools covered in previous sections to iterate towards a better state.
This leads to a vexing question: What friendship metrics can we measure across our games?
We want to measure behaviors that may indicate a ‘friendly’ relationship between players in multiplayer games. Because we want these metrics to apply to most games, we will avoid ‘in-game’ metrics, which would have to be customized to the specific design of the game.
Key concepts in measuring friendship
People playing together.
Co-play: 2 players playing in a WoW dungeon together
NOT co-play: 2 players in different WoW zones chatting
Repeated co-play experience
People playing together after having played together in a previous play session. We’ll refer to the first co-play as co-play(1) and the second as co-play(2). This extends up to co-play(N).
Repeated co-play: 2 players playing in a WoW dungeon together the day after completing a quest together.
NOT repeated co-play: 2 players playing in a WoW dungeon together immediately after completing a quest (e.g., no substantial break between them)
Behaviors that imply an attempt to initiate or lengthen a co-play experience, or that may lead to a co-play experience in the future.
Friendship behavior: Invite to a group; staying in a group to continue co-play.
NOT friendship behavior: Attacking an enemy; healing a teammate
The challenge of strangers
For most commercial games, marketing campaigns used to acquire customers end up bringing in mostly strangers. So any friendship systems need to be tuned at launch to deal with large populations of people who don’t know one another.
There are methods of important friends into a game but often the “Friend Graph” in a social network like Facebook maps very poorly onto the “Activity Graph” of a game, especially a new game.
The challenge of Co-play(2)
Looking across many games (and decades of design experience shared between all the authors) we observed a key challenge: Getting players to go from Co-play(1) to Co-play(2). Matchmaking creates co-play(1) with strangers very well; the hard part is getting people who played together once, to play together another time.
Existing design solutions are weak and teams rarely if ever measure this critical statistic.
Metrics for measuring friendship
How much ‘friendship’ is happening in the game?
This represents an overall metric of ‘how much repeated co-play’ is happening in a game. This metric applies more easily to discrete session-based games (Call of Duty match) than larger game experiences where players may be present but not co-playing (different WoW shards; same WoW shard but different zones). A way of quantifying this:
% of people in a session who the player has played with before. This is a representation of how many ‘familiar faces’ are in a match. If a player has never played with anyone in the match before, that match as a 0%. If a player has played with all the other players, then that match is a 100%. Notes: 1. Multiple matches in a row with the same stranger should NOT count as a ‘friend co-play’ experience. A reasonable break between sessions is necessary to establish whether the co-play experience is intentional. 2. This is a personal property; other players in that same match may have different %, depending on their unique social graph.
Average % of friends across all matches, by player. This a representation of how ‘friend-filled’ a player’s experience is with the game. It can help identify who is having a lot of intentional co-play experiences (playing with ‘friends’ vs. incidental co-play experiences (playing with a constantly different set of strangers). Sudden drops in this metric may indicate an attrition risk (player has lost of a co-play partner).
Global stats can be computed across all player-matches. This is a representation of what the typical player’s experience is of repeated co-play. It is probably most useful to identify the % of players who are at what thresholds of amount of ‘friend-filledness’ in their play experience.
Note: these metrics assume that players are repeatedly playing together intentionally, and were not put together by the system repeatedly.
Measures for pre-cursors of “intentional co-play”
Beyond the above binary metric (intentional co-play: Yes/No), there are some useful pre-cursor metrics for likelihood of intentional co-play in the future:
Have the other on a list. Being on a ‘Friends’ list, or a ‘follow’ list, or guild list, etc. all enable better ability to do other behaviors that are pre-cursors to co-play. However, a large friends list does not necessarily result in more co-play.
Invite the other to co-play. Inviting someone to co-play is (obviously) a good pre-cursor metric. Some additional measures: 1. Co-play invitation gets accepted. 2. Accepted invitation result in co-play immediately.
Attempt to communicate with other. Sending a message, talking, etc. may all increase chances of future co-play. However, some communication does not lead to play (idle chat), or may even decrease the future co-play likelihood (rude chat). 1. Response from other. Communication attempts that receive a response are often (but not always) more likely to lead to a future co-play experience or future precursor behavior. 2. Note: sentiment analysis (algorithm to determine the positivity-negativity of text) is becoming more accurate and faster to do, and so measuring these kinds of precursors is becoming increasingly feasible.
Examples of ‘Friendship’ behavior metrics, in game
Because in-game ‘friendship’ behaviors are specific to the game design itself, it isn’t possible to do a framework that is complete. The list of metrics below is intended to be examples for how to do that.
Gifting. In games where gifting is possible, gifting can be considered either a pre-cursor to co-play, or even co-play itself.
Assisted kill. In games where killing enemies results in ‘assist’ stats, it is possible to determine which player was ‘playing with their teammates’ more than others.
Vote-Kick called. In games where people can kick a teammate off the team, which players call votes on each other, and how they vote are good indicators of anti-relationship behavior.
Reinforcing in-game relationships via displayed metrics in Shadowrun.
The basic Shadowrun resurrection mechanic worked as follows:
Player 1 has a cost to resurrect player 2.
If Player 1 dies, then player 2 dies.
But, player 1 gets half of the money player 2 makes.
Goal: Make transparent system, system is revealed through stats.
Goal: Caused gratitude to be felt. “Did they exchange Packets?”
“Friendship” systems deal with potentially intense emotional territory for players. The concepts we discuss in this paper should be treated with care. You are dealing with real humans and real emotional outcomes. Most friendships formed in games will only be acquaintances
We use the word ‘friend’ but in truth the chances of making a deep friend in a game are very slight. We simply don’t have the mental bandwidth (see Dunbar’s groups) to have that many deep friends. So more likely is that our games are creating shallow networks of friendly acquaintances. However, think of the game as building a friendship funnel that players progress through. It isn’t a bad thing if only a few make it through to each deeper stage of friendship as long as some make it through.
Players may grow wary of overt manipulation
People dislike other people telling them they should be friends or pushing them towards friendship. Software can tell players things about themselves they don’t really want to know. For example, don’t have software define things like a marriage system and then tell two people they should be married. Instead, create opt in systems that encourage buddy behavior. Then give them tools to create ceremonies or the ability to mutually opt into a public badge that say ‘married’.
Asheron’s Call encouraged aggressively trying to recruit a lot of people hoping some of them stick. Backlash risk of this is once newbie finds out what other person was getting in return. “Did you just want me because I’m rich?” causes you to question other relationships. People smell out rubber bands. Create plausible deniability of purely economic behavior when encouraging friendships.
The relationship between behavior and affects
If you have two people who communicate a lot, it could be constant fighting! Be wary of numbers going up if you don’t track why they are going up. The classic example of this in modern times is the dramatic collapse of games at Zynga even though internally key metrics were increasing.
Grief-test your systems
The systems that help people form powerful friendships are very open to abuse. The need for disclosure in particular causes issues, but most of the anti-patterns listed above have some elements of griefing or abuse. Assume players will attempt bad behaviors and have plans in place for when this occurs. If your systems assume humans are always angels, you encourage their demons.
We believe two things when we discuss friendships:
The facilitation of meaningful relationships between other human beings is a noble design goal.
Games are uniquely suited to facilitating relationships.
To make friends, you need multiple people, a reason to bring them together and some form of repeated mutually beneficial interaction. Multiplayer games have all these elements. Every piece of a game can be designed to remove walls and build social connections. What an opporutnity!
We can design our matchmaking and logistics system to encourage proximity
We can design our social signaling, characters and tribes to generate perceived similarity
We can design the economics of reciprocation loops at all stages of friendship formation
We can incrementally enable safe disclosure based off idle friendship formation pacing.
Often we think of computer games as a single player medium for storytelling or some other evocative experience We put games in the same category as books, movies, comics, etc. However, it is also interesting to think of games as intentional human processes; rule-based machines composed of living, breathing, growing people. They operate on the same scale as sports, religions and governments. Such engineered human processes can help players thrive in designed virtual spaces and ultimately in their real lives.
As game designers, this is one of our great powers and responsibilities. We design these machines. We are responsible for growth and nurturing of the machine’s players and communities that they form. The human process of friendship formation is an essential game design tool. Wield it wisely.
Other reference material
Early theory of friendship formation
Festinger, L., Schachter, S., Back, K., (1950) “The Spatial Ecology of Group Formation”, in L. Festinger, S. Schachter, & K. Back (eds.), Social Pressure in Informal Groups, 1950. Chapter 4.
“In the familiarity of strong ties we use simple restricted codes, where much is implicit and taken for granted. In communicating through the weak ties, we need more explicit elaborated codes for meaning to be fully communicated. When elaborating, we have more scope for creativity and the thought that it stimulates makes innovation more likely.
The more weak ties we have, the more connected to the world we are and are more likely to receive important information about ideas, threats and opportunities in time to respond to them.
Ah, the fall. A time to reap what has been sown and contemplate the cycles of the seasons.
If you are a smaller game developer, you’ve likely noticed some cyclical shifts in how we make games. Games are looking nicer than ever, don’t they? That quality bar keeps creeping higher. With so much work to do, your team is a bit larger. And with so many mouths to feed, it feels riskier to lose everything experimenting on wacky new game mechanics. Luckily, it is pretty clear which genres will yield the breakout hits you need to keep going. It is too bad that there’s a such an abundance of similar games; it feels like you can’t even give them way.
Remember when we had a revolution? One person teams could make original games with minimal content and strike it rich. Doodle Jump was a thing! A hit indie game like Braid cost a minuscule $200k to make. A developer and some lovely art and there was a complete top tier game. Press wrote about it.
But it feels if such games were released today, they’d likely be left to rot in obscurity. A modern hit by a “small” team is a game like Battlerite. 25 developers, lush 3D graphics, external funding. An order of magnitude increase in costs over a period of eight years.
To everything there is a season, and game markets follow predictable patterns of growth, harvest and if you’ve been luckily enough, stockpiling for the coming frost.
Have you been making games for less than 10 years? Are you a newer smaller indie developer who has only ever known the bright fields of opportunity known as Steam, console downloadable or mobile platforms?
Here’s what is coming. Here is what happens when game markets mature.
Memories of spring
Historical context matters.
A new game market opens when a new way of reaching eager players appears. In the early 2000s, digital distribution was a technology that cracked opened an industry previously dominated by retail sales. Apple and Google enabled phones to download games. Microsoft, Sony and Nintendo enabled consoles to download games. And Steam created a cohesive and reliable ecosystem for PC players to download games.
If you don’t remember the retail era it is hard to overstate what a radical change digital distribution was to the dominant business models. In retail, about 15% of revenue went to the developer. The rest goes to marketing, publishing and the retail store itself. This creates a power differential that tends to squeeze creativity out of game developers. Forget tales of wide-eyed idealism. Retail game development was a factory job that churned out games tailored to the whims of a giant box shipping machine. This was a mature market with most major game developers owned art and soul by middlemen publishers or platform owners. AAA still follow this model to a large degree. Good people, bad system.
Two things happened when digital distribution hit. For the first time in ages, we saw high demand and low supply.
High demand: Platform owners pushed their new distribution platforms heavily. A platform much preferred a guaranteed 30% cut of digital, especially when compared to a paltry 0-20% cut of retail. Valve bundled Steam with their top selling games. Microsoft gave away prime real estate on their console dashboard. Apple and Google directed users to go through their storefront in order to do pretty much anything. The result is a torrent of customers flooding through these digital stores wanting to buy cool stuff for their cool new toys. Put a pretty picture and a buy button and bam, you’ve got a sale.
Low supply: But there wasn’t anything to buy. A lot of traditional game publishers didn’t want to risk being beholden to some new platform master. Every digital storefront is essentially a monopoly with the potential to exert absolute dictatorial control. So most publishers held back. A few fringe game developers put up games. These were the hippies and hobos whose niche products never broke into the more mature retail markets.
And their games sold like hotcakes. In large part because there was nothing else to buy. For a while it felt you could put almost anything up on a digital market and turn a profit.
Short hot summer
With digital distribution, anyone with a computer could make a game and release it. And because they kept 70% of the revenue, they needed to sell a lot fewer copies to make ends meet. This means lots of little game companies. Call them ‘indies’.
Most were untrained. They didn’t understand how to run a business. Many had never made a professional game before. So they experimented, often wildly. Bizarro mutants popped up. Journey. Day Z. Tower Defense. What can you do with the internet? Or Flash? Or a touch screen. Or a one person team? Who knows; let’s just try something. Will Wright, gushed about the “Cambrian explosion”. New genres were born. That was 2008.
What a time. I look back on it fondly.
End of the growing season
Low barriers to entry
But low market barriers mean new developers just keep flooding in. And the nature of digital distribution means games never truly expire. So the back catalog of great games grows larger and larger. This is no longer a low supply market.
Nor is it a high demand market. Consoles are stable. Smart phones (aka phones) are no longer setting growth records. PC sales are dropping. All those digital customers are a known quantity, divvied in zero-sum fashion across the various DRM locked platform fiefdoms.
What happens to a market when demand is fixed and supply is high? Competition. Here’s the traditional logic. The following sequence has played out across thousands of games and dozens of markets.
Standardization: Players form communities around the most popular game types. This creates a standardized demand.
Competition: Developers try to capture the entertainment dollars of these communities by releasing games in the same genre. For example, they might release a MOBA.
Winner takes all: Players gather around one or two high quality, well marketed examples within genre. Those games earn the vast majority of all revenue.
Escalating costs: In order to win that top spot, Developers invest heavily in art, narrative, marketing events and monetization. Maybe you can beat your competition by simply doing more.
Bloat: This results in larger developer team sizes. Larger teams burn more money, leaving less margin for mistakes.
Risk avoidance: A culture of risk avoidance dominates. You must make proven games with proven themes resting on proven mechanics for a proven audience. Layers of decision hierarchy grow to eliminate exuberant impulses. ‘Wasteful’ experimentation is deprioritized. All focus is on servicing the nuanced needs of expert (high value) players in an existing genre.
What success looks like
There are three broadly successful long term strategies for independent developers in this newly competitive market.
Become a genre king: Have a hit game in a popular genre. Invest those profits in ensure that you have the best developers, community and marketing to own that audience. Set the standard that all others hope to achieve. Be what Blizzard was to MMOs. If you pick the right maturing genre, you can gain 10 to 20 years of stability.
Dominate a niche: Find a niche that only appeal to a wealthy but passionate audience. Become hyper efficient at serving that niche. This isn’t so different from being a genre king except no one cares about you. The press barely cover you. The broader population of gamers doesn’t really know you exist. But a small devoted community cares. So you scope your company to the tiny size it needs to be to serve a tiny market. Artemis Spaceship Bridge Simulator or SpiderWeb’s retro RPG games are good examples.
Manage a brand: There are a handful of companies that have a powerful brand they used to secure funding. During hard times, they essentially freeze dry themselves. This minimizes costs until the next deal comes along. Jackbox is the most common game industry example.
False success of having a hit game
There’s a ton of money flowing through a maturing market and occasionally it arcs over to the random indie in the right place at the right time. Zot! A jigawatt of revenue powers them for years(!) without additional income.
But the result is a lesson in exponentials. Ever play one of those new fangled idle games like Cookie Clicker? As markets mature, escalating exponential costs rapidly consume existing savings. For example: A top shelf ‘Triple-I’ indie’s last game cost $200,000. They made back $2,000,000 in sales. But their next game costs $2,500,000. Maybe they make that back also. Maybe they don’t. The money in the bank only gives them 1 or 2 additional swings at bat, not 10.
We now use the term ‘Triple I’ for medium sized teams that had hit games, but we used to call that same spot in the ecosystem ‘midtier developers’. They all died off as markets continued to mature. It becomes increasingly hard to roll a hit every time. In the end, they had no sustainable advantage.
Selling the farm
So not everyone can stay independent. There are three common outcomes for those forced to give up ownership.
Hobbyist: The team becomes a non-commercial endeavour. Either people get a day job and work a few hours at night. Or their family support them. Or they get grants from some institution interested in their work. Or they make games as students and change careers later.
Hire yourself out: The team becomes a contractor to someone with money. This can be via a publishing deal. Or via outright purchase. Or you actually sign a contract to perform specialized labor like porting or multiplayer development. Mega studios love hired help.
Extinction: The team goes out of business. That whole ‘indie’ thing was neat while it lasted.
You may be curious what winter looks like. Here’s what is coming up for PC, console and mobile.
Consolidation: When a bigger company eats a smaller company..or a smaller company implodes and a bigger company hires their employees, we are seeing something called consolidation. Lots of little studios turn into a smaller number of bigger studios.
Consolidation is a longer term process that will play out over the next 4 to 8 years. These forces don’t apply equally to every team. Some developers earned enough from a hit game they can ride along for many years without confronting their inability to make another hit game. Others are willing to starve for a few years longer before they make any hard decisions. Be patient.
Distribution scarcity: It has already become increasing difficult to get your game in front of new players. The sheer number of games is part of the issue. Also audience capture and advertising cost (see below) limit the general availability of free customers.
Audience capture: The available audience will actually shrink as high value players are locked into long term service-based games like MMOs or other F2P titles. A player doesn’t ‘beat’ a game like Clash of Clans; instead they play one game exclusively for years. F2P companies will attempt to stretch the lifetime of their player to decades. These players are no longer looking for a fresh new games so they are typical unavailable to studios making new games or trying to replace churned players.
Majority of studios priced out of buying ads: The ad market sells its inventory to the highest bidder (across a myriad of categories) And for games, the highest bidder is the game with the strongest Life Time Value (LTV). Do you have a high LTV game in a particular category? Great, you can buy ads that juice your player acquisition. If you have a low LTV game (all premium games, most experimental games, most independent games) effective ad-based distribution is priced out of your reach.
Fewer, bigger hits: As the market consolidates around a handful of high value genre leaders, they will earn enormous amounts of money. The downside is that fewer small developers will capture enough sales to stay independent.
Rise of new publishers: Larger organizations with strong marketing and business development can mitigate some of these trends. They also can build portfolios so that if some games fail, successes still keep the whole afloat. That organization usually is called a publisher. Expect a number of publisher to start snapping up contracts for games from the more capable indie developers. Indie developers get cash to offset the risk of their game failing and and publishers get another chance of owning a hit game.
Rise of first party: Longer term platforms will start taking full ownership of any genre that is a guaranteed money maker. This vertical integration pays off. Platforms can capture all revenue that goes through the game, direct players to their games via promotional spotlights and reduce the riskiness of dealing with a volatile 3rd party developer.
We should celebrate the perennials planted during this amazing cycle. Or at least the tulip bulbs that may one day bloom.
Grassroots game development will continue to thrive
I don’t think we’ll ever go back to the bad old days of early 2000 where ‘breaking into the game industry’ was an actual barrier. Several trends mean the flood of new developers will not cease.
Tools: The cost of tools has dropped dramatically. And the tools that exist such as Unreal or Unity are of unprecedented power and polish. Anyone with time and passion can makes games and I suspect it will only get easier.
Schools: Students want to make games. Schools can charge those student enormous fees to teach them how to make games. This dynamic will exist independent of whether or not there are paying jobs waiting for those students.
Open distribution: There are multiple ways to make your game available to knowledgeable players. Steam, Android and iOS stores have minimal gatekeeping. Sites like itch.io have no real gatekeeping. The vast swath of humanity that doesn’t know about your game will never find out about it from these locations, but at least it isn’t blocked from publication. For the hobbyist developer, even a couple dozen downloads from friends and family can be inspiring enough to encourage further game making.
Expect a situation closer to what we see with writers, painters and musicians. Schools enable the necessary but time intensive acquisition of game making skills. The commercial market for those skills remains difficult to break into without elite level portfolios. However, there’s still a vast community of extremely low income developers making games because their passion is stronger than the need to be wealthy. In my dreams, this group of game making hobbyists regularly gets together for wine and moral support. And maybe even funds the occasional indiegogo when one of them needs a new liver.
There will be new markets
VR is one obvious new market. VR isn’t quite able to stand on its own, but platform owners seem committed to market building. If they collaboratively spend a billion or so to seed VR content, that’s a new billion dollar market for game developers.
And VR is not one new market. A rolling wave of multiple VR and AR markets will appear over the next decade as new technology leapfrogs past efforts. Each will be characterized by tech giants engaging in market building. That’s an opportunity. Early PC development was likely the most similar sequence. We can have multiple Cambrian explosions.
The seasons turn
I hail from Downeast Maine where growing seasons are short and harvests valued. The spring is a (muddy) revelation. The summer a miracle. Even fall is greeted with a delighted grin. Yes, the wind blows so hard it is hard to walk straight. Yes, the frost will kill our gorgeous garden. But if we’ve planted well, the root cellars are at least full. And we’ve got hot apple cider. And if we haven’t, we’ll do what we need to do to make it through. Even if that doesn’t involve owning our own garden.
The key to my admittedly insipid joy is to realize that the world runs in cycles. We can bemoan the loss of summer, but it does little good. Instead, as winter settles in, put wood in the stove, put on some tea and let the infinite snow silence the cacophony of the world. Take some time to think. What did we do wrong during the last big opportunity? Take some time to dream. What would we do right if we had a chance to grow again? A long term view means that there will be many seasons of growth, harvest and frost.
Let’s dream for a moment about sustainable game development.
Game development is inherently unstable. Technology, markets, profit margins and teams shift regularly. Any of these can quickly destroy a previously comfortable business. Individual game developers end up dealing with unexpected layoffs, last minute moves across the country (or across the world) and a level of uncertainty can damage our relationships and long term happiness.
In order to simply make ends meet, you end up compromising your dreams, for years. Or decades. Game development exemplifies Schumpeter’s creative destruction on an accelerated scale with intensely personal consequences.
So what is required to build an oasis? A place where, at minimum, one might make games at least without having your beloved team or your bank account regularly exploded.
This essay covers some of numbers behind reaching success as a developer of premium games in the current market. I don’t offer solutions, but you may find some of the concepts useful.
The uninteresting case
There are obvious examples of extreme success. If you happen to make a game that personally earns you 10 to 30 million USD after taxes, you can likely devote the rest of your life to game development. You may not have enough to fund larger teams, but given reasonable budgeting, you’ve at least covered your expenses until death. For every additional teammate you need to make games, add another 10 million to your lifetime game making budget. (You may not want to actually spend your own money, but that’s a different discussion)
For those of you who find your gilded selves in that particular pickle, well done. None of the rest of this essay is meaningful to you.
What are the borderline cases? Imagine a glider that slowly drifts downwards, but manage to catch just enough of an updraft to never quite crash.
The following are some ideas pertinent to surviving long term in a hit driven media industry.
Defining success: Success rates, Size of success, Variability
Tactics for surviving the odds: Budgeting, Prototyping, Hobbies, Revenue streams
1. Defining success
In the 90s, Sierra expected 1 out of 4 games to be a success and pay for the other products that failed to turn a profit. Recently, Mike Capps, the previous president of Epic, claimed that he couldn’t promise more than a 10% chance a game would be a success. If you made 10 games, on average, you’d expect only 1 would be considered a success.
Success rate is simply the ratio of games that hit some threshold of financial success vs the total you’ve released. It is never 100% and can range from 1 to 25% based on the particular market you are in.
Over time success has been dropping. 25% is almost never seen in modern game markets. Tools are cheaper, distribution platforms are more open and there’s simply a much larger supply of games today than there have been in the past. The number of game players has increased as well, but far slower than the vast increase in developers. Given a set of equally competent games, only a fraction will become profitable.
I typically think of success rates in the context of experienced developers. These are numbers coming from professional developers that are already using every trick in the book to mitigate risks. They are making sequels, they are leveraging existing relationships, they are selling to their fan bases.
When I talk about probabilities in game development, I’m by no means saying that success is all due to luck. Instead, it is merely acknowledging that even when you do everything you possibly can there are still huge risk factors that are out of your direct control.
You might as well plan for only a small chance of success with an individual game. This isn’t being negative. Smart people make good money off probabilistic systems every day. Size of success
How big of a success is actually a success? There are many definitions of success out there. For the purposes of this essay, let’s consider making enough money to not go bankrupt the first tier of success. At the very least that means paying for your failures.
The first thing to realize is that not all profitable games provide long term success.
If you make 10 mobile games for $100,000 a pop.
Brutal failures: 3 make a total of $153.02. They didn’t get featured by the app stores and were lost in the sea of obscurity. Pretty common, though people tend to be shy about discussing their failures.
Moderate failures: 4 make $50,000!
Break Even: 2 games break even. Everyone talks about them as if they were a success.
Success: Only a single game earns $1 million. It needs to earn 10X its cost to cover your million dollars in total dev costs.
What happens if that profitable game make $600,000? It earned 6X its costs! You made a profit of $500,000, enough to make 5 more games. However, you are still on the long road to bankruptcy, despite an apparent success. There’s only a roughly 40% chance those 5 swings at bat will result in a success. Long term, you’ll find yourself out of money or in debt.
I regularly hear press or indies trumpeting that a team broke even or doubled their money on a project and I cringe. I’m happy that they got a scratch off ticket to play again. But these are the same developers that are quitting the industry or sunk into despair when a game or two later they’ve run out of money.
It is a disservice to other developer to claim that a breakeven project is a financial success. Break even means almost nothing. You are still on the knife’s edge of baseline survival and should operate financially exactly as if you had achieved nothing. Variance
Even studios that have successes that are 10X their average project cost still end up going under.
Flip a coin 20 times. On every 1 out of 2 times should be heads. But you don’t get a pattern of alternating heads and tails. You get streaks. You may see 10 heads in a row. This is within the bounds of chance. However, if you really needed tails to come up, you are in a lot of trouble.
Random systems have natural variability and game development does as well. The best team in the world can strike out 10 times in a row. It is just as likely for your failures to be front loaded as it is for your success. So not only do you need your success to pay for the average rate of failure. You need it to pay for the worst possible luck.
The more buffer you have, the longer bad luck streaks you can survive. At the very least, add a few expected failures into your success rate calculation. It isn’t a perfect tactic, but it helps you deal with bad luck in addition to mere average luck. What I personally consider a successful project
At Spry Fox, in the past 5 years we’ve accumulated the following numbers:
31 projects started as prototypes.
20 smaller prototypes that also didn’t pan out. Some took months, others took days.
11 released projects
4 that didn’t make money (both brutal and moderate failures).
4 break even projects
3 outright successes.
For us a success means a released project makes back 5 to 10X its production cost. That is what pays for all the prototyping, failed projects and general poor dice rolls.
I was surprised to note that of our prototypes, roughly 1 in 10 end up being a successful project. I assumed we had a lot more horrible prototypes than apparently we do. For released projects, we are closer to 1 in 3 being successful.
That’s better than expected. But it does make me mildly worried that a bad luck streak is on the way. It would be completely fair to suggest that our successes were front loaded and our actual success rate is lower than the current small sample indicates.
However, the most important aspect of these numbers is that we are aware of them. They limit how much we can spend on a project and how much we could keep in reserve.
2. Tactics for surviving the odds
There are a vast number of techniques that help deal with the variability in game development. The following, however, are ones that don’t fundamentally alter the odds. They help you survive the odds, which is a very different goal.
Basic Budgeting for Sustainability
It is very common to spend too much money making your game. At minimum ask the following questions:
Target Revenue: How much do you expect to make?
Success Rate: What is chance of making that much money?
Your budget is likely Target Revenue * Success Rate. So if there’s a 10% chance of reaching $500,000, you should spend $50,000 on each project. That’s 1 full-time experienced developer for 5 months assume pay of $10,000 a month. Or if you underpay yourself relative to what you might make at comparable jobs and spend 10 months at $5,000 a month.
These numbers should look scary. They suggest that the vast majority of indie developers are ripe for financial ruin and are operating primarily on hope instead of any rational financial strategy. I think that’s accurate.
Low cost prototypes
Notice that the numbers I shared for concept success rate are quite similar to Mike Capp’s 10%. However, our released games have a much higher success rate (30+%). The reason for this is that we prove out the gameplay early using a low cost pipeline of low cost prototypes.
These prototypes cost dramatically less than a released game. Some of those 30 prototypes only took a couple days with a single programmer. By disproving bad ideas early, we put real money into games that have a much higher chance of success.
Releasing on multiple platforms
Each time you release a game on a new platform, you get to roll the dice all over again. And you do it a much lower development cost. Triple Town was only a break even game on the eInk Kindle. It was a true success on Android and iOS. If we had stopped after the first release, I would have considered Triple Town a financial failure.
Using designs and technology that quickly and cheaply transfer to new platforms reduces your porting costs and decreases the size of success you need to remain in business.
Operating as a hobby
One of the trickier aspects of sustainable development is the need to pay for food and housing. What if you can pay for those costs through some other means than games making money?
Some typical paths.
Contracting: You can save up money working for someone else and then spend that money on a period of full-time development. The cost here is two fold. Development goes more slowly and long term you average wage is lower.
Working at night: You work a full time job doing something else and then spend evenings and weekends making your game. The cost here is that work goes much slower. It is also not likely to be your best work since it is difficult to maintain quality while working more than 40 hours a week. You also bear the opportunity cost of sacrificing your leisure time to making games.
Supportive spouse or family: Someone else in your family makes enough money that you have the leisure to work on games full time. The costs to the artist are generally low. The dominant one is a reduction in household family income. A great situation if you can manage it.
We don’t talk about it much, but a large number of successful ‘professional’ artists are in a relationship with someone else that pays their way. They aren’t successful entrepreneurs with a deep understanding of sustainability. Instead they are full-time hobbyists in a fortunate financial situation. They accumulate excess leisure time and spend it on game development.
This sort of blessing is very difficult to admit. But embarrassed silence dupes less fortunate artists into pursuing an unrealistic fantasy of how to thrive. If you are a kept developer and are living off someone else’s money, talk about it. Indie finances could use a little sunlight.
Longer term revenue streams
Premium games tend to have spiky revenue streams focused around launches and special sales. Financial instability is built into the business model.
Here are the most common ways of adding a dash of stability.
Franchise: A long term game franchise where sales come from promoting sequels or remakes. This tactic is regularly practiced by conservative large companies, but also works for smaller operations like Spiderweb Software.
Eternal updating: Continually update a game and making some noise about it. Toss in some sales. For most titles, this tends to drop off after a year or three. A consumable game tends to not be an evergreen business asset.
Freemium: Make a game service and build a stream of revenue. This requires that you know how to run a freemium business. It is an uncommon skill set for an indie, but quite valuable.
These give you a base layer of predictable revenue. As long as your burn rate as a company doesn’t go wildly over your income stream, you can keep making games.
These revenue streams have been our goal as a company. We are looking to build long term games that produce a steady stream of revenue from a community of dedicated players. This isn’t an easy target to hit, but at least we are building games with that conscious aim in mind.
The big lesson is that your exposure to luck is something you can manage. Think about releasing a portfolio of games, only some of which will be a success. And you should budget in such a manner that you can afford to make that portfolio. Blowing your existing capital on a single title is almost always a dumb idea. Sometimes it pays off. Most of the time, it doesn’t.
However, it is also worth realizing that playing the premium market straight on is, by many measures, a sucker’s game. The standard bet is to lose money on 5 to 10 games and have one success that lets you do it all over again. For most companies, the house always wins in the long run.
Perhaps the longer term solution is to run your games as a service. Try to create a product that produces reliable cash flows. This likely require a certain level of business thinking. You are making a financial machine that lasts instead of a Hail Mary piece of art that vanishes.
Back in the 80’s and 90’s, when conversation about game design was first bubbling up out of our community of insecure practitioners, a few polarizing topics would arise again and again. You’ll recognize them:
The correct definition of ‘game’
Narrative vs Mechanics
Randomness vs Skill
The importance of realism
Casual vs Hardcore
Many were (and are) merely the irritated observations of game players picking at specific games. However, with a flip of the rhetorical switch, players become designers expressing a universal design truth. Opinions inevitably differ and thus positions harden in the absence of data. And it snowballs from there.
Thankfully, as a developer community, we’ve grown older. With time and the accumulation of thousands published games, experienced game makers have a lot more insight into how game design actually works. It turns out there’s plenty of room for nuance.
There’s also the growing maturity to ignore false dichotomies and worn out talking points. Honestly, we don’t have time any more. We should be making great games, not arguing ancient design politics.
In the spirit of becoming a forward looking designer, here are my top 5 design debates that I’ve ignored in 2014.
#1 The correct definition of ‘Game’
I’ve seen a metric ton of definitions for game over the years and have dabbled in crafting them myself. Not a single one has been useful to me in my daily practice of making great games.
Why this discussion is outdated
Games are vast and varied. A single definition tends have one or more of the following issues:
Overly broad: The definition is unable to provide any direction or guidance.
Overly narrow: The definition eliminates useful tools and influences from other areas of systems, thought or art.
Overly convoluted: The definition is only useful to lawyers who care primarily about edge cases and not about getting things done.
Alternative discussions to have instead
I focus on finding and exploring useful design tools. I don’t need to care about the definition of ‘woodworking’ in order to be damned happy that hammers and nails exist. The same goes for games. I focus on scaffolding. And loot drop tables. And internal economies.
A useful goal is to find general tools that a smart designer can use to radically improve their work. Like any tool, they should to be applied in the proper context. So they are rarely universal or one-size fits all. And like a craft tool, they need to be applied with skill. They aren’t a pattern that you toss at a problem and get a fixed result.
Recommendation: Build your flexible design toolbox. Master those tools. Apply them where appropriate. Ignore pedants obsessed with defining ‘game’.
#2 Narrative vs mechanics
Science was once plagued by the idea that certain behavior derived entirely from genetics (nature) or entirely from environmental effects (nurture). This turned out to be a naive simplification of a vastly most intricate and interrelated system genetic predispositions, environmental triggers and feedback loops.
Narrative and mechanics have proven to be similarly intertwined.
Why this discussion is outdated
In the end, the human brain has neither a pure systemic understanding the world. Nor does it have a purely narrative understanding of the world. Memory, learning, emotional triggers, cause and effect all feed into how our brain adapts to environmental mechanics and then flow out again as a social response.
So the model suggested by the supposed conflict is simply broken. There is no ‘versus’.
There are many explanations for how this argument even arose. My favorite: A cocky tribe from old linear media clashed with an isolated tribe of game makers. They fought a stupid fight about authority and status that had almost nothing to do with making games. Meh.
Alternative discussions to have instead
A modern discussion could include:
What existing schemas are activated by my game?
How should we implement learning and scaffolding structures?
What is the impact of various forms of stimuli within game loops?
How should we tighten or loosen our systems of cause and effect?
What are systems of pacing?
What social role does narrative serve? How can we engineer human systems to encourage it?
Theories like Interaction Loops or Emotion Engineering integrate narrative and mechanics. In the process of banging our heads against building great interactive experiences, we’ve been forced to break down ‘narrative’ and ‘mechanics’ into atomic chunks and see how they fit in practice. Let’s discuss the rich synthesis of story, world building and mechanical techniques that thrives in interactive systems.
Recommendation: Consider how narrative emerges from existing mechanics. And consider how theme illuminates mechanics by activating existing mental schema. We need holistic, integrated models. Ignore antagonistic dichotomies.
#3 Randomness vs Skill
There’s been a sad resurgence of this 80’s wargamer rant. Randomness is obsessively derided as less masterful or strategic relative to pure skill games.
Why this discussion is outdated
Randomness is just another design tool. Used with skill, it yields some amazing games.
Random systems are rife with mastery. ‘Randomness’ can provide strong elements of mastery, in terms of learning distributions, managing options and adapting to new situations.
Games involve loops. Random outputs almost never occurs in isolation, but are part of an internal game economy. Randomness is often an essential tool for creating strategic variation and context.
There are different, equally valid playstyles. Not everyone is a rigidly intellectual young man who desires only mental-skill games that let them dominate others. Some play to relax, some to socialize, some for physical mastery, some to feel part of a shared purpose. Randomness can be a beneficial tool when designing for these players.
What are other noise generators? Complexity noise, social noise, feedback noise, etc.
How do we make people better through play?
Recommendation: Practice using randomness where appropriate. Explore the space. Make a game with randomness that is about mastery. If you happen to be someone that values intellectual rigor over chance, make a game for someone other than yourself. Stretch your humanity.
Past futurists sold a vision where games must inevitably become indistinguishable from reality. We marketed the hell out of that vision to the point it became dogma. You bought a new console, a new video card, a new computer to creep ever closer to the dream. You argued for 1080p as a paladin fighting for the glorious Holodeckian cause.
Why this discussion is outdated
Realism in graphics or simulations no longer is a dominant goal for most game developers. In practice, it turned out it wasn’t really an essential feature for a successful games. In our far future era, you can snub realism and still make a billion dollars with a game like Minecraft or Puzzle & Dragons.
Realism has niche appeal. It is an aesthetic choice that tends to appeal to a singular sub-culture that we’ve trained with our decades of marketing. Cartoons, text and other stylized forms of representation are also appealing.
Realism can be an unnecessary expense. We sometimes wholesale replicate reality when we don’t know what specific stimuli actually appeals to players. It is sort of a shotgun approach that wastes vast amount of effort to hopefully make something interesting. A substantial portion of the exponential escalating cost of game development can be attributed directly to the pursuit of realism.
Simulation adds design risk: Many simulations are complex and difficult to manipulate. They also are not inherently emotionally satisfying. Insisting on mechanical realism while simultaneously trying to make a fun game tends to yield failed game designs.
Games are also endogenous systems of value. They are like little self contained baubles of math that set up interesting internal relationships. A game like Tetris has immensely value independent of references to the real world.
When players ask for realism, they often aren’t asking for realism. The desire for realism is often best understood in terms of how players learn and apply existing mental schema to new system. A request for realism could be: A new player asking for a metaphor that helps them understand an abstract system. Or it could be an advanced player pointing out unnecessary edge cases. Both these have solutions outside belabored realism.
Alternative discussions to have instead
What is the right art style for your audience?
What are the trade offs between art style, production concerns and budget?
What sort of math or systems are interesting independent of their appearance in the real world?
How do we make game-like, cartoon-like, info rich, surreal virtual reality games?
Recommendation: Ask what utilitarian feedback your game truly needs. Invest your art resources making those elements amazing. Ask what level of modeling a system needs to create rich gameplay. Invest your design resources to create a tiny rule set with deep emergence. Be smart. Be frugal. When someone demands realism, try to figure out what they really want.
#5 Casual vs Hardcore
There’s a set of cultural stereotypes that casual players act one way while hardcore players act another. A surprising number of design decisions are made based off these stereotypes.
Why this discussion is outdated
The casual and hardcore stereotypes suffer from the problems typical of stereotypes. They are gross simplifications that yield the incorrect design decisions.
Many of the stereotypes are simply wrong: The longest average playtimes? Not console or PC. Handheld games, particularly those ‘kiddy’ Nintendo titles dominate session length. Regular daily play happens more often on smartphones and tablets than it does on consoles. When I look at data, there are very few ‘casual’ or ‘hardcore’ stereotypes that hold true. And when they do there are massive exceptions.
The variation within a specific game is huge: You’ve got a half dozen or more distinct playstyles within almost any game of reasonable complexity. Each game is a vast city with many different people living within it. Mere averages tell you very little about how to improve the state of your game.
The market is shifting: Service-based games are driving for improved retention by doubling down on play. Women are playing more. Console owners are aging and slowing down. A lot of the old lessons about demographics and play styles have shifted. And they’ll continue to change in the future.
I see ‘casual’ or ‘hardcore’ as poisoned tribal labels like ‘gamer’ or ‘skinner box’. Mostly they are just weaponized stereotypes, deployed to enforce perceived group boundaries. They have little productive place in a modern design (or marketing) discussion.
Alternative discussions to have instead
How do you break out of thinking in cheap stereotypes in order to gain an advantage over the dinosaurs that don’t see the market has it truly exists?
How do different groups unique to your game behave? (Hint: We can get the data!)
What motivates the groups unique to your game?
How do you include diverse hooks to appeal to multiple passionate audiences?
How do you make a targeted niche game using iteration with a live community?
I personally tend to make games that look ‘casual’, but consistently melt the brains of self identified ‘hardcore’ players trained on endless tutorials, cut scenes and QTEs. Some of the best players are smart 30-40-year old women that have the intense mental stamina for activities like logic, planning and creative thinking. They thrive on hard games. My market doesn’t even exist if you see the world through a ‘casual / hardcore’ lens. Yet there it is, merrily enjoying games amidst the vast diversity of this planet’s billion odd players.
No one really makes ‘hardcore’ or ‘casual’ games. At best, we use existing markets, tribes and distribution channels to get a tentative foothold in a player’s psyche. But then it gets complicated. Embrace the complexity of your players. Learn who they actually are. Create elegant solutions that serve your many types of players.
Thoughts for 2015
If you happen to find yourself facing these 5 topics: Turn away. Our creative lives are limited. Pour your time into something productive.
Teachers that spread these memes: Consider teaching modern game design tools. Cull disproved dogma.
Academics that expound on these ideas: Stop naive theory crafting and start referencing nuanced data from working designers.
Students that gnaw at these bones: Arguing ancient talking points in comment sections gets you nowhere in life. Make games instead. Base your design conversations around your hands-on experiments. You’ll learn more, faster.
Goodness knows that conversations on dead design ideas will not end. Players and their innumerable derivatives (fan press, forum warriors, cultural critics, etc) continue talking about these topics. Some talk for entertainment. Some for status. Some for business. Some talk about their game experiences in order to process them mentally and emotionally. For many of these purposes, simplistic polarizing hooks are more enticing than deep comprehension.
So these inane design views become practically tradition, or at least common hazing rituals. Like yelling at televised football games. Or laughing at trucknuts. Sure, players aren’t having a productive craft conversation, but they shouldn’t be judged by the same rubric. Consider their chatter a cultural performance.
As for designers, you have a different role to fill. Recognize when you are accidentally acting like a uninformed player or student. Instead of getting caught up in the babble of ill-informed internet backwash, try talking directly with other working designers. Build tools and knowledge together.
Many games have loot. Usually this drops randomly. Loot drops are a pretty mundane topic, but one that almost every designer runs into at some point. Here are some best practices I’ve encountered over the years. Many thanks to everyone who contributed to these tips and tricks.
Your basic loot table
The goal is to drop some set of items at a given probability. Let’s say when you defeat an enemy, you have a chance of getting shield, a rare sword or nothing at all.
Item: An item is something you want give the player.
Loot Table: A set of items is put into a loot table. This is just a bucket of items. For example a loot table might include: Sword, Shield, Null.
Weight: An item has a drop weight: 1 to 10,000. For example a sword might have a drop rate of 10.
Null items: One of the items in the loot bucket is ‘null’ which means if that is rolled, no loot is given
Rolling for loot
Total probability: First, sum all the weights in the bucket. In the example above, that’s 10+40+50 = 100. They don’t need to add up to 100 since these aren’t percentages.
Next assign each item a range. Sword = 1-10, Shield = 11 to 50, Null = 51 to 100
Generate a random number from 1 to 100.
Compare that number to the ranges. That’s the item that drops.
Reroll: Generate multiple random numbers to simulate multiple rolls.
So what does this look like to the player? We’ve got a 10% chance of dropping a sword, a 40% chance of dropping a shield and a 50% chance of getting nothing.
As the designer, I could go in and change Null’s weight to 100 and now I’ve got a 6.6% (10/150) chance of dropping a sword, a 26% (40/150) chance off dropping a shield and a 66% (100/150) chance of dropping nothing.
Mapping onto other common random systems
This system is a simple restating of many other familiar methods of randomness. It is a fun superpower to train your designer brain to be able to switch between understanding any randomness issue in terms of loot tables, cards or dice.
Imagine deck of cards that you can shuffle and draw from.
Each type of card in the deck is an item.
The number of cards of a given type is that item’s weight
Shuffling the deck is equivalent to assigning each item to a range and generating a random number.
Drawing a card is the equivalent of selecting the item that drops.
Now a normal deck of cards has 52 cards, but with loot tables, you don’t need to operate with that constraint. Your decks could have 1000’s of cards and a vast array of types. Or they could have tiny decks that are the equivalent of a typical poker hand.
Dice also map onto loot tables.
Each individual die is a loot table.
The sides (1-N) are items (labeled 1 through N)
Each side gets a weight of ‘1’. (Unless you are using weighted dice!)
Multiple dice can be represented as rolling the same loot table multiple times. So 2D6 is the equivalent of sampling a 6 item loot table twice.
Now that we’ve defined a basic loot table, what else can we do with it?
Variation: Items sets
You can also drops sets of loot. An item doesn’t need to be a single thing. For example, I could extend it so that the players gets a shield and a health potion if that option is selected.
Variation: Always drop
A common need is to flag an item so it always drops. One convention is that items with weight ‘-1’ always drop. Variation: Repeatable randomness
Sometimes you want to be able to repeat a random roll. For example, when a player saves a game and then is able to reload to avoid a bad loot drop, it can lead to very grindy player behavior. If there is an exploit that ruins the game for them, most will happily go for it.
Most contemporary pseudo random number generators use a seed value. As long as you can save that seed value, you can run the random number generator again and get the same result.
Variation: Rolling without replacement
The problem with the system above is that players may, through chance alone, always roll ‘null’. This is a common complaint by players. “I played that encounter 3000 times and never got the MegaGoldenLootGun!” This can happen.
In statistics, there are two fundamental types of sampling:
Sampling with replacement: You pull the numbers out of the bucket and then after you’ve recorded what you got, you put them back in. So you have the same chance of getting the same thing again in the next draw.
Sampling without replacement: You pull the item out of the bucket and once you’ve recorded it, you set it aside. You have a lower chance of getting that item again and thus a higher chance of getting the remaining items.
Tetris uses sampling without replacement. Each set of Tetris pieces is in a loot table. Every time you get a specific piece, it is removed from the bucket. That way they guarantee that you’ll always get a long piece if you wait long enough.
Here’s how you implement rolling without replacement in a loot table.
When you roll an item, reduce its weight by 1. This shorten its range by 1 and shortens the max range by 1 as well.
Keep the player’s modified loot table around for the next time you roll.
Variation: Guaranteeing specific drops
Sometimes even rolling without replacement isn’t fast enough and you want to guarantee a loot drop. Blizzard does this for certain rare drops so that players don’t grind for very long times.
You could just increase the weight, but a low chance of getting something with a guarantee can feel very different over multiple plays than a slowly increasing chance of getting an item.
Here’s how you implement guaranteed loot drops.
When you roll any non-guaranteed item, reduce all non-guaranteed items weight by X%
X = 100 / Max number of rolls you before the guaranteed items drop.
Keep the player’s modified loot table around for the next time you roll.
Suppose you want the sword to always drop after 5 turns even though it it only has a 10% chance of dropping.
So X = 100 / 5 or 20%.
So every time you don’t roll the Sword, the weight for the Shield drops 8 (40*0.2) and the weight for null drops 10 (50*0.2)
After 5 turns, the weight for all the other items will be 0 and the sword will have a 100% chance of dropping.
Variation: Hierarchical loot tables
Loot tables are generally source for new resources. However, you can easily run into situations where you are dropping too much or too little of a particular resource. Some sort of constraints would be helpful.
One solution is to use hierarchical loot tables without replacement. When a particular resource runs out, the player doesn’t get any more. We’ve used this for our daily coin awards. We want to give out 100 coins a day, but no more. But we want to do it as part of the loot system.
Create two tables: Rewards and DailyCoins.
Have the main loot table reference the Daily Coins bucket.
When Daily Coins get picked, roll that table and see how many coins you get.
In the example above, a player has a 40% chance of getting coins. Then we roll the dailyCoins table and see that they can win a maximum of 100 coins a day with 10 awards of 1 coins, 4 awards of 10 coins and 1 award of 50 coins.
When the dailyCoins loot table is emptied, they’ll get nothing until it refreshes after a day.
Variation: Conditional drops
Sometimes you want to test if you should drop the items base off some external variable. In Realm of the Mad God, we wanted to avoid free riders getting loot for a boss kill without doing at least some damage. So in the loot table, we added a check. If a valuable item in the loot table was rolled, then we’d check to see if the player had done more than X% of damage to the enemy.
You could also build in switches for which loot it valid based off player level or even enemy level. I tend to instead use multiple smaller loot tables, but the system is flexible enough that you can easily architect your data with a few large tables and use of conditionals.
You can also modify the quantity or weight of a drop based off some external logic. For example, a player with a skill in harvesting could yield 2x as many of a particular item drop compared to a player without that skill. Or you could modify the weight. A high level character might have a -50% weight for all items marked lower than their level. (Thanks to a Reddit commenter for this idea)
Drop tables are commonly used for dropping loot. But I also find them useful in other areas.
Procedural generation: Use a table to build weapons or characters from components
AI: Use a table to select behaviors such as attacks or moves.
This may seem a little silly..surely there are better ways to model AI! However, one way to think about randomness is that it is a very rough first order model of any system. How does the human brain model a system? We make an observation about a system. We note the frequencies and tendencies for those observations to reoccur. It is only much, much later that we start to understand ‘why’ something happens or the causal relationship between parts.
In physics, we often joke that in order to model a cow, a complex biological organism, the first step is to ‘imagine a spherical cow’. By creating a simplistic, easy to work with model, we can often generate useful insights at a very low cost.
Many times, a drop table is a ‘good enough’ human-centric approximation of a complex system. For many systems, most players will never move beyond a basic probabilistic understanding so modeling more complexity is a waste of time. Efficient game design is an exercise in modeling elements only to the minimum level necessary to create the desired experience.
Consider: D&D modeled entire universes with what were essentially loot drop tables. That was a deliberate focus on minimizing systems that were in many ways just secondary flavoring to the core roleplaying.
A loot drop table isn’t the only tool you need, but in many scenarios, it is good enough.
Procedural generation thought experiment
Here’s a simple procedural generation system using drop tables. There are lots of other ways to do this, but this is more to get your brain thinking.
Let’s say you want to build a procedurally generated enemy
Start by making a list of unique enemy parts. Maybe your enemy is made up of a type of movement, a type of attack, a defensive buff and a type of treasure.
Make loot tables for each one of those parts.
For each item in the loot table, give it a power value based off how powerful you think it might be. for example, a knife attack might be weak so it only has a power of 5. But a large hammer attack might have a power of 15.
Create another loot table of buffs. These are modifiers to various attributes. For example, ‘Strong’ boost a value on an attack by 20%. You can have debuffs as well ‘Weak’ might diminish a value by -50%. These have reduce the power value of a part.
Now let’s generate an enemy
Set a target: Set a target power for your generated enemy. Say you want an enemy of power 40
Roll: Roll each of the parts once and add them into a list.
Score: Add up all the power values to get a score.
Adjust: If the sum of the parts is over the target, add a debuff or roll for a lower power part. If it is under, add a buff or roll for a higher power part.
Repeat: Repeat this process until you hit a desired error threshold (distance from power 40) or you’ve exhausted the number of iterations you are willing to spend.
You now have a procedurally generated enemy. There are tons of tweaks you can do to this basic system, but it works most of the time. As an exercise, think about:
Exclusion lists: If two parts are picked that are on the list, throw the enemy away and reroll.
Multiple constraints: Parts are scored on multiple criteria. Note, the more constraints you add, the less likely you are to converge on a viable result.
Any time there’s a discussion of randomness, there’s a huge number of secondary issues that come into play. I recommend the following for further reading:
Resist being dogmatic about randomness. Be a broadly educated designer whose aesthetic choices are based on hands on experimentation. A good rule of thumb is that you can’t intelligently critique a design tool until you’ve made a couple games that use it successfully.
Anyway, this is just how I’ve done loot tables; a mundane part of any working designer’s life. I’m curious if other folks have other ways of managing loot (and randomness) that they love and live by.
(And before I forget – I’ve recently freed up some time to do some games consulting. Ping me if you need help with your games!)