Multiplayer Logistics

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How do we get players to play together in a manner that fits their schedules? This is a key logistical challenge a designer faces when building multiplayer games.

The promise
We are seeing a blossoming of innovative multiplayer systems. In previous eras there were a handful of default models that games might use (matches, play-by-mail). Games today exist on a spectrum from fully concurrent to fully asynchronous and everything in between. A game like Dark Souls is predominantly single player, but includes interactions that are asynchronous (the leaving of messages and deaths) or fully concurrent (the joining of another player into your game for PvP or Coop.)

We are entering a golden era of multiplayer gameplay. Server costs are falling dramatically with the advent of cloud computing. Broadband internet and always on mobile connections are spreading rapidly across the globe. Business models like in game payments, crowd funding and service-based gaming are evolving to the point to financially support a broad range of long-lived communities. Designers are playing with these new capabilities to invent new forms of multiplayer gaming.

The challenge
However, multiplayer is both expensive to build and has a high risk of failure. Often teams invest 50 to 100% of their development budget into creating a multiplayer mode. It seems worth it. During development, the team plays every Friday and has so much fun they are convinced that multiplayer is what will turn their game into the next League of Legends or Counter Strike.

The real test occurs when the game faces a live population of players. Upon launch, multiplayer games often see only a few weeks of active multiplayer activity. Too many people show up. Then not enough. Players visit sporadically and the player experience is deemed unreliable. The active matches trickle down to nothing. The traditional matchmaking lobbies (a design from the 1990’s) are left empty and will never be full ever again. The multiplayer portion of the game dies a sad sputtering death.

I see this as a challenge of logistics. There were players who wanted to play. However the way that the game put those players together results in weak community that was unable to self sustain.

Are there atomic elements of multiplayer logistics that lets us approach the topic of inventing new systems in a more rigorous fashion? Simply copying multiplayer patterns from previous eras works poorly. To invent new multiplayer modes, we must have conceptual tools that let us clearly and concisely manipulate topics like logistics, concurrency and interaction schedules.

Concepts when talking about multiplayer

Here are some concepts I think about when designing a multiplayer game.

Interactions


You can break up any multiplayer system into a series of interactions. An interaction is anytime players interact with one another via a game system (be it chat, hitting one another, etc.) These are the multiplayer verbs of your game. Usually a game has a set of single player verbs (move, quit, etc) and another set of multiplayer interactions mixed in. Interactions have a wide range of multiplayer properties such as frequency, scope, mode, etc.

If you map an interaction onto time, it looks something like this

  • The player starts the interaction
  • They end the interaction
  • They wait for a response.
  • If no response is forthcoming, they leave.

Interactions aren’t a new thing. The structure is identical to that found in atomic game loops. However, instead of a single loop you have something closer to a figure 8 with at least two participants. These concepts go back to communication theory that Chris Crawford adapted to games design theory in the 1980’s. This is fundamental stuff that all professional game designers should know.

Initial loop:

  • Model A: Player formulates an action and a target player or group.
  • Action A: Player performs the action.
  • Rules: The results of the action are mediated by the game logic.
  • Response A: Player A sees the immediate results as generated by the game.
  • Response B: Player B sees the immediate results as generated by the game. Note that what Player B sees is likely different than what occurs for player A. This naturally leads to divergent mental models and enables gameplay concepts such as hidden information or Yomi.

Reciprocating loop

  • Model, Action, Rules, Response B: The target players tries to understand what happened and formulates a response.
  • From here the loop ping pongs back and forth between participants.

Frequency of interaction

What is the frequency of interaction necessary to yield the impression of concurrency? You may find that you need to interact once every 5 minutes in a strategic game like Civilization while you need to interact every 200 ms to create the same impression in a twitch-based action game like Counter-Strike. See the article “Loops and Arcs” for a more detailed explanation.

In general, the higher the frequency of interactions, the more information being communicated between players. This can increase the pace of relationship formation.

As with many interaction variables, there are distinct phase changes in the players perception as the frequency hits a threshold. Simply by changing the spacing between interactions, we get radically different forms of play (and associated logistical challenges):

  • Real time: Players perceive interactions as ‘real-time’ when the frequency reaches the point where: A player starts and ends an interaction and then sees a response before they move onto other tasks; interactions overlap. Chat, for example, can feel real-time despite there often being more than a minute between responses. Real-time systems have less need for persistence but are often more expensive to run and build.
  • Asynchronous interactions: The frequency at which a player can start an interaction and end the interaction and then quit the game without seeing a response is seen as asynchronous. Generally you build in some sort of persistence so that a player that logs in later can see the results of the interaction and formulate a response.

Types of interaction
There are a variety of interaction types. Think of these as ‘how’ players interact. For a much more in depth description of all the various multiplayer interactions, see Raph Koster’s seminal talk on social game mechanics.

  • Spacial avatar interaction: Two or more avatars interact with one another. Shooting players in Quake is the classic example. Following a player in Journey is another.
  • Spacial environment interaction: Players also interact through the intermediate environment. In Minecraft, players build castles that other players then explore. For a higher frequency example, in Bomberman, players place bombs that open up passages or do damage to others.
  • Decoration and Display: Players signal status, affiliations and history via what they wear or how they decorate their weapons, pets and houses.
  • Economic: Players give, trade or pay for various resources to transform or transfer to another player. This can be a typical sale of a sword to another player for gold. Or it can paying mana for a buff that boost the health of a nearby player. See Joris Dormans work on internal economies for more on this topic.
  • Text: The most common method of introducing language into an online game is through text. It tends to be low cost and there’s a rich set of tools (spam filters, stylistic conventions) for dealing with common issues. It tends to work best with a keyboard.
  • Voice: Voice offers additional nuance including emotions, age, gender and more. It has limits for group size, bandwidth and is notoriously weak when it comes to filtering.
  • Body language: In local spaces like on a couch or around a table, we pick up on high bandwidth communication such as facial expression, posture, body height and physical presence. When a tall pretty boy looks you in the eye and asks that you trade your rare treasure with him, you may be getting signals that go far beyond what is found in other types of interaction. This creates rich emergent multiplayer gameplay. However, it is also hard to mediate and incorporate explicitly into the game systems.

Size of community
There are also massive phase changes that occur as you increase the number of participants in a community.

  • 1 player: Mastery, progression, exploration, narrative are available as design tools.
  • 2 players: Communication, relationships, status, gifting, trade, cooperation and competition become available.
  • 3-4 players: Alliances, politics, gossip, othering/stereotyping become available.
  • Small group (5+): Group vs group interactions, Official leadership, role specialization, official punishment
  • Medium group (12+): Factions, barter economies, and banishment
  • Large groups (40+): Hierarchy (leaders and sub-leaders), Currency-based economies, role enforcement. Adhoc systems of government, public codification of social norms.
  • Very Large groups (200+): Merchant classes, market-based pricing, codified systems of government, underclasses, celebrity, propaganda. This is the point at which a players is guaranteed not to know everyone and official systems are required to make social norms work. (see Dunbar’s Number)
  • Massive groups (1,000+): Polling, city-scale production efforts. There are very few dynamics that happen at this scale that aren’t also explore with 200+ or even 40+ groups.

I’m defining these groups in the context of player interactions.  The actual game population may be much larger.  For example with trade in Realm of the Mad God, we saw simple trade interactions happen with as little as two people even in populations that are in the thousands.  Two good rules of thumb when considering group size is to ask:

  • Who does this action impact or target?  This gives a rough estimate of the group size your system needs to support.
  • Is a larger group size necessary for this behavior to emerge?  If not, you can usually get by by targeting your design at multiple instances of a smaller group size.

The actual transition points fluctuate around these numbers based off contextual factors. For example, the transition to the dynamics of a Very Large Group can occur as soon as 60 or 70 people if there are weak communication channels that stress a player’s ability to maintain relationships.

Also, large groups are inevitably composed of smaller groups. So as systems are added, the dynamics of lower number groups are still present.

The dangers of large group sizes: It can be tempting to make epic multiplayer games with thousands of interacting players that could theoretically all fit in the same room. However, the technology and design costs are high and the benefits weak. Past 150-250 players, your game is in territory beyond Dunbar’s theorized biological limit on maintaining meaningful relationships.  All those extra people end up just being treated as number or abstractions by your players. A simple sim or polling system can often capture the major benefits of the next highest group size. 

Realm of the Mad God was completely playable as an MMO with action sequences of 40-80 players and trade / hub interactions of 150.  Players did not miss the 1000s of players. 

This reality raises serious questions about the need for designs that emphasize ‘massively multiplayer’ experiences. Just because a concept sounds exciting (“a million people building a new society!”) doesn’t mean it is a smart design. Human social capacities are limited and we can (and have!) over-engineer multiplayer systems.

Scope of interaction
How many people does a single interaction impact? A player can interact with a single individual or they can interact with one of the group sizes listed above.

  • Targeting a player interaction at small groups: With smaller group sizes you get communication similar to a conversation. There is a clearly defined interaction loop that can stabilize on a set of shared vocabulary and social norms quickly.
  • Targeting a player interaction at larger groups: With larger group sizes you see more broadcast scenarios and interactions are broader, less tailored to individuals. When interacting with large groups, it is common for the massive response to flood the recipient with too much information. Extreme reactions are also more common as people talk over and past one another.

Degree of interaction

  • Parallel: Players can behave independently from one another. A ghost racing car rarely impacts another player. Often the primary benefit here is a sense of presence though it can also tie into lower frequency zero sum interactions like a leaderboard.
  • Zero Sum: The action of one player blocks or reduces the interaction of another player. In Habbo hotel, movement is a zero sum interaction since the placement of one character blocks another character from occupying the same spot. This was famously used as a griefing tactic to box in players.
  • Non-Zero Sum: The action of one player benefits another player. In Realm of the Mad God, shooting an enemy makes that enemy easier to kill for other players. Killing an enemy gives XP to everyone on the screen.

Matchmaking
Matchmaking is the computer mediated act of introducing players to one another so they might interact.

This is a very broad definition of matchmaking, but is useful in the context of the wide range of multiplayer systems available. For example, a traditional console title might match players together by requiring players in a shared lobby to manually join a specific game. In Realm of the Mad God, players notice groups of players on a shared map and teleport to them. Both are forms of matchmaking, but they appear quite different in the player’s mind.

You can treat matchmaking abstractly as another interaction with a wait time.

Matchmaking window

The time you have to introduce a player looking for a multiplayer experience to another player. If the window is too long (and the player is not entertained during the window), they will leave.

Matchmaking failure
When a player comes online and there is not another player immediately online, the players will quickly become bored and leave. There is often an implicit promise of a fun multiplayer experience and if you don’t deliver that in seconds, your game is judged as a failure.

What can be frustrating to the developer is that another player pops in a minute later and experiences the same exact thing. If one players sticks around long enough, another player will show up.

Calculating daily failure threshold: If the matchmaking window is W in minutes, then failure will occur when the daily active population is less than Minutes In a Day / W. So for example if people are only willing to wait half a minute, you’d need a daily active population of 1440 / 0.5 or 2880 players. Actual results will be lumpy because we are dealing with a statistical process and player populations peak around specific times of day.

This may seem quite reasonable, but if you are matchmaking primarily with small groups of friends, players may feel like no one they know is ever on.

Fragmentation
When the player population is segmented by social groups, game modes, players skill levels, time playing and other factors, it becomes fragmented. This reduces the actual concurrent player numbers available to the matchmaking system and increases the chance of a matchmaking failure.

Example of fragmentation: Suppose a game has 3 multiplayer modes and matches players into 10 skill categories. If the daily failure threshold is 2880 (from the previous example), then in the worst case scenario, you’d need 3x10x2880 or 86,400 concurrent players for everyone to get their first choice.

Fragmentation creeps into a design. Someone wants to add another event or another game mode. The code is free, so why not? Surely the players will self sort. They do a little, but mostly they wonder why the matchmaking experience is so painful and then leave your game in frustration. Avoid fragmentation creep and put players together in big easily matched buckets when possible.

Concurrency ratio
Any game has a number of active accounts and a number of players that are online at once. Players cannot be playing constantly and are often offline For example, an MMO might have 100 active subscribers, but only 10 of those are on at any one time. This would result in a concurrency ratio of 10:1.

Some typical concurrency ratios:

  • MMO: 10:1
  • Online Console Service (like Xbox Live): 25:1
  • Individual Console game: 150:1
  • Flash game: 250:1
  • Couch multiplayer: 1000:1

The Active User Trap: One common mistake is that developers assume that high active player numbers will result in robust multiplayer communities. However you really need to look at actual concurrent users since many game types have extreme concurrency ratios. A game may have 1000 players but when each of those logins last 5 minutes and are spread over a week, you’ll average 0.5 concurrent players. If your matchmaking system doesn’t deal well with these sporadic, tiny populations, the game dies.

Relationship strength
Not all player interactions are equal due to unique relationships between players. Players build complex social models of other players both in game and out of game. Strangers are understood through simple, stereotype-based models. Close friends are understood through complex individual models built up over thousand or millions of minute reciprocation sequences.

Building mental models of another human is a biologically expensive operation. We seem to be able to keep 5 to 9 detailed models active at any one time though we can store many more at various levels of detail. Friendship is rare, complicated and built over long periods of time.

There are numerous benefits and trade offs that come from gaming with strangers or friends and friend-based play is often highly desirable. Games can help create friends by promoted repeated positive interactions. The higher the frequency, the quicker the relationship evolves.

Relationship strength is a spectrum, but there are two commonly drawn categories

  • Multiplayer with Strangers
  • Multiplayer with Friends

Multiplayer with Strangers
Let’s tackle multiplayer between strangers online first.

Pros:

  • Anyone playing the game can be matched with anyone else with little regard for existing social bonds.  This model becomes immensely attractive when there is a small initial player base. Often this means if 10 people are online, 10 people can be playing together.
  • Strangers, particularly young males, historically tend to compete with one another. This means that player vs player games that emphasize open conflict are an easy means of generate fun for some stranger populations.

Cons:

  • Strangers have weak bonds and will not naturally engage in prosocial activities like collaboration.
  • Skill differentials matter since players tend to compete. This forces developers to focus on segregating experts from newbies and fragments the population.
  • Not all player populations thrive on overtly competitive gameplay. Some players prefer to collaborate. Others compete quietly for status by manipulating social relationships. These are difficult in stranger scenarios.

Multiplayer with Friends

Pros

  • Players are much more likely to schedule time together to play.
  • Cooperative and communication heavy activities are considered fun.
  • Mentoring between divergent skill levels is more likely to occur.
  • Competitive play is still valid.

Cons

  • There’s often little overlap between existing social groups and interest in a specific game.
  • There’s often little overlap between existing social groups and share scheduled.
  • Friend groups are small. Engaged players typically have 5-9 close relationships. Casual acquaintances may be higher in number, but in practice may act more like strangers. If you have 10 friends and the concurrency ratio for a service is 25:1, you will essentially never stumble upon them online.

Tools for dealing with multiplayer logistics

So far I’ve just talked about the concepts behind multiplayer. Now we’ll dig into some common patterns that make use of these. There are three broad architectures:

  • Match-based games
  • Room-based games
  • Asynchronous games

Tools: Match-based games

Due to the long history of event-based matches in sports and board games multiplayer computer games often are organized into matches that start at a specific time and stop at a specific time or win condition.

Matches are the default logistics model used for many console and PC-style online games. They are immensely problematic. The matchmaking interaction has a very narrow window during which it requires a full set of players to show up in order to enter the game successfully. If you don’t get in, you need to wait till the next match starts. If this time is longer than the wait window, you’ll quit. Considering concurrency ratios, fragmentation and the burden of a tiny matchmaking window, it is not surprising that only the most popular match-based online titles survive.

Scheduled Events
Ask people to show up at the same time. This essentially shifts play times so that they are on at the same time. Scheduling is an expensive planning activity on the part of the player. You’ll get a low overall engagement rate but those who do participate are likely to find others to play with. A special Halloween boss encounter in a MMO is an example of a scheduled event.

Events can be scheduled by the game developers or they can be scheduled by the players. Player scheduled events have the benefit of stronger social ties in play. Folks that get together for a board game night are such an event. The downside is that arranging meeting is a convoluted process (as anyone that tries to set up meetings with more than 6 people can attest). It often requires leadership or persistence, attributes that are often in low supply for lightly engaged players.

Regularly scheduled events
If you can make the event regular, people will get in the habit of being at a particular place at a particular time. This reduces the cost of planning for the player and they can just reliably show up at a specific time instead of worrying about conflicts. A standard Wednesday game night for a guild is an example of a regularly scheduled event.

Short matches
If matches are short enough (2 minutes? 30 seconds?) players that don’t get into the current match wait less time than the matchmaking window and thus are still around when the next match starts. Online word games do this, but it could be readily applied to other titles.

Spectating on matches while waiting
If you can keep players entertained by letting them watch the game in progress, you can lengthen the matchmaking window. Games like Counter Strike do this upon entrance into a server and upon death.  Chatting is often tossed into this mix since it is a nice downtime activity that can build relationships.

Bots during matchmaking to fill waits
Instead of putting players in a queue where nothing happens, put them directly into a match with bots as the opponents.

Getting bots that act like humans is often a tricky Turing test to pass. Not letting players talk and having a very narrow window of expression helps.
When players learn this is happening they will start to distrust the game and question if all opponents are bots.

Mechanical Interdependencies
Create activities that require multiple people to show up in order to achieve success. Not showing up lets down the group and thus increases the social pressure to show up. This can take the form of explicit roles or by limiting resources so that players can’t accomplish large goals independently.

Tool: Room-based games

Ultimately match based games result in often insurmountable logistical issues for smaller games. A favorite alternative is room based games. Unlike a match which has a distinct start and exit, room-based games create a persistent playspace that players may independently join the game in progress (or leave the game in progress)

Rooms have a maximum number of ‘slots’ or spaces for players to join them. Once the room is full, no more players may join. This dramatically reduces the load on matchmaking. All you need to do is find a room with an empty slot available and dump players into it.

The downsides to rooms is that they eliminate certain game types. Group starting times are obviously out which eliminates most traditional sports. Games with progression arcs result in players that start at different types having differing levels of progress. You need to get creative.

A game like Journey is essentially a room based game with join and leave in progress. The max slots was 2 and as long as there were two concurrent players you could have a multiplayer experience.

Most MMO’s are room-based games with very large rooms.

Join In Progress, Leave in Progress
One reason why rooms offer such improved logistics over strict matches is that players may join or leave at any time.  Since it is highly unlikely that everyone will leave at once, especially in games with a predominance of parallel interactions, shortly after one person leave another person will join and you’ll get a consistent average population in the room.

Pure match-based games are often quite rare because many popular games treat the individual server as a room and the match-based elements are merely scoring atop a dynamic population of players joining and leaving in progress.

Elastic Room Instances
Create and remove rooms to fit that maximum currency. Given a room of maximum size N, you create new rooms so that the number of rooms equals Concurrent Player / N. So if 10 players are online and your default room size is 4, you’ll make sure there are 3 rooms to join.

To collapse a room, just wait until it naturally empties out as players leave the game or kick people out due to some in-game event intended to free up the instance. Once the room is empty, delete it. By giving rooms priority, you can fill the highest priority rooms first and kill off the low priority rooms. The result is that almost all rooms are constantly full and only the remainder are left alone.

We used this when creating world shards in Realm of the Mad God. The world generally felt full even when the concurrent population fluctuated dramatically.

Default to single player gameplay for rooms with one player
Room-based games have the ‘remainder’ issue. A given maximum room size rarely divides evenly into the concurrent population. If the room size is 2 and there are 3 players online, there will be 1 player placed in a new room by themselves.

To deal with this scenario, it helps to have a game that is playable as a single player game until the next player joins the room.

A retail game like Dark Souls assume very low concurrency and plays almost entirely as a single player game (with light async ghost interactions) The concurrent matchmaking is a silent parallel interaction that happens without interrupting the single player adventuring. Since having a second player in the right place at the right time is uncommon, the game instead treats it as a special occurrence. (Note that since Dark Souls promises a single player game, they make the concurrent multiplayer experience opt-in through the use of soapstones. The soapstones signal that a successful match has occurred and the player must accept it. Respect your initial promise when you mix single player and multiplayer interactions.)

Asynchronous techniques

Play-by-mail
A player completes an interaction and then the game signals to them that they have a very long period of time before the other player responds. The next day or so, the other player sees the first player’s action and composes their response. This can take place over days.

Words with Friends is a modern example of this technique, but the practice goes back decades if not centuries (if you include play-by-mail board games). It is an intimate method of play that works well with text communication much like instant messages or email. Play-by-mail is very amenable to play between friends.

A downside is that players are deeply impatient. A single turn may not be all that satisfying and then having to wait multiple days for a response has a major drop off in retention. There are still matchmaking issues if fragmentation is too high but the explicitly long wait window ensures players don’t get too worried that the system is broken (they may just not like the system).

The other downside is that in turn-based games, the non-response of one player may block another player.

High Capacity Play-by-mail
One solution is for a player to start a large number of play-by-mail games. Given a response time of T days and a desired average wait time of W days, then the optimal number of games going at once is T/W. (So if you want a game popping in every hour and it takes 24 hours to response, then you need 24 games going.)

One added benefit of all this is that player response times are semi-random. This acts as a random reinforcement schedule and can result in very long term retention.

The downside to the technique is that it requires players to start up a lot of games in order to reduce the wait window and motivating players to do so is tricky. Automated game matching may be an answer.

Inviting
You can leverage active players to invite new players to the game. These players often have strong relationships with the player and can potentially act as a source of new players into the game.

Match with friends
Since async forms of multiplayer rely heavily on players to come back later, their game designs often relies on social connections outside the game as a form of additional pressure. If you can get people to invite or match with friends (as in Farmville) a lack of reciprocation in interpreted as putting their existing relationships at risk. The threat of being rude or seeming like you don’t care to someone you like is often enough of an incentive to encourage returning to the game.

Systems that play off existing relationships run the risk of alienating players. Players not invested in the game tend to find mechanical interactions annoying. Authenticity and intentions matter when it comes to human relationships.

Visiting
In building games, you may create a persistent structure such as a town that other players can then visit independently of your presence.

Clash of Clans uses this when players attack your town. The town is a persistent structure that then acts as a level for the other player to conquer.

Visiting usually boils down to a simple resource exchange despite the trapping of being something more meaningful. The issue comes from questions of what happens when multiple people visit at once and the solution is to spin up different instances.

Jason Rohrer’s The Castle Doctrine uses the unique design of making visiting a blocking interaction. This opens the possibility for permanent changes being made to the visited location. One can imagine more complex versions of musical chairs as the foundation for some innovative designs.

Ghosts
Record players behaviors and then play them back alongside the player in a similar environment. This works particularly well with parallel interactions like you see in racing games. It can also work with the rare non-zero sum interactions like you see in multiple time track games like Cursor 10 or Super Time Force. Ghosts gives a sense of presence but removes the matchmaking time constraints.

The downside is that ghosts usually works poorly with blocking or zero-sum interactions. The other downside is that if the ghost data and the environment get out of sync, then the ghost data becomes invalid. These can be alleviated slightly by either skipping blocked actions or falling back on AI behaviors that manage exceptions

On a more abstract level, ghosts are just tracks of player data that can be replayed on any sort of trigger. They can be triggered at the start of a race, when the player comes onscreen or when the player uses the special amulet of Ally Summoning.

General practices

This essay has covered a lot of ground (and is still incomplete!), but I’ll leave you with a few quick recommendations.

  • Don’t fragment your matchmaking population. Be very wary of the point at which your concurrent game’s matchmaking fails due to high concurrency ratios.
  • Use room-based methods where possible, not match-based play.
  • Persistence is your friend since it enables asynchronous interactions.
  • Relationships are your friend since they increase retention. Try to build them where possible.
  • Prototype early and deal with low populations density issues during the prototyping phase.

Conclusion

I remain quite excited about new multiplayer games. When I look at the theoretical advances being made with game grammar via Joris Dormans internal economies and some of the multiplayer concepts in this essay, the unexplored space for new forms of game seems vast. If you want to make your mark on our modern world, make a great multiplayer game. Solve the logistical issues that prevent people from playing together and build a game that spreads quickly and easily throughout communities.

take care,
Danc.

Notes and references

Topic for future investigation
Concurrency is a statistical process; there’s a chance of a player being on at a given time. This whole topic could stand to be dealt with in a mathematically more rigorous fashion.

Essays and books

Prototyping Challenge: 3D Modeling Tool for 2.5D RPG Art

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I was creating some 2.5D art for an game jam recently in a perspective similar to a 2D RPG like Zelda. Naturally my next step was that I started thinking about how you might recreate this style using a custom 3D modeling tool. Yes, another art tool design challenge. 🙂

I’ve played with voxel editors in the past, but I’m not completely happy with the blocky results that they produce. So where’s a quick and dirty minimalist 3D modeling tool design with the following goals:

  • Enable artists to make beautiful 3D models that include curves, ramps, intersecting shapes, and other sophisticated elements.
  • Make a 3D modeling tool that is as easy to use as a pixel art editor. In particular, I’ve realized that this editor can avoid a lot of the messiness that usually appears when you include 3D rotation.

The result should be art that is still stylized, but has still has an immense range for a talented illustrator. I’ve made other attempts at this in the past, but I think this one has legs. 🙂

Target style

I put together a set of 2D art for a future game jam. The resulting 3D modeler in this essay should be able to easily create everything in this image, plus a whole bunch more.

When making this art, it occured to me that there is a rather magical property of the traditional 2.5D view that I don’t think has been well tapped before. Once you adopt a forced 2.5D perspective, most 3D primitives are possible to be represented in a 2D plane. This makes a ton of traditional 3D operations dramatically simpler. You can think of a 3D space being reduced to a couple of 2D controls

  • Top Plane: The top of a cubic volume enclosing the primitive.
  • Front Plane: The front of a cubic volume enclosing the primitive.

Here’s an example with a cylinder and the planes made explicit

With these, you can do basic moving and scaling of the object. The trade off is that you lose rotation.  My bet is that like voxel editing, you can lose rotation and still end up with a vast visual play space.

If you get fancy, you can flip an object 90 degrees forward so that:

  • Front Plane: Extrusion, XY position
  • Top Plane: Scale, XZ position

The basic flow of modeling

Here’s what you do to make a model.
  • Add primitives to an object
  • Arrange (scale, position) and color them.
  • Combine these tile-like objects together in a game to create complex scenes.

List of Operations

Here’s the list of features that a simple prototype of the editor would support.

1. Add a primitive
You can add a primitive to the scene

  • Cube
  • Cylinder
  • Arc (half cylinder)
  • Ramp (NSEW variants)

2. Select a primitive
Click on a primitive in the scene to select it.

  • 6 dots appear
  • The bottom 4 define the front plane.
  • The top 4 define the top plane.

3. Move in XY plane
Grab the front face of a primitive to move in the X,Y plane

4. Scale in XY plane
Grab the corners of the front face of the primitive to scale it.

5. Extrude in Z
Grab back corners or edge of the primitive to extrude it.

6. Move in XZ plane
Grab the top of the primitive.

7. Select a color
Once you have selected a primitive, click on a color from the color palette to change the color.

Constraints

There are a variety of limitations enforced that make modeling far easier and closer to pixel art.

  • Snapping: All operations snap to a 16x16x16 grid.
  • Primitive budget: Each object is made up of a total of 32 primitives.
  • No rotation of primitives. Again, this is a hard problem in 3D. So we avoid it.
  • Limited colors: All primitives use the same 16 color palette. This allows us to appear to make complex objects out of multiple primitives by simply connecting simple shapes of the same color.
  • Surface details are generated using other primitives. Primitives whose surfaces are coplanar are rendered cleanly as 2D textures. See bricks in the example above. Use creation order or order in the selection list to determine what shape is on top.

Bonus features

The above features are the minimal set.  There are other features you could add to flesh out the tool.

  • Selection list: A list of all 32 primitive in the object. Click on one to select that primitive. Thumbnails are a plus. Bonus points if you can rearrange these.
  • Hiding/Showing primitives: There is an eye icon in the selection list next to each primitive and you can hide or show that.
  • Rounded corners: Give the selected primitive rounded corners. These are in 1, 2, 3 or 4 grid width rounds.
  • Flip Front / Top: Rotate the primitive forward or backwards 90 degrees. Example: A flat disc becomes a wheel.
  • Cutter object: The selected primitive now subtracts from the solid instead of adding. This lets you cut holes.
  • Textures: In addition to colors, you can specify some simple textures.

Special rendering tweaks

There is a reasonable chance that objects will look like rather ugly without the right rendering. Play with this till you get something that works.  Here’s what I take into account when drawing things manually.

  • Parallel Light source (think sun) from the top so the front is in slight shade.
  • Shadows on other objects. Slight ambient occlusion will tend to make the objects feel more connected.
  • Shading objects darker near the bottom and lighter near the top help preserve a sense of depth.

Test cases

Making art tools without art samples is tricky. The following are test cases that you can try to replicate once you’ve built the basic tool.

House

Factory

Stone

Tree

Woodchuck

If anyone makes a prototype, I’ll link to it here.
*Update!* Angry Octopus has a prototype.  (The more the merrier, so keep making ’em.)
All the best,
Danc.

A single game as a lifelong hobby

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Do you finish one game and then move onto the next? This is the dominant pattern of play for gamers. What happens when players stop consuming and starts investing in a single evergreen computer game for years on end?

Players of traditional games specialize

Across the 5500+ year history of gaming and sports, players typically focus on a single game and turn it into their predominant hobby. A chess player may dabble in other games, but chess is their touchstone. They join chess clubs, they play with fellow chess fans and they spend 90% of their gaming time playing chess. Overall, players specialize.

Such players do play other games, but to a far lesser degree.

There are also communities that embrace the identity of being good at multiple games or sports. These are a minority.

And some are inclined to claim all hobbyists are ‘athletes’ or ‘players’ and thus unified in some common tribe. Such verbal gymnastics rarely provide much insight into a dedicated hobbyist’s specific passions or the nature of their community.

Specializing in a hobby occurs for many reasons. Traditional sports or games often have the following attributes:

  • Evergreen activities: You don’t beat them. You stop when you get bored. Usually they consist of nested loops that operate on time scales of up to a generation. Consider the nesting of Match : Event : Season : Career : Training the next generation.
  • High mastery ceiling: Most are nearly impossible to master completely. You can always get a little better. You can always get better at Go, Soccer or Poker.
  • Strong communities: There exist strong social groups of like-minded players that have their own group norms, hierarchies and support structures. To be a dedicated basketball player is to be part of an extensive basketball playing network.
  • Life long identities: Someone who excels in the game starts to identify as a member of that group. The game becomes source of purpose bigger than themselves. They can look back on their life and say “There were some ups and downs, but I’m secure in my accomplishments as a player of game X”
  • Grass roots or service-based business models: Any cultural structure can be fruitfully analyzed by understanding the flow of money. Many traditional games have extremely low barriers to entry. It costs little to access the initial equipment. Often items like decks of cards or chessboards are either communally owned or purchased by a family and one set of equipment serves multiple participants.At higher levels of play, cash flows into the ecosystem through purchases of more advanced or higher status equipment or various service, membership or event fees. In all cases, the businesses involved have strong financial and culture incentives to get you playing and keep you playing.

Players of digital games consume

The hobby of computer or console gaming follows a different usage pattern; gamers play a wide variety of games. NPD claims core gamers buy an average of 5.4 games in a 3-month period. In a recent discussion of Steam purchases on Kotaku, commentators chimed in that they had purchased 100 to 800 games. These are played for a period of time and then set aside so that a new game might get some play.

These players specialize far less. They may prefer a genre of games such as RPGs or shooters, but they’ll still consume many games within that genre.

Why the difference in playing patterns? Commercial digital games have some distinct attributes that encourage serial play instead of evergreen play. Not all digital games fit this mold, but the trends are worth noting.

  • Complete-able games: Most computer and console games can be completed in 5 to 40 hours. It is rare that you find digital games that retain users longer than 6 months. Actual playtime is shorter than the official length since most players do not complete their games and even fewer play through a title more than once. Compare this to the generational nested loops of traditional evergreen games.
  • Narrative and Puzzle-focused gameplay: The majority of the gameplay is focused on high burnout single use puzzles or evocative narrative stimuli. Designers spend their budget handcrafting specific scenarios for maximum emotional impact the first time through.
  • Low mastery ceilings: Since the design goal is to move players through the content of a game as smoothly as possible, the game mechanics are generally balanced towards the average skills of first time players. It is rare and surprising when a single player narrative computer game offers examples of masterful play. All this leads to early burnout where players rapidly become ‘bored’ and put the title aside.
  • Weak player identities: It is difficult for a player to establish their identity around their excellence in any one game. To be a good Braid player just isn’t that special. Lots of other people have walked the same path; there is little player creativity and outside the occasional Let’s Play video, few people care.
  • Content-focused business model: Digital games businesses have a strong financial incentive to get you to pay upfront and then move onto their next title. Games are treated as a content or boxed product business. An optimal strategy is to put high quality boxes on shelf (either physical or virtual) and get people to buy as many boxes as possible. Since exciting content remains a large cost center, there is ever increasing pressure to make games flashier and more marketable on the front-end and shorter on the back-end.

Shortness of play is perhaps the key reason why players end up consuming multiple games. With gamers spending 16-18 hours a week gaming, it doesn’t take long to burn through a single title. When a single game fails to entirely fill a person’s leisure time, players buy additional games. Only a set of multiple consumable titles provides enough engagement for someone to make a full-fledged hobby out of content-based games.

This fits the general profile of a media hobbyist. As we shifted from evergreen hobbies to digital retail-focused games, we trained users to behave in a fashion similar to that of a reader who reads many books or a movie goer who watches many movies.

A media culture

To be a ‘Gamer’ is to buy into numerous requirements that only exist to enable the creation of easily consumable media products.

  • Reviewers exist to help players select their next media purchase
  • Critics exist to demonstrate how media conveys a message to society. They are trained (if they are trained) in other media-centric fields such as movies or literature. There is little systemic thinking since media is first and foremost not a functional system but an evocative stimuli.
  • The form of popular games is determined by whether or not it fits in a media box. Form is the standardized structure of a piece of media. The 2-hour narrative movie is a form of video. The 300 page novel is a form of writing. So too is the 14-hour adventure game or the level-based narrative FPS.
  • Stores and storefronts exist to sell the hobbyist a steady trickle of new media. Since media creation is expensive and the share of a player’s time is small for any single piece of media, aggregators of content are typically 3rd parties that don’t produce all the media themselves.
  • Communities are built around mass media that act as a shared experience for large populations of consumers. Big brands like Mario, Mass Effect or Final Fantasy form cultural anchors much like Star Trek or Star Wars. Comparisons, reminiscences and fan fantasies about future sequels or expansions are common.

Digital evergreen hobbies

Into this media-centric ecosystem we’ve seen the reemergence of major games that hew more closely to the traditional games of old. MMOs like World of Warcraft or MOBAs like League of Legends are services. A digital game like Minecraft ties into numerous communities and is often played for years. Some like Halo or Call of Duty cleverly camouflage themselves as traditional consumable boxed products all while deriving long term engagement and retention from their extensive multiplayer services. These games share many of the attributes of older hobbies:

  1. They attempt to be evergreen.
  2. They have high mastery ceilings and robust communities.
  3. Many, especially eSports, replicate the nested yearly loops of a traditional sport.

    Each of these games is a hobby onto itself. People predominantly play a single game for years. In one poll of 5400 WoW players, 49% claimed to never actively play another MMO.

    The rise of services

    This shift to services is accelerating, driven by business factors and steady player acceptance. Developers are slowly coming around to the realization that an evergreen service yields more money, greater stability and a more engaged player base. Experiments of the past few years with social, mobile and Steam games suggest that microtransactions will likely become a majority of the gaming market. They already represent 70% of mobile revenue and continue to grow rapidly on other platforms.

    This new revenue stream places new constraints on game designs.  Types of laboriously handcrafted content that was once feasible when your game was played 10 hours is no longer profitable if revenue trickles in over hundreds or thousands of hours of play.  Deep mechanics once again matter.  Communities you want to spend time in become a competitive advantage.

    There are indeed manipulative companies scamming settlers in this newish frontier. Don’t act so surprised. This is the case for any frontier and this is not the first time games have attracted disreputable developers.  Look beyond the flashy, inevitable crooks, just as you looked beyond the licensed games, the porn games and the gambling games that infest your typical game markets.  Look at the big picture and observe where the new opportunities for greatness blossom.

    No, they won’t cross over

    These new evergreen players become hobbyists, but not media-centric gamers. This is most evident in the audiences that play ‘casual’ social and mobile titles. Many of these players never bought into the current gamer culture. It is common to see someone deep into Candy Crush and when you ask them if they are a gamer, they will deny it. They do not ‘game’, they never have ‘gamed’. They don’t share a common heritage of Mario, Zelda, COD, Halo or any of the mass media touchstones that unite current gamers. What they have is a wonderful hobby that in their mind has nothing to do with existing computer games.

    There exists a fantasy that somehow new players will get hooked on one game and then transfer over to consuming other games. Since this assumes a play pattern of high volume serial consumption, I doubt that this will occur. Great evergreen games leave little room in a hobbyist’s schedule for grand feasts of consumable content. You don’t finish a great hobby and then look for your next dalliance. You keep playing the game for years or even generations.

     The perfect service-based game is one worthy of your entire lifetime of leisure.

    If this seems an exaggeration and current titles feel unworthy of this high bar, wait a while. Developers are very talented. And the financial incentives to build the perfect service-based game are strong.

    Not one gaming hobby but many

    So where does that leave our understanding of ‘gaming?’

    • Some people avidly knit in their leisure hours.
    • Others play a creative game like Farmville, Dwarf Fortress, Minecraft or the Sims.
    • Others participate in a social online game like World of Warcraft, Eve or Facebook.
    • And then there is a small but active community of proudly old-school Gamers that like consuming puzzles and story media.

    What we currently think of as ‘gaming’ becomes just another hobby amidst a vast jungle of digitally augmented hobbies.

    There are those who might see this as a threat, but that is mere fear talking. Existing hobbies tend to last for at least a generation. Those who’ve tied their identity to consuming media-style games as their hobby will stop participating in the hobby when they die. I expect to see 80-year olds still buying adventure games because that is what they were raised on and that is what they love. Niche producers can make good money serving these avid fans.  The rise of new hobbies thus do not invalidate a current hobby.  In fact, you’ll have media-centric games for at least the rest of your life.

     Though each hobby likely will need to compete for new members.

    Impact on the cultural ecosystem

    With this shift comes change. The following may challenge your existing expectations.

    • Specialized interests, not shared experiences: The drop rates on defense potions matters little to your typical gamer. Yet it is of earth shattering importance to the community of Realm of the Mad God players, impacting hundreds of hours of their life. At a certain level of mastery, the language used to describe in-game concepts becomes indecipherable to casual audiences. This inhibits communication with external groups, but facilitates bonding within the group.
    • Deep systemic analysis, not broad media criticism and reviews. Hobbies are predominantly comprised of human systems and communities, not texts to analyze or boxes to sell. Political, anthropological or economic forms of discourse are more appropriate yet there are few game critics trained in these fields. Successful commentators are typically past players with a master-level understanding of the hobby. They are rarely dilettantes flitting from media event to media event.
    • Unique cultures, not mass cultures: A hobby can develop a set of inward facing social norms. This can be a negative if extreme viewpoints are allowed to fester. It can also be a huge positive and promote inclusivity, equality and long term positive relationships. Each hobby is a cultural petri dish that need not adopt dominant tropes or values.
    • Participation, not marketing campaigns: New players of a hobby hear about it from a friend or stumble upon a free trial. They participate first and see if they enjoy the lifestyle that the hobby promotes. Big bang media events can flood the early stages of the acquisition funnel, but they do not directly result in revenue or a sustainable community. 
    One aspect that surprises me the most is the stealthiness of inwardly sufficient hobbies. A smoothly running process is barely newsworthy for those unfamliar with the hobby. Over 5 million people partake in Geocaching, one of the greatest modern games ever invented.  Yet other than the occasional human interest story, it rarely breaks into the public consciousness. What would a media-focused rag say?  “People are having healthy fun…still.  Just like they were last year.” That’s not news. There is no new box to hype or content to whinge about.  There’s no advertising to sell. So silence is the default until you look inside the vibrant magic circle. Geocachers return the favor by labeling outsiders Muggles.

    Let a thousand flowers blossom

    The concept of one true gamer community will be less feasible as evergreen hobbies grow in popularity. Instead, we have a crazy mixing bowl of diverse, separate, long-term communities. Few will share the same values or goals. Few players will consider themselves having anything in common with players of a different game.

    Social organizations such as PAX will still promote common ground, much like the Olympics promotes common ground between athletes. But day-to-day cross-pollination will be rare.

    I personally value a wild explosion of diversity. We need less mass culture and more emphasis on vibrant, generative communities instead of passive industrialized consumption.

    The existing society of players may be tempted to deal with those not like themselves negatively through shaming (“I can’t believe you play Farmville, stupid person!”) Here’s how we might instead react positively.

    • Freedom of Play: Like freedom of religion, any player has a right to devote their life to any game even if it isn’t something enjoyed by another player.
    • Mutual respect: Any player deserves your respect for their hobby even if you do not personally understand it. Avoid stereotypes and engage with the person.
    • Willingness to explain: Any insider should be willing to explain to an outsider how their hobby works. Proselytize by inviting them to play with you. An open-minded outsider should be willing to listen.

    The fact that individual hobbies exist is not new. The shift comes from realizing that individual digital hobbies will soon to be the default play pattern. Adapt accordingly.

    take care,
    Danc.

    References and Additional Links

    Note: Gamers often wonder why Farm Equipment simulators sell.  Judged as mass media, they are horrible.  Judged however as an independent hobby, they have many of the attributes of an engaging lifelong interest.  If you laugh at them, it is because you are outside their tribe and ignorant. 

    Coercive Pay-2-Play Techniques

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    Coercive monetization models are used by many of the large corporations that dominate the “Pay to play” (P2P) charts in retail, console and mobile.

    They employ carefully engineered psychological traps intended to defraud ignorant players of their money. This shocking expose shines a light on their dark, inhumane practices. Be forewarned: Despite extensive examinations of opinions similar to my own, I am intentionally unaware of any company that manages to use these systems of coercion in a positive manner.

    1. Purchasing sight unseen

    The primary method is to get a player to purchase something without ever playing it. If you can get players excited about a new game, most will buy it with little more to go on than a box shot and a video. Many secondary techniques tie into this basic strategy of deceit.

    Companies intentionally avoid releasing demos or providing free trials in order to increase the number of purchases independent of whether or not a player might enjoy the actual game.

    2. Use of propaganda to artificially increase excitement

    P2P publishers feed players videos, paid end caps, advertisements and canned previews. Often the marketing spend for a title is greater than the development budget. It is cynically assumed that if you shout targeted propaganda at an audience, they will buy in increased numbers.

    3. Limiting information to prevent alternate opinions

    Since no one can play the game, the publishers are able to keep any information about the game tightly focused on the most effective message that drives purchases. Heavy use of the captive fan press ensures that press releases are repeated verbatim.

    4. Distorted game design

    Since all that matters in order to make the sale is the initial propaganda, the actual game design is sacrificed. You make money by having a catchy theme, pretty graphics and the ability to turn out short sequential games rapidly. As a result, P2P encourages developers to short, consumable interactive sequences with shallow, low risk, well-worn mechanics. I hesitate to call them “games”. Most are little more than a collection of puzzles or QTE that can be clicked through in 5 to 10 hours.

    Also because all that matters is if someone buys the box, game designers need not worry much about retention or engagement. Most P2P games are built with little care given to the final few levels. It is common that 50-70% of players never complete a P2P game.

    5. Targeting those least able to understand modern sales techniques

    Though it might be a stereotype, most P2P titles target poorly socialized teenage males. Unlike women, an educated demographic that makes the majority of purchasing decisions in Western markets, these younger males are likely to naively buy into the pre-sales propaganda without critically questioning its actual purpose. Now if these were shopping savvy 40-50 year old women, you might be willing to say “Let the buyer beware”, but can we really expect an audience that has difficulty buying fresh boxers on a regular basis to purchase games responsibly?

    6. Bundling and time-limited sales

    One of the more effective methods of psychological manipulation is to bundle multiple products together and offer them at an apparent discount. Players perceive they are getting a massive value when in fact they are just accumulating more games that they are unlikely to play or even enjoy.

    This also preys upon those damaged individuals that possess strong hoarding inclinations. How many times have you seen players with vast collections of hundreds of uncompleted games? This is an obvious sign of mental illness which P2P developers are all too willing to exploit.

    7. Skinner Boxes

    Players end up treating game purchases like a slot machine. They may buy dozens of games in a year, but only one or two will be worth their time. This creates a random reinforcement schedule that sets up a form of psychological addiction. Players find themselves stalking the latest game sale in the hopes of getting a new hit of gaming goodness. Of course the system is rigged so that it is nearly impossible to know upfront whether the game in question is worth their money. So they press the ‘buy game button’ and spin the wheel. Oh, the Steam sales!

    In the process a few ‘whales’ spend hundreds or even thousands of dollars a month on games. Some even purchase meaningless, ostentatious ‘arcade cabinets’ or inordinately expensive peripherals that retail dealers call ‘consoles’. Most of these claim their purchases are part of a healthy hobby and have no regrets. However, I’ve gone out of my way to find adults with poor spending habits that have stripped their meager bank accounts to ‘collect ’em all’. Some young men holding down minimum wage part time jobs were forced to eat ramen in order to continue their spending spree. This deceptive form of capitalist gambling, aka ‘shopping’, ruins ruined lives.

    Other means of manipulation

    This small sampling of techniques points to the deep corruption inherent in both making and selling P2P games. There are numerous other other manipulative practices:

    1. Use of fake tribalism: “Genesis does what Nintendon’t”
    2. Collector’s editions: Use of socially questionable materialism to artificially increase ARPU.
    3. DRM: The pay before you play model leads directly to DRM as a means of artificially blocking non-paying users from trying the game and seeing if they might like it. Piracy becomes meaningless if you provide a long term service or hobby, but that is not the optimal strategy for money-grabbing P2P firms.
    4. $60 price tags: If you are selling a fantasy product, you might as well take any willing mark for as much as possible.
    5. False console cycles: With a mere billion dollars on fresh propaganda, P2P companies know that they can artificially stimulate a mass of people to invest in a new console and then repurchase their old games all over again.
    6. DLC: Since P2P is essentially about churning out cheap, consumable content, these “games” only get upgraded if the expansions take the form of cheesy modular DLC. Mechanical upgrades that improve the core gameplay or social systems are rare since there is little financial incentive.
    7. Overemphasis of reviews instead of actual player behavior: Good reviews are just another form of message control and propaganda. This is why dev bonuses are tied to Metacritic scores instead of statistically valid player metrics.

    There is a substantial human cost to these shenanigans. Through I have zero hands-on experience making P2P games (and honestly have no interest in them), several inexperienced indie friends attempted to make a P2P game. After one attempt in a crowded and competitive market, they failed to buy a Tesla. Since I personally enjoyed the prototype they showed me at a game jam, I think it is clear that all the blame for their game’s failure (and subsequent public emotional turmoil) can be laid at the feet of the P2P business model. This is not the silver bullet you fantasized about as an inexperienced non-developer.

    In closing

    In the end, P2P hurts gamers and the game industry as a whole. I urge you as an ethical designer to reject this immoral practice. The egregious abuse of players by popular pay-2-play practitioners makes any use of P2P invalid. I question if it is even possible to make a moral P2P title. (Indies should especially distance themselves from this culture that is little better than legalized gambling.)

    What we really need is to make great games where players can try the games for free and then make an informed decision on whether or not the game is worth their money. In an ideal world, games should be meaningful long term hobbies that enrich a player’s life, not some cynical scam job reliant on engineered propaganda spam, sexed up artwork, forced sequels and a captive press.

    Imagine games where players only pay their hard earned cash if they find the gameplay meaningful. They can try any and all of a game for free as long as they want. If they don’t feel the game is adding to their life, then they can leave at any time.

    Sadly, such an honorable course seems unlikely. No doubt that we’d see overblown rhetoric and misappropriated science denouncing such an idealistic experiment by those deeply involved in coercive, yet highly profitable, P2P businesses.

    Yours truly,
    Monsieur Troll

    PS: When posting comments be sure to see if it passes the “I understand that this essay is satire” check. I’d hate for there to be any sort of embarrassing misunderstandings.

    PPS: These are exciting times where business models and design problems are evolving radically and rapidly every month.  That multiverse some call ‘free to play’ is mutating along dozens of different variables. The old familiar retail ‘pay to play’ model is also fracturing into something new.  If you think you grok how any current business model works, you probably are several years behind.

    The uncertainty and change can be scary. Maybe you recently played a game that was different than ones you played as a child. Or you heard some stories. And now you feel the urge to express your emotions via loud opinion spewed onto the internet. Oh look…more precious time passed while you were ranting and you learned nothing. 

    Polarized views backed by mere opinions fails to move the science and craft (and ethics) of making games forward. What are you personally doing with code and art and functional gameplay in order to carve out a viable, sustainable future for great games? Let’s talk about that.  Mere gnashing of teeth, often witnessed in the same form as the essay above, is noise that drowns out thought. 

    Understanding randomness in terms of mastery

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    Instead of categorizing games as either ‘games of skill’ or ‘games of luck’, I see games with randomness as being a subset of ‘games of mastery’. This view helps the designer see randomness in games as the intersection between both the player skill set and the game mechanics. By understanding the underlying skills involved in mastering randomness, we can build more meaningful games.

    Discerning cause and effect from noise

    One of the fundamental elements of any game is how the player learns to distinguish useful patterns from environmental noise. Without a mental model of how a system works, most games appear random or at least arbitrary.  (Randomness is a concrete property of a rule set. However perception of randomness is a state of mind that can exist independent of the rule set.)

    With time, experimentation and practice, some players build up a mental model with conceptual tools that let them manipulate the system to reach desired outcomes. They transform from unskilled players into skilled players.

    The idea of noise is a broad one. A cluttered scene with hundreds of objects is said to be noisy. A combat scene rife with particle effects and crazed camera angles also is noisy. Noise is the extra stimuli that hides the next conceptual insight.

    The perception of noise vary based off the player’s skill in understanding and filtering various classes of noise. A chess board in the middle of a game is highly noisy to a new player trying to simply figure out how a knight moves. All the extra pieces and their subsequent movements are extraneous to what the player needs to learn next. However, that same chess board offers reams of insight to the advanced player. They are able to process the information and predict future outcomes based off their sophisticated cumulative models of chess cause and effect dynamics.

    Categories of noise
    Noise comes in a variety of categories that flow naturally from the basic skill atom we see in most game loops.

    • Action Noise:  Uncertainty, extraneous elements or unmastered complexity in the player action. 
    • Rules Noise: Uncertainty, extraneous elements or unmastered complexity in the processing of the blackbox rules. 
    • Feedback Noise: Uncertainty, extraneous elements or unmastered complexity in the stimuli that shows the effect of the player’s action. 
    • Model noise: Uncertainty, extraneous elements or unmastered complexity in the player model of the situation. 

    Each class of noise has its own category of skills associated with filtering the meaningful signal. In a hidden object game, the visual complexity of the scene creates noise. Advanced players cope with this by mastering silhouette detection, efficient visual search patterns and object association skills. A good hidden object game players is measurably better than a new player.

    Randomness as a form of noise

    From this viewpoint, randomness in the form of internal dice rolls can also be treated as a class of rules noise. There other forms of randomness that map onto Action Noise and Feedback Noise, but randomness as rules noise seems to cause people the most trouble.

    Since randomness is just another form of nosie, we can expect it to have several key properties:

    • A model: There is often an underlying pattern or model that helps players deal with the randomness
    • Model ignorance: This model will not be readily apparent to new players. 
    • Learning curve: With time and education, players will learn how to appropriately deal with randomness. 
    • Learning variables:  There are also likely important variable for the system that make learning to deal with a system’s randomness easier or more difficult. 

    Skills for player modeling of randomness

    Probability and statistics provides use with a set of mathematical skills for dealing with randomness.  Players instinctually use roughly equivalent concepts but modified by a set of well document unconscious biases.  Instead of summarizing all of probability theory, let’s cover the symptomatic player behaviors you’ll see in the field. 

    Existing heuristics
    When a player lacks a mental model for a phenomena, their immediate instinct is to adapt an existing model. They look for past experiences and skills that fit the current situation and then act accordingly. Players can pick from their personal experiences or they may use forms of social proof to follow what others are doing.

    There is strong evidence that many of our default heuristics for dealing with randomness are instinctual and perhaps biological. As such, evolution selected for survival, not necessarily accuracy. This leads to a wide array of biases such as loss aversion or difficulty processing large odds.

    In general, reliance on existing models is a poor method of dealing skillfully with random behavior. It is better than a purely random reaction in a pinch but is not well adapted to the engineered random systems players face in modern designs. The player can’t know the properties of the random system beforehand and the wide range of different types of randomness mean that they will likely guess incorrectly.

    Sampling
    Perhaps the most confusing aspect of randomness it that it occurs as a result of an interaction loop. In a simple slot machine, you pull the handle once and get a single result. By its very nature, it is difficult or impossible to detect what that result might be.

    So the first skill players acquire is the ability to take multiple samples of the event. For very rare events, you may need to take large numbers of samples. For common, more predictable events, you may need to sample it less often.

    Sampling is a general skill that is useful for both complex, yet entirely deterministic systems and for systems with high amounts of pure randomness. Humans observe the vast universe through a tiny straw. Only by repeated and methodical exposure can we build up a more comprehensive image of what exists.

    Cost of sampling
    Sampling almost always has a cost. Here we see one of the more interesting economic decisions at the heart of random systems: Will the expense of sampling further result in enough improved understanding that I can then leverage in the future for outsize gains?

    Averages and Variability
    With large enough samples, most random systems become predictable. They tend towards an average with some variability around that average. Thus with enough sampling, the next skill that players learn is getting a feel for the ‘typical result’ and the likelihood of an ‘atypical’ result.

    Advanced players of Triple Town see luck as a very minor component of the game. As you plan out 30 or 40 moves into the future, you learn that there is a very good chance that you’ll get a bush or bear within your window of control. You don’t know the order, but there are tools for mitigating out of sequence drops. The learned mental map of average drop rates becomes a tool to be applied skillfully.

    Types of distributions
    Often the player sees a variety of different types of distribution. The normal curve, multi-modal or exponential distributions are most common. Advanced players get a sense of the distribution. What will outcome is most common? What outcome is least common?

    Payouts
    All actions in games have payouts. Sometimes they are explicit such as a pawn capturing a rook and removing it from the board. Sometimes they are implicit such as a gift to a player that may in the future be reciprocated.

    Through sampling, understanding averages, and understanding distributions, players gain a sense of the value of the payouts. In a sequence of player initiated causes and effects, how useful are the effects?

    Expert players weigh these benefits against the costs reaching that average outcome.

    New player mistakes due to model ignorance

    There are numerous and well documented mistakes that the naive player makes when dealing with systems of randomness. With training, many such players can overcome these. Some will not. Placing an inexperienced driver in the middle of a professional NASCAR race will likely end in physical harm. Even with training, a certain population will never become competitive drivers.

    Reliance on non-evidence based models
    Players use existing models without considering the evidence. For example, it is common to assume that because 1D6 results in an even distribution of values, 2D6 will also result in an even distribution.

    Not enough samples
    Players don’t sample enough instances of the game to understand the typical outcomes.

    Low quality sampling
    Players sample, but don’t actively look for patterns. Without consciously making observations and testing those observations against future results, critical signals are often ignored. Many players will perform actions, faintly register the results but never ask ‘why’.

    Poor cost / benefit analysis
    During the learning stages of a game, players typically over invest in learning activities, beyond what is strictly necessary to accomplish the desired result. This is seen as ‘play’ or ‘practice’ depending on how experimental the routine ends up being.

    However, it is common for new players to invest huge amount of resources in activities with very little future pay off. They engage in ‘play’ behavior (not a consciously forward looking act) and find themselves never recouping. They misjudge when hold them, when to fold them or for that matter, when to walk away.

    Balancing for skill in games of luck

    Like any game of mastery, we have concepts of balance and progression in games of luck. Typical balancing techniques work

    Dominant strategies
    Is there an average outcome that is preferable? This is tricky to ascertain since you can still have a balanced random system where a single sampled event yield a rare outcome. When new players see this, they will scream at the top of their lungs that something is overpowered. With a reasonable understanding of combinatorics, you can guarantee that such events are interesting outliers. You can also gather metrics over a large population of games and verify that the ‘game breaking outcomes’ are in fact rare circumstance.

    Is there any benefit to even having these outliers? I think so. They certainly add a strong emotional drama to the game that would otherwise be missing. Also players are kept on their toes and must plan for blackswan events as much as the average events. That’s an interesting decision.

    In Triple Town, the players that come back from a scenario with 5 ninja bears dominating their game end up being better players because of the experience. If that random outcome hadn’t occurred, they would never have been pushed to take their tactical skills to the next level.

    Does the game structure allow for multiple samples?
    A single hand of poker is deeply imbalanced since it is prone to highly variable random outcomes. However, during a poker night or tournament, players churn through dozens of hands. This allows players to take multiple samples and use their knowledge of the game’s random distributions to gain material advantages over weaker players. Thus, the right number of samples results in a more balanced game full of meaningful decisions.

    Progression considerations in games of luck

    You can use the following learning variables to create a progression system to help teach new players the subtleties of a random system. 

    Scaffolding
    Can new players learn foundational rules with a small number of samples? If you start players off with a random system that takes dozen or hundreds of sample to understand, they may quite before they accumulate enough experience. Instead, use system at are reasonably easy to figure out. In Triple Town, players get grass the vast majority of the time. This helps them learn how to build up more complex structures since they learn very quickly that there’s a good chance that the next object is going to be grass.

    Existing schema
    Is there a known random system you can mimic in order to tie into existing heuristics? For example, many games use a 6-sided die since that is a model of randomness that many players have been using since childhood.

    Use of random systems that reveal structure upon inspection
    One of my favorite techniques is to pull random outcomes from a fixed pool. Thus the expert players learn what they are going to get, but not in the order they are going to get it. This is the basis of all card games that disallow reshuffling.

    You’ve got two key variables you can tweak for progression escalation:

    1. When the pool is small, players tend to learn it quickly. By increasing the size of the pool, you require additional mastery.
    2. Randomness without replacement ends up being reasonably predictable when sampled across the size of the fixed pool. So if your sample count is higher than the pool size, players will learn the pool quickly. If the sample count is less than the pool size, they’ll learn it slowly (or never)

    Black hat techniques

    There are also cynical techniques that will result in players never learning the system. There are entire gambling journals dedicated to these methods since the number of human randomness hacks are quite large.

    • Obscuring average results through high variability and high sample requirements.
    • Use of artificial close calls so new players see patterns were there are none. There is a measurable sub-segment of players that process near misses as wins. These games prey on people who are essentially dyscadentic, or the random equivalent of dyslexic.
    • Use of social signals so players approach the game with a costly mindset.
    • Obfuscated odds combined with a high cost of playing.
    • Use of high odds that players don’t process well. At a certain point the brain says ‘many’ and doesn’t quite grasp that there is a good chance the universe may expire first.

    Conclusion

    A well rounded designer does not remove randomness from their games. The world is a random place and learning to deal rationally with randomness is a critical life skill. Instead, they embrace the fact that players can learn to understand and master the game’s random systems.

    It is your responsibility as the designer of random systems to facilitate masterful play. Put new players through a progression where you teach them the system’s average results, outliers and distributions. Give them tools for managing and mitigating randomness. Create expert game modes where players roll the dice enough to manipulate the big picture.

    When you use randomness as an opportunity for mastery over noise, I think you’ll find that games of luck become highly meaningful games of skill.

    take care
    Danc.

    References

    Psychology of near misses

    Gambling addiction as a learning disability

    Building Tight Game Systems of Cause and Effect

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    To play a game well, a player must master a mental model of cause and effect.  You learn that pressing a specific button moves you forward.  You figure out that a sequence of controller moves lets you dodge a fired rocket.  You observe a slight pause before an enemy attack and theorize that you could fire off a headshot at that exact moment.  At each stage of learning, you create a hypothesis, test it via your actions and refine your mental models of the whirring black box at the heart of the game.

    This escalating refinement and mastery of new mental models and tools is essential to what makes many a game enjoyable. Such mastery obviously depends on the player.  Yet it also is dependent on the designer and the systems they build.  You can accidentally create a broken black box. 

    Not all systems are readily amenable to the intuitive formation of models of cause and effect. As a game designer, it is your job to create systems that are intriguing to master without being completely baffling. If the system is too predictable, it becomes boring. If it is not predictable at all we assume that the system is either random or spiritual in nature. Both of these are failure conditions if you are attempting to encourage mastery.

    Tight and Loose systems

    I am a mechanic who fixes broken black boxes. One importance concept that has served me well is to think of the relationship between systems and the feedback the game uses to describe interactions with the systems as either ‘tight’ or ‘loose’. A tight system has clearly defined cause and effect. A loose system make is more difficult to distinguish cause and effect relationships.

    There is no correct ‘tightness’ of a loop. However there are clear methods of increasing either the tightness or the looseness.

    Techniques for adjusting tightness

    For your reading pleasure, I’ve put together a list of tools that I use to tweak a system’s tightness.  Not all are applicable to any given system but all of them should be part of an expert designer’s toolkit.  Some of the tools are worthy of dedicated books so I apologize up front for any obvious shallowness.  For example, probability has so many subtle flavors that some designers devote their lives to studying how it impacts a player’s ability to predict outcomes.  At best this is an overview.

    To tighten a system, I’m making the cause and effect more obvious.  To loosen a system, I’m making the connection between cause and effect less obvious.

    Strength of Feedback

    Peggle



    Tighter: Multiple channels of aligned feedback such as color, animation, sound, and touch that reinforce one another.  The classic example is Peggle which uses particles, rainbows, Ode to Joy and time dilation to let you know that yes, the match is over and glorious points are being scored.

    • Am I using all the potential channels I need to make an impact?
    • Is the feedback sequenced correctly so that player can read it clearly?
    • Does the feedback leverage an existing mental schema so that becomes more impactful?

    Looser: One channel of feedback that is weakly evident.  In multiplayer FPS games often the only sense that you have that another player is near comes from the faint patter of their footsteps.   Expert players gain immense satisfaction from being able to predict the location of their opponent by combining knowledge of the levels with tiny hints of where they might be. 

    • Does the feedback have nuance that is not readily understandable upon casual inspection?
    • Can the feedback be combined with other non-obvious information to give a clear picture to an expert user?

    Noisiness

    Space Giraffe



    Tighter: A clear signal of effect that is related to the cause.

    • What is the most important piece of information the player needs right now?
    • Have I removed extraneous elements that distract the player’s attention?
    • Is my feedback at the center of the player’s attention? 

    Looser: A multiplicity of conflicting, attention sapping signals, which are not related to cause. One of the critical skills in Jeff Minter’s Space Giraffe is learning to see through the visual noise of the psychedelic backgrounds.

    • Are there ambient elements I can add that distract, but don’t annoy?
    • Can noise create a perceptual puzzle for the player?

    Sensory type

    Assassin’s Creed 3:  Nice use of contrast and perspective



    Tighter: Visually or tactile feedback is often more clearly perceived.  Consider the many billions of dollars spent on improving visual feedback each year so that we can demonstrate the visceral impact of a players bullet on simulated flesh with ever greater fidelity.  Tight visual feedback is highly functional; it communicates the effect to the player in an elegant efficient fashion.  It is not just about making pretty pictures. In a recent update of Triple Town, we changed the color scheme so that the background was the same general value as the foreground objects.  The result was attractive, but players were pissed because the icons weren’t nearly as visible as before.

    • Am I using good visual design such as color, motion, contrast, line, white space, shadow, volume, perspective so that my visuals read clearly?
    • Did I make something pretty when I needed something functional?
    • What feedback is functional and what is evocative or aesthetic?
    • Am I over investing in visual feedback?

    Looser: Auditory and smell are less clearly perceived.  Not as much has been done here, but due to the looseness that come such systems it would seem that there are potential systems of mastery.  It is perhaps ironic that most music games, a topic typically associated with auditory mastery, can be played with the sound turned off. 

    Tapping Existing Mental Models

    Plants vs Zombies



    Tighter: Closely map the theme, feedback and system to existing mental models.  Due to decades of exposure to pop culture, players know how zombies move and that they should be avoided.  One means of quickly communicating the dozens of variables in a particular slow moving group of monsters is to label them ‘zombies’.

    • What is the cartoon model that players have in their heads (vs the ‘realistic model of how the real world works)?
    • Does my theme support my mechanics?
    • Does my theme inspire useful variations on my core mechanics?
    • Am I engaging in the cardinal sin of watering down my mechanics to fit the theme?

    Looser: Step away from existing models and introduce the player to new systems that they’ve never experienced.  Consider the metaphors involved in Tetris.  Falling elements are something our brain can process as reasonably familiar.  Tetriminos that you fit into lines that disappear to earn points while Russian music plays?  That doesn’t fit any known metaphor that I know, yet it results in a great game.

    • At what point do I no longer need a gateway schema and the game can stand on its own internal consistency?
    • Are there opportunities for surrealism or intentional disorientation?
    • Can we step away from cliches to synthesis fresh experiences?

    Discreteness

    Advance Wars:  Limited units and small numbers. 



    Tighter: Discrete states or low value numbers. Binary is the tightest. For example, recently we were playing with units moving a various speeds.  By making them move a 1, 2, and 4 tiles/sec, it suddenly became very obvious to the player how each unit type was distinct.  This is one of my favorite techniques for getting unruly systems under control. 

    • What is the minimum number of values that I need to create meaningful choices?
    • Can player clearly distinguish between the effect of each increment in value?
    • What would happen if I had to reduce this variable to 3 discrete values?

    Looser: Analogue values or very high value numbers. For example, in Angry Birds, you can give your bird a wide range of angles and velocities.  This makes the results surprisingly uncertain.  Think of how predictable (and boring) the game would be if you could only pick 2 distinct angles and velocities. 

    • Do I have enough range that players can play creatively?
    • Do my values add interesting uncertainty to choices?

    Pacing

    Diablo Loot Pacing



    Tighter: Short time lapses between cause and effect.  When creating mouse over boxes like you find in Diablo, a common mistake is to add a delay between when the mouse is over the inventory item and when the hover dialog appears. If the delay is too short, the hover dialog pops up when the player doesn’t expect it.  If the delay is too long, the dialog feels laggy and non-responsive. (In my experience, 200ms seems ideal.  That’s right inside the perception gap where you’ve decided to do something, but your conscious mind hasn’t quite caught up) 

    • Where does the game play lag?
    • What happens if I speed timing up? 
    • What happens if slow timing down?
    • What systems allow me to vary timing in an indirect fashion?
    • Am I adjusting pacing using manual content arcs when I could instead use with algorithmic loops?

    Looser: Long time lapses between cause and effect. Too long and the player misses that there is an effect at all. Imagine an RPG where you have a switch and a timer.  If you hit the switch, a door opens 60 seconds later.  Surprisingly few people will figure out that the door is linked to the switch.  On the other hand, early investment in industry in Alpha Centauri resulted in alien attacks deep in the end game.  This created a richer system of interesting trade off for players to manipulate over a long time span. 

    • What are the longer loops in the game?
    • Are there long burning effects that cause players to reconsider their models for long term play loops? 

    Linearity

    Castlevania Medusa movement (via Kotaku) 



    Tighter: Linearly increasing variables are more predictable. Consider the general friendliness of throwing a sword in a straight line in Zelda versus catching an enemy with an arcing boomerang while moving.

    • What happen if I simplify the model and make the reaction linear?
    • How can I remove non-linear systems from early gameplay?

    Looser: Non-linearly increasing variables, less so. The Medusa heads in Castlevania pose a surprisingly difficult challenge to many players because tracking them breaks the typical expectation linear movement.  Even something as commonplace as gravity throws most people off their game.  After all, it took thousands of years before we figured out how to accurately land an artillery shell. 

    • What systems are exponential in nature?
    • How do I constrain my non-linear systems so they are predictable?
    • How do I create interestingly chaotic behavior via feedback loops?

    Indirection

    SimEarth



    Tighter: Primary effects where the cause is directly related to the effect. In Zelda again, the primary attack is highly direct. You press a button, the sword swings out and a nearby enemy is hit. 

    • What systems can I remove to make the results of an action more obvious?
    • Is my cognitive load high enough?

    Looser: Secondary effects where the cause triggers a secondary (or tertiary) system that in turn triggers an effect. Simulations and AI’s are notorious for rapidly become indecipherable due to numerous levels of indirection.   In a game of SimEarth, it was often possible to noodle with variables and have little idea what was actually happening.  However, the immense indirection yields systems that people can play with for decades. 

    • How can simple system interact to create useful indirect effects? 
    • How can I layer useful indirect effects to create wide expressive opportunities for the player?

    Hidden information 



    Mastermind



    Tighter: Visible sequences that are readily apparent.  For example, in Triple Town we signal that a current position is a match.  The game isn’t about matching patterns so instead the design goal is to make the available movement opportunities as obvious as possible. 

    • Is there something hidden that shouldn’t be?
    • Is there something visible that doesn’t matter?

    Looser: Hidden information or off screen information. A game like Mastermind is entirely about a hidden code that must be carefully deciphered via indirect clues.   Board games that are converted into computer games often accidentally hide information.  In a board game, the systems are impossible to hide because they are manually executed by the players.  However, in computers the rules are often simulated in the background, turning a previously comprehensible system into mysterious gibberish. 

    • Would hiding information fully or partially make mastery more challenging?

    Probability



    Tighter: Deterministic where the same effect always follows a specific cause. In a game like chess, the result of a move is always the same; a knight moves in an L and will capture the piece in lands upon. You can imagine a variant where instead you role a die to determine the winner. You can make that tighter again by constraining the probability so that certain characters roll larger dice than others. The 1d20 Pawn of Doom is a grand horror.

    • How do I make the outcome highly deterministic?
    • Is this direct action still interesting if repeated hundreds of times?

    Looser: Probabilistic so that sometimes one outcome occurs but occasionally a different one happens. In one prototype I worked on there was both a long time scale between the action and the results as well as a heavily weighted but still semi-random outcome. Players were convinced that the game was completely random and had zero logic. If you pacing is fast enough and your feedback strong enough, you might be able to treat this as a slot machine.

    • Do I need a simple method of simulating a complex system?
    • Do I need a means of adding interesting pacing to the game?
    • Does the player perceive that they have the situation under controls despite the randomness?

    Processing Complexity

    SpaceChem



    Tighter: System requires simulating few steps to predict an outcome.  In a vertically scrolling shooter, you see the bullet coming towards you.  It doesn’t take a lot of thought to figure out that if you stay in that location you are going to be hit.

    • How much can the player process in the time allotted?
    • Are players getting mentally fatigued playing the game?

    Looser: System requires simulating multiple steps to predict an outcome.  On the other hand, in Triple Town, good players need to think dozens of moves ahead.  Thinking through all the various machinations necessary to get the result you want adds a serious cognitive load to the player.  A single mistake in the player’s calculations yields unexpected results.

    • Do players feel smart?
    • Can players plan multiple moves ahead?
    • Can players debug why their plans didn’t work?

    Option Complexity

    Steel Battalion



    Tighter: Fewer options are available to consider. In a recent upgrade system I was building I give players 3 choices for their upgrades.  I could have given them a menu of 60 upgrades, but that would be rather overwhelming.  By focusing the user on a few important choices, I give them the mental space to think about each and pick the one with the biggest impact.

    • Can I reduce the options?  
    • If I had to remove one choice, what would it be? Would the game be better?
    • Which options are the most meaningful?

    Looser: A large number of options must be considered.  In a game of Go there are often dozens of potential moves and hundreds of secondary moves.  This options complexity is a large part of why the game has been played for thousands of years. 

    • How do current options yield an exploding horizon of future options?
    • How do I re-balance outcomes to make more options useful?

    Social Complexity

    Death of Lord British in Ultima Online



    Tighter: Another human broadly signals intent, capabilities and internal mental state.  In an MMO, a player dresses as a high level healer and stands in a spot where adhoc groups meet up. There’s a good chance you know what they’ll do if you ask them to go adventuring together.  Or in a managed trade window, you know exactly what you are getting when he puts up a potion for your sword.  There is little ambiguity.

    • Can I make a character automatically signal future intent via their current actions?
    • Do the options collapse to a reasonable number so that I can predict what the other player might do if they are acting rationally?
    • Do I know enough about the goals and resources of the other player?
    • Have a spent enough time with the other player to model their internal state?
    • Are there predictable methods of interacting between players?

    Looser: Another human disguises, distorts or mutes intent, capabilities and their mental state.  

    • Can people communicate?
    • Can people lie and what is the impact of that?
    • Can people harm others? Can they help? Are there repercussions?
    • To what degree is my choice dependent on another player’s choice?
    • What are group dynamics that influence behavior?

    Time Pressure

    WarioWare



    Tighter: Requires simulating the model at the player’s preferred pace.  This is related to processing and option complexity since players can only execute their models at a given pace.  Players are more likely to make causal connections if the time pressure is greatly reduced.   For example, the game NetHack has complexly interwoven systems that require real detective work to decipher.  In order to increase the likelihood that players will make the connection, the game is set up as a turn-based game where players may take as much time as they want between turns.  You’ll see that as the situation becomes more complex, even good players will slow down their play substantially so they can understand all the ramifications.

    • How much time does the player need to understand what is happening?
    • Can I let the player choose their pacing or do I need to force a universal timing?
    • What are the multiplayer ramifications?

    Looser: Requires simulating the model quickly.  In a game of WarioWare, there isn’t really much complexity involved in each individual puzzle.  However, we can dramatically ramp up the cognitive load and increase outcome uncertainty by setting a very short timer. 

    • Would time pressure push the player’s cognitive load into a pleasurable flow zone?
    • Is the player feeling analysis paralysis?
    • Is the player feeling wildly out of control?

    Applying the tightening techniques

    When I run into the common situation where players don’t understand the system, I often use the tightening techniques to make the system’s cause and effect relationship more crisply defined for the player.  In almost all cases, my changes are in response to observations stemming from playing a prototype myself or from watching someone else play a prototype.   I find them to be most useful as tuning techniques and less reliable for making grand plans in the absence of functional code.

    Gameplay is composed of loops and these loops have distinct stages (Actions, Rules, Feedback, Updating of the player’s mental model).  Depending on where in the loop the observed issue might be, I use different techniques to tweak it.

    Action Problems

    • Option complexity
    • Pacing

    Rules Problems

    • Processing complexity
    • Probability
    • Indirection
    • Linearity

    Feedback Problems:  Feedback failures are the most common error I find when dealing when implementing known systems. Most new designer make feedback errors.  Intermediate designs often focus on feedback to the exclusion of other problem areas. 

    • Strength of feedback
    • Noisiness
    • Sensory Type
    • Hidden information
    • Discreteness

    Modeling Problems:  

    • Time pressure
    • Tapping existing mental models

    Tightness vs the stage of player mastery

    Skill loops build upon one another.  The jumping in Mario evolves into advanced platform navigating skills. What I find is that often the lowest levels of skill loops need to be the tightest. These are the systems you need to be most obvious in the first seconds of play…they are the gateway into the rest of the game, so to speak.  Keep the number of options low, tap into existing mental models and make the cause and effect as crisp and obvious as possible.  Then once the player is comfortable manipulating the basic system, you can introduce looser connections that take more effort to master.

    The player’s perception of tightness and looseness changes over time. There’s a mental chunking operation that occurs as we master skills. Sequences that were once confusing and complex get reduced down to easily repeated and manipulated patterns. So the higher level skills that are made of multiple chunked precursor skills end up feeling very clear and obvious. You’ll often find controls that a new player describes as twitchy or sloppy are described by an expert player as extremely precise and tight. Mastery can turn loose systems into tight tools.

    Conclusion

    New designers often treat the systems at the heart of their games as inviolate features of nature.  The properties of a sniper rifle, the combo system in Street Fighter or the energy system in a farming game are treated as mathematical facts.  You can tweak some values, but the basic system has always existed and will always exist.  Yet the truth is that these systems were invented and then adopted because they had useful properties.  They are easy to pickup, yet provide sufficient depth for long term mastery.  They are designed artifacts.

    We can design new systems that hit the sweet spot between mysterious and boring.  By looking at your new games through the lenses listed above (and likely some others that I’m forgetting) you can iteratively tune the systems, models and skills at the heart of your game to be more or less understandable. By following a methodical process of invention, you can take a weak game and turn it into a great game that dances hand-in-hand with player capabilities.

    take care,
    Danc.

    Goodbye Realm of the Mad God

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    It is hard to let go of something you’ve worked on for such a long time, but such is life.

    After a rather successful launch of Realm of the Mad God on Steam and Kongregate, our partners at Wild Shadow Studios decided that the best course of action was to sell the game to a larger operator, and we agreed to sell our stake alongside them.

    Kabam will be operating the game from here on out and Willem Rosenthal, who has been designing the new dungeons and loot in RotMG for several months now, will stay on board to guide the project going forward.


    Before

    RotMG will always be a special game for David and I. Alex Carobus is one of the most talented programmers we’ve ever had the pleasure to work with, and the game itself pushed the boundaries of what an MMO could be. When we started out, RotMG had the bare bones of a multiplayer bullet hell shooter. The foundations of the game were fascinating: coop only, permadeath, procedurally generated worlds, and retro 8-bit art. It had such promise, but it was on track to end up as just another interesting game jam prototype.

    After

    Over the course of 2+ years, we worked with Alex to turn RotMG into a full-fledged MMO with more meaningful cooperation, a trading system, guilds, a compelling advancement system and community full of passionate players. We measured fun, retention and monetization and steadily increased all of them. At this point, millions of people have played a game that at first glance appears to be a niche hobby project.

    I’m particularly proud of how monetization turned out in RotMG. The game is completely free-to-play, but it is not a pay-to-win game. Skill matters (much more so than in many other games) and the items we offer for sale for hard currency never imbalance the game. In fact, some purchases (such as dungeon keys) are highly social purchases that can benefit free players as much as they do the original buyer.

    If you are interested in learning more about how RotMG evolved, David gave a lecture at GDC that you can watch for free at: https://www.gdcvault.com/play/1015659/Realm-of-the-Counter-Intuitive

    We wish the best of luck to Kabam as it proceeds to make the most of a very special game. And to the RotMG community: we want you to know how grateful we are for the years of support and encouragement you gave us. We appreciate how hard you pushed us to be better at our craft, and how warmly and generously you treated us when we weren’t screwing things up. 😉

    We wish we could have continued to grow RotMG alongside you, but we know we’re leaving you in good hands. In the meantime, we’re going to keep cranking away on a couple of new online games that we’ve been quietly developing for the past year or so. We can’t wait to share ’em with you!

    -‘Chedd’ and ‘SpryFox’ signing off from Realm of the Mad God.

    Looking to hire unicorn programmer for Spry Fox

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    Hi everyone — my company Spry Fox is looking to hire a senior-level engineer/developer. If you are not this person but have worked with someone you love and trust, let me know!

    Job title
    We don’t really do titles here. Feel free to call yourself something amusing and/or impressive.

    What we’re looking for…

    • Senior level engineer (five to ten years of work experience, minimum.)
    • Can program both the front end and back end of an original online game – by themselves or as half a team of two
    • Has worked on multiple shipped games in the past
    • Very comfortable with frequent, rapid iteration (daily to weekly)
    • Excited about original, free to play games
    • Familiarity with Flash and Unity is a major plus but not a requirement. It’s actually more important for whomever we hire to be flexible and not wedded to any given language, as we frequently find ourselves adjusting our tech to meet specific circumstances.
    • You must be a self-starter who can work effectively without being closely managed or prodded. This is a company for entrepreneurs, not worker bees.
    • Reliability and honesty are essential.  We love working with nice people. 
    • Location is not an issue; we all work remotely. But if you live in Seattle or the Bay Area, you’ll get to have lunch with us pretty regularly. 🙂

    About us
    Spry Fox is a successful developer of online games that have collectively reached over 30m people. Our titles include Steambirds, Triple Town, Realm of the Mad God and Panda Poet. We are passionate about two things: making great original games and bringing happiness to the world.  It is kind of a sweet gig.

    Send unicorn intros to jobs@spryfox.com

    take care,
    Danc and David

    Prototyping challenge: Make a web-based 3D modeling toy

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    I’m rather obsessed with user generated content, particularly art tools.  Recently, I had a wonderful experience with Realm of the Mad God.  Alex Carobus added in a simple pixel editor that allowed anyone to create sprites that might be used in the game.  Very rapidly, players created thousands of truly delightful pieces of art.

    Inspired by this, I set a design challenge for myself.

    • 3D in a browsers. What is an easy-to-use 3D modeling tool that lives in the browser?
    • Unique style:  I want the output to be instantly recognizable as being created in this toy.  That means radically constraining the tools.  Instead, I was particularly inspired by the extruded 3D style of Land-a Panda. 
    • Minimalism: Are there any ways of simplifying 3D modeling? What is the pixel editor equivalent of a 3D modeling tool?
    • Professional results:  Can we build something where you look at the results and think “Wow, that is really nice.”  Think of it as the Instagram effect. I’m particularly targeting casual games, but I suspect if that is nailed, people will find all sorts of uses for the toy. 

    What I’m avoiding:

    • No copying an existing tool.  Sure there are well established paths for 3D modeling or vector editing, but that is too easy.  Lets go back to the design roots of these complex monstrosities and build up something elegant and different. 
    • No voxels: I don’t want to use voxels.  Minecraft already does this so let’s push in a wacky new direction. 

    The closest I’ve found that fits these constraints is the amazing TinkerCad, which is a simplified solid modeling tool.  It is very nice, but only really ticks the first checkbox.

    Here’s what I’ve come up with.  If anyone find the idea curious enough and wants to build a prototype over a few weekends, I’m happy to collaborate.  This wacky, broken and experimental.  But what is the fun in sharing only perfect ideas?

    Model Toy

    Model Toy: An easy to use drawing and modeling tool for making stylized objects

    Model Toy is a ‘back to the roots’ effort that asks if you can make a modeling tool by only manipulating vertices on simple curves. The tool is made of several basic elements

    • Grid-based drawing plane: All drawing occurs on a plane.  This can feel more like a 2D tools than a 3D tool. 
    • Shapes:  The key primitive is a unique extruded vector shape defined by 4 points on a plane. 99% of the time, the artist is moving around vertices. 
    • Shape Palette:  A list of available primitive shapes. 
    • Shape Properties:  List of the current shape’s color, extrusion, etc. 

    Shapes

    The heart of the tool are these odd 2D path-based primitives that Pete Blois and I have been experimenting with.  You can play with an example of it here: http://apps.blois.us/Drawing

    • The shape is a 2D vector composed of 3 to 4 vertices. 
    • Each vertex is either a rounded corner, half rounded or straight corner. 
    • Vertices only snap point on the grid. 
    • The shape can be extruded and beveled. 

    These actually came out of a lot of different experiments and I realized something really obvious.

    • Engineers tend to make art primitives that have lots of knobs and widgets…they are highly parametric objects with a complex interface.  
    • Yet, many artists don’t necessarily think in terms of complex objects.  Instead, they use simple  things that are easily manipulated and then repeat the same tweaking action thousands of times until the composite result is interesting.  There are no explicit ‘rotation’ or ‘scale’ operation when painting.  Yet the results are still impressive.  
    • So this design preferences ‘tweaking thousands of times’ over ‘a complex object where you set variables once’.

    Basic move, scale and translate operations

    One interesting aspect of these primitives is that they don’t have an explicit scale, rotation or translation matrix for the user to manipulate.  Instead, all those operations are performed by moving vertices around. That’s all you really do in this tool…move vertices about.

    • Move shape: Click on a shape to select it. Drag on the body to move it around.  This moves all vertices together.  Note that all vertices always snap to the grid. 
    • Deformation: You can deform a primitive by moving its vertices in a 3D plane. Drag on the square surrounded a vertex to move it to a new grid point. 
    • Rotate: To rotate, move vertices one by one until the new shape looks rotated.  This is not true rotation since the snapping to the grid will not allow true rotation.   However, the result will look rotated and that is all that matters in art.  This works surprisingly well. 

    There are big limits on the shapes

    We could allow thousand of these objects on the screen.  But instead I’m inspired by the elegance of low resolution pixel art where beauty comes from working within limitations. 
    • All vertices are constrained to a 16×16 square grid.  This allows for easy selection of vertices and accurate adjoining of shapes. 
    • There are only 32 shapes in any one model.  This encourages the artist to create elegant compositions. 
    • Each shape is one of 16 colors in a fixed palette. 

    Shape Toolbar

    There are four basic shapes you can create with this method.  Click one of the primitive button on the toolbar and the shape is added to the scene.

    • Circle: 4 rounded vertices
    • Rectangle: 4 straight vertices
    • Half Circle: 3 vertices: 1 curved and 2 half curve / half straight
    • Triangle: 3 straight vertices 
    Example shapes that can be created by moving vertices about on grid

      One system for defining hidden control handles

      The follow is one method of getting the desired curves using bezier handles. Straight corners are a trivia case, but round and half round need to be tweaked to allow for aesthetically pleasing circular geometries.

      • For round corners, handles are defined only by adjacent vertices (vertex 2 and 3 are adjacent to 1)
      • Handles are parallel to the line segment ‘a’
      • Length of handle is proportionate to segment ‘a’  (Note that the .27 in the diagram is a value that results in 4 round corners arranged in a square yields a perfect circle.  There is likely a mathematical means of deriving this as well, but that is beyond me. 🙂 

      • For half round, half corner points, calculating the normal based off the points adjacent to vertex 1 (in the picture above) results in a bowed out shape.
      • Instead, mirror point 2 across the line segment A. This creates a new ‘Fake A’ that goes in the correct direction.
      • The new curve handle for point 1 is now parallel and proportionate to ‘Fake A’

      What this toy lacks

      • 2D scale and Rotation: With such simple primitives that are easily rearranged, we don’t need these operations.
      • Full color picker: You can’t define arbitrary colors
      • Layers and grouping: With 32 shapes, a shape list is the layer list
      • Lines: There is only the shape color. Later on, we can have effects that apply to the object as a whole.
      • Empty shapes: Shapes always have a fill color.

      Extending to 3D

      To the left is the side view palette.  This is a bit like a layer palette in photoshop, but it also lets you control Z-depth.  This is a bit geeky and isn’t my favorite part of the design, but worth trying.

      • Dragging on the body of the shape moves it left or right.  This is changing the depth of the object. 
      • Dragging on the left side of the shape extrude backwards. This snaps to the grid. 
      • Dragging on the right side extrudes forwards. This snaps to the grid. 
      • The profile of the shape shows its bevel. 

      Other shape Properties

      You can select a shape and edit its properties.

      • Color: Click a shape, click a color and the shape becomes that color.
      • Bevel:  Select the bevel for the object.  No bevel, rounded corners, dome, flat bevel
      • Extrusion:  Select how far you want the object to be extruded. 

      Open questions

      • Is this expressive enough?
      • Is there a better method of expressing the 3D extrusion?
      • How might it be simplified even further?

      Near Future

      The first part of the challenge is to get a basic editor up and running. For these new drawing tools you usually need to build it and then iterate on it 5 to 10 times so that the feel of the program is solid.

      Web-based editing, saving and viewing
      The model is editable in a browser window. You can save to a database and load. You can share the model with another user and they can make a copy of it and edit their own version.

      3D view
      Once you have a 3D view you can rotate the drawing plane to see the object from from various angles.  Some experiments to try:

      • The plane always snaps back to the frontal view when you release. 
      • Alternatively if you rotate the object 90 degrees, it snap to the side view and swap the side view for the front view in the other palette.  

      Export options

      • 3D model: Exports a static 3D model for import into something like Maya, 3DS or Unity. 
      • Bitmap: Export as a series of X (64?) images rotated around a center point. Includes Alpha

      Far future

      Shader sets
      Users can load in different shader sets as alternates to the base 16 colors. For example, there is a wood set that has different types and tones of wood. Or there is a metal set that has pitted bronze, steel and copper.

      Post processing and Lighting Presets
      You can apply a variety of preset post processing filters much like Instagram. Honestly this is where the magic occurs. The idea is that these are incredibly high quality professional filters that give your simple model a distinct style.

      • Outline: Add an outline to the image so that it looks like Land-a-panda. 
      • Pop art: Dot shading.
      • Sepia: Grainy, old timey image

      States
      Define states for each model with each state have a different configuration of the 32 shapes.  For example, you could have a walk state and an attack state for a character.

      Now if you bundle these states into templates, you could provide users with a ‘character template’ that they can fill out to their heart’s content to create a thousand unique characters that all ‘work’ the same.

      Animations between states
      Allow for tweening animations between states.  Add ease in and ease out for basic timing.

      Conclusion

      This odd art toy is not a perfect tool.  Having made art for a few decades now, I’m not sure there is such at thing.  Instead it is series of constraints.  The theory is that these constraints will yield interesting art when placed in the hands of motivated artists.  We’ve seen this happen before.  Vector art is a style that emerged from the limits and strengths of printing technology.  Pixel art emerged from the constraints of early computer displays.  There is an exuberant creativity within carefully chosen walls. Is it possible to artificially foster that?

      Mostly I wanted to share these ideas.   For the folks that love an oddball project, this might be fun to play around with for a weekend or two.  It is certainly a way to learn about curves, 3D extrusions (and the exquisite pain of iterating on an artist-centric UI.)  I’d be delighted to give feedback and try out prototypes if any emerge.

      Long term if the basics works out, I could see making an entire professionally polished game in this art style with every single character, wall, door and tree built out of these shapes. This is the real test. Once you get artist trying their hardest to build real things with a new art tool, a feedback loop is born.  The artist asks for tiny yet critical features you could have never imagined.  After a few dozen iterations, the simple odd tool begins enabling amazing artists to create a certain kind of masterpiece.

      take care,
      Danc.

      Prototypes!

      In order to keep all the learning going on in one spot, here are the prototypes that folks have made so far and feedback to each:

      Pete Blois’s Model Toy – Iteration 3
      http://apps.blois.us/Drawing

      This was the first prototype Pete and I iterated on and got the basic primitives working.

      Jeiel Aranal’s Model Toy – Iteration 1
      https://twitter.com/chemikhazi/modelingtoy/

      This one was done in Unity and has manual control handles and some extrusion. Thoughts here:

      • Drag to move shape: The ability to click on a shape and drag it on the plane will make the tool much easier to use. (You can put rotate the view on right press or by dragging on the empty canvas.)
      • Auto-control handles: One of the neat things about the little 4 point vector objects is that the control handles are automated and not actually visible to the user. The intent is that every time you move a vertex, you look at the adjacent vertices and then calculate the length and orientation of the handles. This really simplifies the use of the tool since many users find manual control handles fiddly. (Though you did a good job putting them in!)
      • Hit region on handles: In the current build, the hit region is the circular vertex. If you use the rectangular region behind the vertex, it will be much easier to grab the vertex.
      • Mouse over: Outlining / highlighting the object on mouse over and showing the vertices makes it much clearer what you are about to manipulate.
      • Ctrl or Alt drag to duplicate: This is a classic short cut that makes it much easier to make complex objects.  Works when combined with ‘Drag to move shape’. 
      • Slightly tilted drawing plane: A more complex tweak is to make the drawing plane tilted so that you are always drawing in 3D space. Since everything is still on a grid, it should be possible to still treat it as primarily a 2D drawing surface. This does require that the drawing plane be aligned with the face of each selected object.

      Mikko Mononen’s Model Toy – Iteration 1
      https://www.tinkercad.com/sketch/curve/

      • A lovely testbed for the 2D shapes.  It is clear that there is something off with the control handle behavior. 
      • Maybe adjust the control handles independently since currently they are completely symmetrical. Perhaps bisecting A in some manner may give a better value for each handle
      • The whole thing starts feeling much better if you can drag directly on the shapes themselves to move them around the 2D drawing plane.
      • Same thought as above on the tilted drawing plane. 

      Loops and Arcs

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      Here are two tools I’ve been using lately to better understand the functionality of my game designs.  The first is the loop, a structure that should be very familiar to those who have looked into skill atoms.  The second is the arc.

      Loops

      The ‘game’ aspect of this beast we call a computer game always involves ‘loops’.

      • The player starts with a mental model that prompts them to…
      • Apply an action to…
      • The game system and in return…
      • Receives feedback that…
      • Updates their mental model and starts the loop all over again.  Or kicks off a new loop. 

      These loops are fractal and occur at multiple levels and frequencies throughout a game. They are almost always exercised multiple times, either within a game or by playing the game multiple times.

      Nested, dependent loops yields complex feedback loops and unexpected dynamics.  Loops tend to deliver value through the act of being exercised.  Thus they are well suited for mastery tasks that involve trial and error or repeated exposure. The goal of both loops and arcs is to update the player’s mental model, however loops tend to rely on a balance of the following:

      • Interrelated actions that trigger multiple loops in order to bring about specific system dynamics.
      • Systems of crisply defined cause and effect that yield self contained systems of meaning.
      • Functional feedback that helps players understand causation. 

      Loops are very good at building ‘wisdom’, a holistic understanding of a complex system.  The player ends up with a mental model that contains a thousand branches, successes, failures and nuances that lets them approach new situations with confidence.

      Arcs

      ‘Arcs’ have similar elements to a loop, but are not built for repeated usage. The player still starts with a mental model, they apply an action to a system and receive feedback. This arc of interaction could be reading a book or watching a movie. However, the mental model that is updated rarely results in the player returning to the same interaction. The movie is watched. The book consumed. An arc is a broken loop you exit immediately.

      Arcs are well suited for delivering a payload of pre-processed information.  You’ll typically find many arcs have the following footprint:

      • Simple independent actions such as turning a page or watching a movie
      • Simple systems that rely heavily on complex mental models to have meaning.  Text on a page is a good example. 
      • Complex evocative feedback that links together existing mental models in some unique, interesting or useful manner.  For arcs, the feedback is 99% of the payload and the actions and systems are simply a means to an end.  Once this payload is fully delivered, the value of repeated exposure to the arc drops substantially. 

      Arcs are highly efficient at communicating ‘success stories’, a singular path through a system that someone else previously explored. The best teach a lesson, either informative, positive or negative. This is a brilliant learning shortcut but the acquired knowledge is often quite different and less robust in the face of change than ‘wisdom’. With a slight shift in context, the learning becomes no longer directly applicable. It is not an accident that we make the distinction between ‘book learning’ and ‘life experience’.

      One of the common issues with arcs is that people burn out on them rapidly, rarely desiring to experience them more than once. It is possible to give arcs a bit more staying power by stringing them together serially in a sequence of arcs. This is a pretty proven technique and is at the base of the majority of commercial attempts to give content arcs longer retention.  Businesses that rely on a constant sequence of arcs to bring in ongoing revenue often find themselves running along the content treadmill.  If you stop producing content, the business fails.

      Any loop can be superficially described as a series of arcs with one arc for each pass you make through the loop. This is an expanded loop. This is useful for recording a particular play-through, however it tells you little about the possibility space described by the loops.  Where loops often describe a statistical spectrum of outcomes, the arc notation describes only a single sample.

      Mixing Loops and Arcs

      Since both loops and arcs can be easily nested and connected to one another, in practice you end up with chemistry-like mixtures of the two that can get a bit messy to tease apart.  The simplest method of analysis is to ask “What repeats and what does not?”

      Narrative games are the most common example of mixing loops and arcs.  A simple combination might involve layering a segment where the player is engaged with loops with a segments of arcs.  This is your typical cutscene-gameplay-cutescene sandwich.

      However, the analysis can get far more detailed.  For example:

      • Parallel Arcs: You can treat the emotional payload of song as an arc that plays in parallel to the looping gameplay.
      • Levels:  The spatial arc of navigating a level provides context for exploring variations on a central gameplay loop. The ‘Golden Path’ in a single player level is really just another name for an arc. 
      • Micro Parallel Arcs:  A game like Half Life combines both levels and parallel arcs to deliver snippets of evocative stimuli as you progress through the level. 

      These structures also exist in traditional media. For example, if you look at a traditionally arc-based form such as a book, you find an odd outlier in the form of the Bible.  At one level of analysis it can be seen as a story arc that you read through and finish.  However, it is embedded in a much larger set of loops we casually refer to as a religion. The game-like loops include everything from worship rituals to the mining of the Bible in order to synthesize weekly sermons.  The arc is a central rule book for a larger game consisting primarily of loops.

      In the past I’ve discussed criticism as a game that attempts to revisit an arc repeatedly and embellish it with additional meaning.  The game is to generate essays superficially based on some piece of existing art.  In turn, other players generate additional essays based off the first essays.  This acts as both a referee mechanism and judge.  Score is accumulated via reference counts and by rising through an organization hierarchy.  It is a deliciously political game of wit that is both impenetrable to outsiders and nearly independent of the actual source arcs.  Here creating an arc becomes a move in the larger game. Intriguingly, tabletop roleplaying games use a similar core structure though the high level rewards differ.

      Even in these complex cases, understanding which behavior is a loop and which is an arc helps tease apart the systemic behaviors. Of the two, loops are rarely discussed in any logical fashion.  People note the arcs and comment on them at length while being quite blind to the loops driving the outcomes. Both criticism and religions are lovely examples of how loop analysis can provide a practical description of the game’s ruleset and magic circle even when the actual players are only vaguely aware of their constraints.

      The growth of arcs in games

      In the pre-computer era, games dealt almost entirely with loops.  The light arcs that games like Chess or Monopoly contained served the highly functional purpose of triggering a player’s mental schema.  Once that setup payload was delivered, the games focused almost entirely on loops. One could easily claim that historically the term ‘game’ was used to describe an entertainment made predominantly of loops.

      With the advent of computer games, designers started mixing more arcs with their loops. Adventure games, game endings and other narrative elements became more prevalent.  There are strong cultural and economic reason why this occurred at this period of time that are not strictly an inherent function of the computer game medium.

      The primary driver for the proliferation of arc-based games is that they fit nicely into the existing retail business model.  Over the past 40-years, the dominant way you made money off media was to sell the customer an arc, be it a book, an album or a movie.  Once they had consumed that, you sold them another one.  With a large enough portfolio of games (typically managed by a publisher), you’d get a reliable stream of revenue.

      As is the case with evolutionary systems, certain ill-fitting forms of games were punished financially and thus faded from the market. Assume you tried to build a popular evergreen game. You sell it once and that is the only money you get for the rest of the consumer’s life. The retailers didn’t want that outcome. Nor did the publishers. They preferred to sell players multiple games a year, year after year. The developers that made games that fit the constraints of this specific market reality flourished with profits from mega hits used to fund future moon launches.  Many of the modern game tropes such as beatable games, sequels, game concept conveyable by box covers, etc are a direct result this early retail environment.

      Again, this is a statistical process, not a conspiracy.  Mammals and dinosaurs coexisted for millions of year but the shifting climate ended up being more amendable to one form than the other.  During the retail era, evergreen games still existed, but in diminished quantities.

      Since systems are hard to understand, one popular just-so story that emerged during this period that arc-heavy games are some ideal outcome of new computer technology. This matured into a strange arc-worshipping segment of the population that predicts a technology-driven singularity for games that involves ever richer payloads and an eventual acceptance as an equal of other arc-centric media. Someone like David Cage, maker of Heavy Rain, is a modern example of such ideals.  But the roots go back much further to the dreams of early science fiction writers and researchers that had little practical experience with creating games.  They sold us a delightful dream for the future of games without understanding the first thing about the actual loop-like nature of games.

      On reflection, it seems quite false to claim computers enabled arc-heavy gaming. A choose-your-own adventure was technologically feasible a hundred years ago. This suggests that arc-heavy games are not nearly as inevitable as some might imagine.

      Consider the arcade market with its very different business requirements.  The arcade owners, publishers and developers were less interested in selling consumable boxes and more interested in repeat play.  This business constraint encouraged the creation of evergreen loop-based games that thrived for decades. The market and the culture hugely shapes the form of the games we make. It is certainly not locked in stone.

      The market is shifting once again.  With in-app purchases, there is a large financial benefit to keeping the player engaged both emotionally and financially for long periods of time.  A fit game is one that you play forever all while paying for your hobby.  It is not one you beat and cast aside. This suggests that loop-heavy games may be making a comeback.

      Untangling loops and arcs in existing game forms

      So how do we evolve our designs with the market environment?  One exercise I’ve been performing on various games is identifying loop and arcs in a popular genre and then removing the arcs to see if what is left stands on its own.  What I’ve discovered is that arcs are almost never critical game elements. You can remove them and still have a playable game.

      As an exercise, take your favorite genre (such as platform games) and remove the following:

      • Puzzles
      • Missions
      • Narrative sequences that are not specifically functional feedback that powers the completion of a loop.

      To take this one step further, remove any elements of a computer game that you can ‘beat’ or that render the game boring or meaningless upon repeated play.

      Can you make a wonderful game out of the remaining bones?  The vast majority of the time you can.  Even deeply arc-heavy graphical adventure games yield procedural hidden object games at their root.  Now, you can never get rid of arcs completely, nor would you want to.  Loops and arcs are ingredients and the goal is to create a new recipe with different mix rather than unquestioningly recreated the same meal again and again.

      A brilliant future for loops

      However, this is admittedly a rather reductive exercise.  What I’m far more interested in is what happens when we, as designers and developers, invest our full energy in exploring the potential of loops.  The language here is far less developed and it is an extremely fertile field for a young developer to make their mark.  Consider the following sparely settled frontiers:

      • Both Will Wright and Notch made millions by exploring the loops of player expression.  
      • Eve forges forth into new territory with every update by exploring the loops of economics and politics.  
      • Star Craft thrives because it taps into the mastery loops at the competitive heart of sports.  
      • No one is even talking about the loops inherent in religion, a system that has driven the behavior of humanity for thousands of years. 
      • Games of improv or bluffing or charades are all loop-based activities with nearly zero traction in the markets today.  These are games that can be played for life. 

      Conclusion

      Look for loops and arcs in your game.  What is the balance between the two elements in your design?  What does your game need?

      This isn’t a black and white situation and I respectfully ask you to avoid couching this in any tired us vs them terminology.  There is not one market.  You may find that the traditional arc-heavy recipes are exactly what you need.  If you are selling to a community whose norms for buying games were set during the retail era, creating a great beatable payload of entertainment may make you a lot of money.   Many of the popular indie sales channels remain conservative recreations of markets past.  It is a well trodden path.

      • Author evocative arcs
      • Build sequels 
      • Reduce portfolio risk in order to survive long droughts between mega hits 

      If you are making a more modern evergreen game, consider how loops may result in delivering long term value to the players.  Question the forms of a traditional game and ask yourself if they are still valid in today’s market.

      • Invent dynamic loops
      • Build a hobby
      • Create a fortified island nation with an ongoing stream of revenue

      This is admittedly the harder path.  You need to analyze your design preconceptions. You need to understand the psychological functionality of what you are building something instead of merely mimicking patterns of the last generation.  Break your game down into loops and arcs.  Understand what is filler.  Understand what core elements form a endless engine for generating value (be it ‘fun’ or your outcome of choice.)

      Above all, evolve.

      take care,
      Danc.