Value chains – A method for creating and balancing faucet-and-drain game economies

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INTRODUCTION

The problem with picking up sticks

Recently I was designing the harvesting and crafting system for our Animal Crossing-like game Cozy Grove when I ran into a problem: picking up a stick is not that fun. 

The core activities in a life sim are generally not full of mastery and depth. You chop trees. You dig holes. You pick up sticks. In isolation, each of these is dull. Our playtesters would harvest a leaf pile, get some sticks, and then put down the controller. They’d turn to me and ask “Uh, okay, where is the game?” 

If we were following the standard advice on prototyping core mechanics, we might as well stop development right there. Clearly the core was not fun. We tried extending the loops out from 5-seconds (gathering), to 30 seconds (wandering), to 5-minutes (selling). No luck. My playtesting group hated the game. 

Yet life-sims do exist! And they are delightful. Clearly there’s more to establishing value in a game than just perfecting a ‘fun’ core mechanic. 

Discovering value chains

It wasn’t until we spent 12-months building out the rest of the game – the crafting, the decorating, the daily pacing structures – that players finally began to value picking up sticks. Because it turns out the value of sticks was entirely driven by their utility in reaching future goals. And if those future goals don’t exist, the sticks have no value. 

You tend to see this scenario in high retention, progression focused games

  • The core mechanic is not the sole or even the primary driver of player value. 
  • Value for a particular action comes from how it facilitates subsequent activities.
  • Often players engage in long chains of rote economic activity in order to reach their actual final goal.

Why you should care

Understanding how to generate meaning with sticks is not an idle concern! High retention, progression focused economic systems are at the heart of most games as a service (GaaS). There’s a huge demand for economy designers who know how to build and balance robust game economies that provide rich value to players. 

Getting your economy design wrong costs time and money. Very often, it can kill the game. Yet game economies are also a rarely discussed black art. So it is hard to know where to start. And hard to hold constructive conversations with your team. There’s an inherent complexity to the topic that makes matters even worse.

So let’s try to improve the situation. 

What this essay covers

We’ll cover the following. 

  • Chapter 1: What is a value chain?
  • Chapter 2: Balancing value chains
  • Chapter 3: Architecture of multiple value chains
  • Chapter 4: Establishing endogenous meaning in games

The whole essay is around 30+ pages and can be a bit technical. Feel free to take it slowly. But if you are interested in game economy design, this is a good crash course.

CHAPTER 1 – WHAT IS A VALUE CHAIN?

We’ll model game economies and associated activities as endogenous (self-contained) value networks. These networks are composed of value chains. The value chains combine to form a full faucet-and-drain economy. 

Basic structure of a value chain

This is the shorthand I use when jotting these out on paper. 

  • You get a stick!
  • Which lets you make a lamp
  • Which lets you decorate your house
  • Which satisfies your need for self expression, the ultimate motivational anchor for wanting the stick in the first place. 

Notice the structure

  • Each node contains an output of some (currently unspecified) action. 
  • The nodes are connected to one another in a linear fashion that’s easy to read. No strange loops or spaghetti-like diagrams. 
  • The chain terminates with an anchor node representing player motivations. 

There’s a lot that’s not specified here in the shorthand version. We’ll get into more verbose versions below.  

But you can see some useful traits

  • By jotting this down, you are forced to consider the direct purpose of each resource. 
  • And how it relates to ultimate player motivations. 
  • If the chain is broken, imbalanced or obfuscated in some way, players will stop finding value in the early steps of the chain. 

Inputs and outputs

Now let’s look at a more verbose description of a value chain. In practice each node is composed of three elements:

  • Action: What the player (or the game) is doing to cause a change in the world. 
  • Inputs: Resources that the action requires. These can be tangible resources or abstract concepts like time. 
  • Outputs: Resources that are the result of the action. Again, they can be concrete or abstract in nature. The anchor node is always abstract since it represents an internal psychological state. 

The diagram above is pretty, but hard to quickly put together. In practice, I use a text-based format that can be typed out in any basic text editor. Feel free to adapt the formatting to your project; it is the ideas that matter. 

In purely text format, we get something like:

  1. ChopTree (-treeHealth & -player time) | +stick 
  2. Craft ( recipe & -stick & -rag ) | +lamp 
  3. Decorate ( lamp, other decorations ) | Decorated Space 
  4. Decorated Space |Self-expression anchor

What this says is: 

Step 1: “ChopTree (-treeHealth & -player time) | +stick”

Player chops a tree and gets a stick. 

  • The symbol means that the action consumes treeHealth (a variable on the tree) and player time. This makes the action a sink in economic terms.
  • The & symbol means that this action takes both health AND player time. If one is missing, the action can’t be performed.  
  • The + symbol means that this activity is a source for sticks. 
  • The | symbol splits up input activities from output resources. 
  • The action is italicized for clarity. 

Step 2 “Craft (recipe -stick -rag) | +lamp”

Then the player crafts with a recipe, stick resource and rag resource. 

  • The stick and rag have the symbol next to them indicating this action is a sink for those resources and they are removed from the game economy.
  • The recipe is not consumed. It has no next to it. 
  • The output of this action is a lamp. Notice the + symbol signalling a source. 

Step 3: “Decorate ( lamp, other decorations ) | Decorated Space”

Then the player decorates with the torch

  • The , symbol represents options that are valid inputs. Decoration can occur with the torch OR any other decoration. 
  • There’s no concrete new resource here that is produced but we do get a decorated space as an output. 

Step 4: “Decorated Space |Self-expression anchor”

Finally, the player serves their goal, which is to express themselves. Self-expression is a strong intrinsic motivation for some players and acts as the anchor for the entire chain.

Key concepts when working with value chains

Now that you have a definition of value chains, let’s look at how they tie into some other important game design concepts. 

Value chains are one way of structuring your game’s internal economy

Internal economies refers to the practice of modeling economies as a network of the following basic operations

  • Tokens: Resource tokens that flow between various nodes in the network of player or system operations. 
  • Sources: A node that creates new tokens and add them to the flow.  
  • Pools: Nodes that accumulate or hold some number of tokens
  • Transforms: Nodes that transform of tokens into other tokens
  • Sink: Nodes that destroy those tokens. 

For a rich description of how internal economies work, read Joris Doman’s book Game Mechanics: Advanced Game Design. He goes into common design patterns and explains the ideas in more detail. 

You can describe almost any economic system in a game using these basic elements. But that flexibility also can be overwhelming and hard to communicate. 

Value chains are a specific sub-case of an internal economy. They make use of all the basic operations but in a far more restrictive manner that makes both the construction of economies and more importantly, their subsequent analysis easier. 

Value chains are a form of “Faucet-and-Drain” economy

In a typical real-world economy, tokens circulate in enormous pools that slosh back and forth due to feedback loops and emergent market dynamics. These are enormously complicated and difficult to visualize. 

However, value chains focus on a simplified cartoon economy known as a faucet-and-drain economy, defined by strong sources and strong sinks, limited object lifespans and limited circulation of goods. 


Example of a ‘faucet-and-drain’ economy from Ultima Online, The In-game Economics of Ultima Online, Zachary Booth Simpson, 1999. 

A faucet and drain economy (like the one visualized for Ultima) might seem complicated at first glance, but it has some greatly simplified attributes

  • Faucets: Resources in the game are generated from nothing as needed. They are virtual so we can make as many as we want (or as few)
  • Transforms: The resources flow mostly one way into a series of transforms to produce various other elements players desire. These are all designed game activities. 
  • Drains: We get rid of excess materials. Again, they are digital so there’s no unexpected externalities like pollution or landfills. We press a button and boop, they are erased from existence. 

These faucets and drains map directly onto the various portions of the value chain. 

  • Early stage of the value chain has sources: Players perform actions (the core gameplay!) and generate a steady flow of base resources. 
  • Mid stage of the value chain transforms resources: Those resources are transformed into a very small number of intermediate resources. 
  • End stage of the value chain has sinks: Finally players pay resources into sinks that help them gain access to whatever their anchor motivations might be. As a result, there is a steady stream of goods being destroyed. 

Faucet-and-drain economies have some really useful attributes for economy designers.  

  • Constant demand: There’s a nice velocity of goods flowing through the economy and out via the sinks. This means we can easily incentivize players to continue engaging in gameplay actions that generate resources. 
  • Limited feedback loops: There’s limited pooling of excess resources. This leads to simpler dynamics and fewer unexpected feedback loops. 
  • Easier explanation: Real economies are complex and hard to talk about. A faucet-and-drain economy is much easier to explain to players. This lets them form models of cause-and-effect and helps with their long term planning and engagement. 
  • Easier balancing: You can usually trivially balance sources and sinks against one another.  If there isn’t enough of a resource being created, the designer can tweak some number so player actions generate more. Or decrease the cost of sinks. If there is too much of a resource being generated, the designer can increase the cost sinks or reduce the sources. 

Game economies as cartoon economies

Note: You don’t see many pure faucet-and-drain economies in the real world. All real-world economists need to deal with the messy reality that real-world extraction of resources and making of objects is incredibly expensive. You can’t wave a magic wand to increase some source of scarce goods. So instead, we see more circular economies where limited rival goods are created infrequently and then circulate within a competitive market for a long period of time. Your modeling must include supply chains, warehousing, environmental externalities, transaction costs and more. 

Games economies are special since there is zero cost to creating, transforming and destroying new digital resources. If we want every person in the game to get a puppy, we snap our digital fingers and it is done.

This means every economic feature associated with supply chains, transaction costs and various externalities is a design choice; we include them if they make the game better. As digital economy designers, we can use these special powers to make our job as game economy designers easier and the experience of playing the game better for our players. 

Value chains are structures for creating demand 

A node creates demand and value for players to engage with earlier nodes in the chain. You can say that a node ‘pulls’ resources up from those early nodes. 

The powerful sinks at the end of each value chain acts to maximize flow of resources through the chain. Because we usually want clear systems of cause and effect so players can easily plan ahead, building murky pools of inscrutable assets can hurt gameplay if not done with care. 

Big pools reduce pull. However, see “The emotions of scarcity and abundance” below for more detail on how planned scarcity and abundance (within a narrow band of outcomes) can drive desired player emotions. 

Value chains connect to real world motivations through Anchors

Traditionally, when we think of value, we often think of valuable goods as those that serve a physical need like food or shelter. However, in games, there is no actual need for food or shelter to satisfy. A game will not feed you if you are hungry. Instead valuable digital goods are those that serve a player’s psychological needs. Game food in a game like Valhiem fulfills a player fantasy of survival (mastery over the environment), not actual hunger.  

When designing value chains, anchors are how we define and represent these psychological needs. And by inserting them at the end of each value chain, we call out how meaning is carried through the earlier stage economic nodes. A powerful anchor can be a big reason why earlier nodes have value. If you don’t identify how each node serves your psychological anchors, you’ll very likely end up with disconnected nodes of isolated atomic meaning. 

Other sources of meaning: Each node can always generate meaning. It can contain a beautiful interaction. Or a delightful puzzle. Or moment of insight. By no means am I saying that only economic value networks provide meaning or value! However, if you can connect these small moments of value together by a self-reinforcing value network, you build a self-consistent space for players to form and pursue meaningful goals. 

Possible anchors: There are lots of motivations that act as anchors. For example SDT (Self Determination Theory) lists

  • Autonomy: Do you accept the decisions you have made?
  • Competence: Do you feel like you are gaining mastery or competence in your actions 
  • Relatedness: Are you supported and do you support others in a pursuit of self determination?

Or you could reference work by Quantic Foundry, who has tried to map out some of the player motivations from popular games. 

  • Destruction
  • Excitement
  • Competition
  • Community
  • Challenge
  • Strategy
  • Completion
  • Power
  • Fantasy
  • Story
  • Design
  • Discovery

Player interviews: Ultimately, these models are only a starting place for finding your game’s anchors. You’ll discover that needs are nuanced and perhaps best uncovered by talking to your players and finding what resonates with them. When you hear that your game changed someone’s life for the better, don’t roll your eyes. You are likely observing an anchor for a value chain.

I remember once someone told me that my cooperative factory building game helped restore his faith that people can be good. And how he made lifelong friends just through playing the game. Those shared goals, trust and long-term friendship are powerful anchors that made the atomic actions of placing road tiles and cranes very meaningful. 

The deep meaning behind value chains is this: By understanding anchors you start to understand how your game provides tangible value to your player. Games are never ‘just games.’ People play them (and keep playing them) because games add real value to their lives. You need to design with that goal in mind. 

Can economic systems serve intrinsic motivation?

At the most basic level, extrinsic motivations are when you feel forced to do an action in order to attain some other purpose. Intrinsic motivations are when you want to do an action for the sake of the action itself. 

It is around this point that folks trained in the pop-psychology pits of YouTube game design start worrying that economic systems of this sort promote coercive extrinsic motivators. The actual answer is complicated. 

Some things to keep in mind that are usually not discussed in a typical like-share-subscribe rant. 

  • Extrinsic vs Intrinsic motivation is a spectrum: It is not a binary. A lot of things we do are a little extrinsically motivated and a little intrinsically motivated. So if you are desperate for black and white morality, be aware this is a topic that is mostly shades of gray in practice. 
  • Individual perception matters: People often move from being extrinsically motivated to being intrinsically motivated for the same exact action and reward as they incorporate the action into their personal feelings of self-determination. For example, at one point I made a french press coffee each morning to wake up and get my caffeine. It was extrinsically motivated, rote behavior. But then I started thinking of myself as a coffee drinker and over time the rote activity turned into a ritual that I truly enjoy. People shift and change. Perception is often more important than the exact mechanics or numbers involved. 
  • Intrinsic motivation is often a journey: The story about coffee suggests another truth. Players don’t start out intrinsically motivated. Often they are just playing around, following once bright sparkly to the next bright sparkly. Overtime they discover how a set of rote actions serves one of their unmet deeper human needs. This can take time and learning! But by the end of their personal journey, the previously rote actions are transformed. They become time spent with purpose and intention. 

Why you choose to perform a rote action as well as your level of personal buy-in to this choice have a huge impact on whether or not an activity is seen as intrinsically or extrinsically motivated. If you are interested in this topic, I highly recommend doing a deep dive into SDT since it is one of the few experimentally verified models of key factors involved in shaping motivation. 

Some tips

  • Design each action node to support feelings of autonomy, competence and relatedness. The more each moment in the game supports feelings of self-determination, the more likely players feel intrinsically motivated. 
  • Ensure each anchor supports feelings of autonomy, competence and relatedness. If your ultimate anchors are tied to materialistic numbers going up like “Make as much money as possible”, you really aren’t supporting any of the key factors related to intrinsic motivation. 
  • “Mastery” is often a short term motivator: Historically games have focused on helping players feel competence by teaching them novel skills. For example, you learn to double jump for the first time or beat a new boss puzzle. But eventually players learn the skill and chunk it into a rotely executed tool. True evergreen mastery mechanics are rare and expensive to invent. But ‘infinite mastery’ doesn’t need to be the goal of every mechanic in your game! Because it turns out that rote competence in service of other unmet needs still triggers feelings of competence! Think of mastery in terms of the player creating cognitive tools they can then apply to serving higher order needs. 

Things to listen for in playtests 

  • Coercion: Do players say they feel coerced into doing something? If you hear this, you are leaning too heavily towards extrinsic motivators. 
  • Changes in perception: Do players say “This didn’t meet my initial expectations, but now I really enjoy it.” That suggests players are transitioning from extrinsic motivation to intrinsic motivation. Ask them why they enjoy it. There’s a very good chance you’ll discover some powerful anchors in your design (that you can then amplify or reveal sooner!) 

The big picture: There’s a fun study that suggests games that lean heavily on intrinsic motivation tend to improve player’s well-being. And they tend to have longer term retention and improved monetization. While games that lean heavily on extrinsic motivations tend to harm player’s well-being. 

So even though the topic is messier than suggested by internet moralizing, it is still worth building in the factors of intrinsic motivation into every step of each value chain.

CHAPTER 2 – BALANCING VALUE CHAINS

Why you balance your economy

One of the critical jobs you’ll perform as an economy designer is making sure your economy is balanced. As game developers, we are selling amazing experiences and a poorly balanced economy leads to a crappy player experience. 

From the player perspective, an imbalanced economy produces complaints that look like the following: 

  • X activity or resource seems pointless. 
  • X is boring
  • I didn’t even notice X
  • I don’t understand why I’m playing. 

These mundane phrases are some of the most important pieces of player feedback a designer can hear. Your players are not being stupid. They are giving you incredibly valuable signals on what is wrong with your game. 

Role of value chains in economy balance

There are many potential root issues that drive this sort of feedback. The trick is finding them. For example, an abundance in one location might be driven by a lack of sinks further down the chain. If you don’t know the structural dependencies of your economy, you’ll struggle to pinpoint the root cause of a player’s report. 

Value chains provide an analytical framework that helps you do the following:

  • Define each resource in the game and why each is valuable. 
  • Define structures for how resources are relate to one another in a meaningful way. 
  • Analyze, pinpoint and fix issues where resources or activities are not valuable. 

Every prototype you make starts out poorly balanced. And then you iterate on the balance to make it better. Value chains speed up iteration by simplifying the underlying problems and helping you identify and classify observed problems faster. They help you reduce the cost of fixes by targeting specific problems while limiting ripple effects.

Balance from the technical perspective

From a technical perspective, we can define a balanced value chain as one where there is a strong enough set of anchors and associated sinks to consistently pull resources up from all nodes along the chain. 

  • The player is motivated at each node to perform game activities in order to reach subsequent nodes (and ultimately the anchor)  
  • The player doesn’t face an overabundance of a particular resource that swamps sink or makes exercising an earlier node’s activity meaningless. 
  • The player doesn’t face extreme scarcity of a much needed resource that makes grinding an earlier node laborious or irritating. This can lead to pacing delays or grinding burnout. 

There are a few key steps to balancing a value chain.  Each of these is a major topic we’ll cover in detail. 

  1. Step 1: The structure of the value chain must have clear links of cause and effect carrying over from each node all the way to the anchor. 
  2. Step 2: You need to identify the types of sources and sinks used in your value chain.
  3. Step 3: You need to match the power of your sinks with the power of your sources. For example exponentially increasing sources should be matched by exponentially increasing costs on sinks. Mismatches here result in nearly impossible to balance economies. 

Step 1: Debug the structure of the value chain

At the most fundamental level, a value chain is a connected series of economic actions. If the links in the chain don’t connect to one another at a structural level, the chain fails. This is super useful! 

  • We can look at the structure of any specific chain and quickly identify structural errors without taking into account the massive complexity of the whole economy. 
  • We can often further focus on a single node in a single chain to identify the issue with great specificity. 

Errors at the structure level are great to catch early since they often result in economies that are impossible to balance. Defining and debugging the structure of your chains is a wonderful pass on any economy design task. 

Issue: Break in the value chain

The most common root issue is that there is no subsequent link in the value chain! Most actions in games quickly get mastered, chunked up and turned into a rote task. If there isn’t a reason to do the action, it becomes as meaningless as a disconnected doorbell. 

Solution

  • Write out the value chain for this action. 
  • Make sure there’s a consistent chain of nodes all the way to an anchor. 

Issue: Lack of sufficiently compelling anchor

Identifying compelling anchors is a rarer skill. So many designers just leave out this step entirely. However, the game then falls flat and they don’t know why. 

Solution

  • Your intrinsically rewarding anchors are often very related to your game’s core pillars or promises. 
  • Do the exercise of asking what players really want out of your game in terms of need fulfillment or core motivations. 
  • See if you can have your value chains directly contribute to these ideas. Your game will become stronger. You’ll also gain a culling device for eliminating features that don’t serve the pillars of your game. 

Issue: Visibility on the chain of cause and effect necessary to reach the anchor

In games, players engage in interaction loops that teach skills on how to manipulate the world. Interaction loops and arcs (also known as skill atoms or learning loops) are the fundamental iterative sequence of modeling, deciding, acting, processing and responding that occurs within any computer mediated interaction. It is the heart of any interaction design. 

Here’s how interaction loops map onto value chains

  • Each interaction loop directly corresponds to the action element inside node of the value chain. For example, there is an interaction loop about learning to harvest leaves. And that maps onto the value chain node about harvest leaves and getting sticks. 
  • Exercising an interaction loop yields emotional reactions. There’s evocative stimuli (ooh, pretty jewels go pop!), mastery, autonomy and more. 
  • A player exercises an interaction and learns cause and effect. A chunked skill always results in a lesson, or cognitive tool for how an interaction can manipulate the game. For example, players learn that if they harvest a leaf pile, they’ll get sticks. 
  • The player can then use their new acquired tool to pursue goals. This corresponds to a subsequent node in the value chain. For example, if a player wants to build a decoration, they now know they need a stick.

Interaction loops are recursive in nature and occur at pretty much every level of gameplay. I’ve written a lot about them over the years and they are essential to almost every part of my design practice. But that’s far too much to cover in this relatively small essay that I’m desperately trying to keep focused on game economies. If you want more information on interaction loops, check out this presentation: Interaction Loops – Public

The important part is there are many ways a specific interaction loop can go wrong. The interaction loop may have the wrong affordances. The game could provide poor feedback to the player. The player might not have learned foundational skills. Etc, etc. 

This is also a deep topic. For more information on how to diagnose issues with interaction loops and skill chains see: Building Tight Game Systems of Cause and Effect 

From the perspective of analyzing value chains, you should know that a failure inside a single node of the chain can destroy the value of all subsequent nodes along the chain. Knowing these dependencies can help you backtrack and find the root causes. If you can track a big economic issue down to a single interaction inside a single node, you can make far more targeted changes. 

Issue: Visibility of the anchor

You may have a strong anchor for a value chain, but only long term players end up figuring it out. And new players, because they don’t see how the game fulfills their needs, decide to leave early. 

Solution

  • Identify your value anchors and tell them to the player at the very start of the game. This is your player promise
  • They won’t be able to experience the satisfaction of the anchor immediately, but they’ll know what they are working towards. And this should give them a long term goals and perspective on the long term payoff of current tasks. 
  • The player promise can be couched using a narrative frame. For example, a need for completion and accomplishment is often couched as ‘beat the game’.  A need for dominance and mastery is often couched as ‘beat the final boss’. These simple frames help contextualize the abstract psychology of an anchor as a familiar concept. This can provide enough visibility on an anchor to justify earlier actions. 

Issue: Weak player motivation associated with the anchor

Motivation and their associated narrative frames are not universal! Many players don’t care about dominance or mastery. In our life-sim, Cozy Grove, players were actively repulsed when mastery elements were experimentally added. If you present the wrong audience a game about beating the final boss, they will leave immediately because their true needs are not being met.  

Solution

  • Talk to target players. Tell them your player promises. See if they are excited! If you are a new developer pound into your head that there is no universal gamer profile. Nor is there a game that is perfect for everyone. (You’ve been lied to if you believe this) 
  • If they aren’t excited, you need to either find a different set of player promises or a different target audience. 
  • Don’t be afraid to workshop your player promises until you find a strong audience fit. A mismatch here can cause your game to fail before you even start. 

Step 2: Identify types of sources and sinks

In more freeform descriptions of internal economies, there are innumerable ways of adding resources and extracting resources from the economy. Once you start including feedback loops, pools, conditionals on actions and other attributes of an internal economy, you might as well have written a full Turing complete simulation. Such a system is difficult to explain, difficult to reason about, and difficult to balance. (Check out Machinations.io if you are interested in exploring what these simulations can look like. It is a wonderful tool.) 

In my personal practice building game economies, I’ve hit upon a relatively robust simplification where I categorize source and sinks into a few common categories. There are certainly edge cases that these types will not cover. However, by restricting your economy design to well-defined and easily manipulated components, you make balancing far easier. 

In this section we’ll talk about how to approximate complex economic structures with various curves. 

  • The 5 major types of sources: Capped, Trickle, Grind, Investment and Random
  • The 4 major types of sinks: Fixed, Repeatable, Exponential, Competitive
  • The 3 big balancing challenges: Scarcity, Abundance, Variability

Source Type: Capped (Constant) 

Capped Example A: At first interaction, the player gains 10 resources. And no more afterwards

In this type of source, there’s a fixed amount of the resource that comes into the game via some action node (or nodes). 

  • Player completes an action. I sometimes use the metaphor of ‘turning a crank’ where the player needs to execute a full interaction loop of: mental model -> decision -> action -> rules processing -> feedback -> updated mental model and resources. 
  • The total amount that comes from the action is fixed
  • Executing the action again (if even possible) does not provide more of the resource. 

Some variations on capped sources include:

Capped Example B: You can perform the action multiple times, but you get a capped amount of resource
Capped Example C: You perform the action multiple times and get diminishing returns that rapidly converge on a value that is equivalent to a capped number.

Capped sources are one of the most common sources, especially in single player games with a fixed completion point. They tend to be used in easy to control systems, but can be a little brittle. We’ll get into that more when we discuss numerical balancing. 

Source Type: Trickle (Linear) 

In this type of source, there’s a fixed rate of a given resource coming into the world during a given time period. 

Trickle Example A: Every time period (t), we gain 2 resources. This is a linear equation where TotalResources = 2*t
  • Again, like with capped sources, a trickle resource delivers resources whenever an action node is completed. 
  • However, unlike capped sources, you tend to get a consistent amount every single time the action is repeated. Forever. 

Accumulation challenge with trickle sources: On larger time scales, you can accumulate an infinite amount of that resource. Say you earn 10 gold every day for signing into the game. After 10 days, you have 100 gold. After 100 days, you have 1000 gold. After 2 years, you have 7300 gold. 

When players have an excess of a resource, they are less economically motivated to engage with the production nodes of its value chain. Though they may still perform related actions for the intrinsic joy of it, the marginal value of gaining an additional resource is low. 

We’ll keep seeing excess accumulations show up as one of the failings of an imbalanced economy. 

Variation: Limited actions per time period: In Animal Crossing, there are 6 rocks that spawn in the world and can be mined once a day. This is still a rate limited source, but the limit is placed on the actions the player can perform, not the amount of resources produced. 

Variation: Capped pools fed by a trickle source: A common type of trickle resource is energy in a F2P mobile title. Energy recharges each day and can then be spent on a limited number of actions. Some elements of this patterns

  • The action of the energy production node is simply ‘waiting’. Time passes and you automatically get more energy. (You can pay, but that turns this into an investment source, which we’ll cover in detail below) 
  • There’s a capped pool, which is a pool that holds the energy resource. It is capped in that it only holds some maximum amount. After reaching the cap, any additional energy is lost. 

Capped pools are one partial solution to the accumulation challenge. In our gold example above, imagine that gold feeds into a treasure chest that can only handle 100 gold. If you wait 10 days, you’ll have 100 gold. If you wait two years, you’ll still have 100 gold. 

Capped pools are unfortunately not a complete solution. Someone who diligently empties the pool every day still will be able to spend all 7300 gold over two years. So you still need a mechanism for dealing with excess. 

Source Type: Grind (Linear) 

A grind source is one where players can spend near unlimited external resources such as time or money to increase the amount of a given resource. Again, you’ve got an action the player performs on a node that generates resources. But they can grind that action by performing it as many times as they desire. 

Though on the surface this looks a lot like a trickle or capped source, from a balancing perspective it is very different. 

  • The total amount is mostly uncapped. It is limited only by how much a player wants to grind overall.
  • The rate is also mostly uncapped. It is limited by how much a player wants to grind in a day. 
Grind Example A: Player 1 plays 4 times as much as Player 2. And extra on the weekend. They earn a huge surplus relative to player 2. 

The most common example is that the player invests more time by repetitively performing an action again and again. Though time is limited in reality, in practice we often balance our games by assuming a certain moderate level of engagement. And someone who plays 14 hours a day, 7 days a week can grind out surprising amounts of a resource. 

Variability Challenge: The big problem with this source is that it is highly variable, which makes it hard to balance. A player could not grind at all. Or they could grind 18 hours a day for 300 days. In one case, you’ve got scarcity. In another case, you’ve got overabundance. Both players will complain that the economy is poorly balanced. 

The pattern of play varies how much a grind source produces. In the chart above, we see a bump for player 1 during the weekend. They may experience a huge glut of resources as a result. 

It is often good to convert this into a capped or trickle source. Or pair it with an exponential sink. 

Source type: Investment (Exponential) 

A common structure in internal economies is the positive feedback loop

  • Player does an action
  • This gives them resources. 
  • But these resources ‘feed back’ into the original action. They can invest the resources to do more of the action. 
  • Which in turn gives them even more sources that lets them to the action even more. 

Positive feedback loops result in hard to balance economies. 

  • Early on in the investment cycle, these sources produce small quantities of a resource.  
  • Later on, positive feedback produces exponential growth of a resource. 
  • But this exponential growth oly happens if the feedback loop is being actively exploited by a smart player. 
  • So we end up with scarcity early on and then are hit with abundance and variability later. It can be a huge pain. 

An example of an investment resource might be fruit trees in Animal Crossing. When you start out, you feel great harvesting a single fruit tree. 

Simplification: Treat Positive Feedback Loops as Exponential Sources: For years, I’ve been relying on a straightforward simplification: I treat positive feedback loops as exponential sources. I design defensively and assume the worst case scenario where feedback loops are going to get out of control for some players. 

Investment Example A: Player earns a little resource early on. Which they invest to produce more of that resource. Later. This is approximated by an exponential curve.

This cartoon model of a positive feedback loop has several benefits

  • Instead of dealing with complex, tricky to communicate diagrams that chart out the exact structure of a feedback loop, we can just say a particular node is an investment source. This lets us continue to deal with the economy using targeted value chains. 
  • When we get to balancing sources and sinks, it unlocks clear numerical tactics for sopping up abundant resources. Instead of some mysterious dynamic system of emergence, the source becomes just another common type of math curve. 

Variation – Increasing the starting baseline: We can help eliminate scarcity during early stages of an investment source by adding a trickle source to fall back on. 

Animal Crossing’s fruit is not a pure investment source. Instead, they start you out with a set of ‘wild trees’ that let you harvest at least some baseline quantity of fruit each day even if you haven’t engaged with the investment loops.  

Investment Example B: Player earn 50 resources each tick from a trickle source. They then invest that to gain exponential growth in their access to that resource. 

Source Type: Random (Noise function) 

Some sources come with high variability. The most common of these in video games is a loot drop table, but almost any game that uses dice to determine resource rewards is using a random source. 

  • Random sources have a distribution of outputs: They can be a normal curve, exponential distribution, pure random noise or some other histogram. These will usually have some sort of central tendency where on average you can have a typical result. 
  • Random sources are really just a noise function applied on top of one of the other source types. So you can have capped, trickle, grind or investment sources with randomness. 

Simplification: Use the mean of normal distributions and convert to a less random source type: I tend to work with average outcomes when looking at how random sources contribute to the economy long term. This turns a random source into one of the other sources (capped, trickle, grind, investment). 

This simplification doesn’t work for very short term variability balancing, but can be highly effective for understanding scarcity and abundance in longer lasting games. 

Variation: Constrained randomness: You almost never want pure randomness in an economy. In your million of players, there will be that one person who rolls 1s for most of their game and attains 1% of the progress of the typical player. The system isn’t broken. Sometimes true randomness results in crap outcomes. 

If possible, use systems like ‘drawing-without-replacement’ (decks of cards with a discard pile work this way) or various pity systems that guarantee a drop after X draws. These ensure that the outlying experiences aren’t substantially different from the average experience. 

Again, we aren’t interested in techniques that allow you to balance any system. We are interested in building systems that are easy to balance. 

Sink Type: Fixed (Constant) 

Now we get into sinks. These extract resources from the game and thus limit accumulation and pooling.  You’ll see that these fit into categories very similar to sources (constant, linear, exponential)

The simplest sink is the fixed sink. When an action on a node in the value chain occurs, a fixed amount of resource is removed from the game. This is not repeatable. This is the mirror of a capped source. 

There are lots of examples of fixed sinks

  • A powerup you can purchase once. This takes a fixed amount of currency out of the economy. 
  • The one time cost in XP to earn the next level in an RPG. This takes a fixed amount of XP out of the economy. 
  • A boss you can beat a single time and in the process it uses up healing or mana potions. 

Most fixed-length games make heavy use of fixed sinks. You put them all in a spreadsheet and tally them up. This tells you how much you can give out from capped sources. 

Sink Type: Repeatable (Linear) 

The mirror of a trickle source is a repeatable sink. Every time the action for a node is performed, a fixed amount is removed. However, unlike a fixed sink, the action can be repeated multiple times. 

Some common examples of repeatable sinks

  • Damage being done to someone’s health bar. Each time the attack repeats, the same amount of health is lost. 
  • The crafting cost for a crafting recipe that can be crafted multiple times. 
  • A lamp in Valheim you need to regularly refuel or else it goes out. 
  • The cost to buy a consumable item in the store that replenishes each day. 
  • The cost to purchase a tree in animal crossing. 

Why not distinguish trickle sinks and grind sinks? You’ll notice that there’s no mirror sink to the grind source. You can absolutely have a trickle sink that only allows a certain amount of some resource to be destroyed in a given time period. Or a grind sink where players must grind to remove more of a resource. 

In practice however, these distinctions tend not to matter too much. Repeatable sinks are naturally limited by the supply of a resource. So we don’t get the specific runaway cases like ‘grinding’ that need special attention like we do with sources.

Sink Type: Exponential (Exponential) 

The mirror of the investment source is the exponential sink. In this sink, to get the next incremental (linear) increase in output, we logarithmically or geometrically increase the input quantity. This means there’s always room to sop up more. 

Some examples of exponential sinks

  • Each additional level for an RPG character costs exponentially more than the last level. 
  • In an idle game, each upgrade to an idle resource generator costs exponentially more than the previous upgrade. 

Sink Type: Competitive (Adaptive) 

There’s a specific type of sink that doesn’t have a clear mirror source. A competitive sink is a form of adaptive sink. In a competition between multiple players, whoever puts in the largest amount of a resource gets the largest prize. 

  • Pro: The nice thing about this sort of sink is there’s no top end so it can suck up lots of resources. 
  • Con: However, it can only be paired with competitive motivational anchors. And only a tiny percentage of the population is motivated by competition (mostly young males). So there are limited types of games you can use this. 

Examples of competitive sinks

  • Guild vs Guild competition in a game like Clash of Clans
  • Armies battling in an RTS game. 

There are lots of variations of this type of sink

  • Races: Players try to reach a specific goal. Whoever reaches it first, wins. There can be vast and expensive chains around training and other improvements to enhance your ability to get ahead of others. 
  • Leaderboards: There are more than two players competing and the positions are ranked relative to one another. So someone comes in 1st place, 2nd place, 3rd place, etc. Often the rank is measured within a league or session window. 

Mixing and matching sinks

All these types can also be mixed and matched. Idle games use leveling as a repeatable sink whose cost increments exponentially each time you level. Leveling can be fixed at a fixed number of levels.  So short term, a sink is exponential, but long term it is fixed. 

Like sources, it helps to classify a sink within a given time window. 

Step 3: Match power of sources and sink

You can essentially classify these sources and sinks according to their power

  • Constant: These are your capped sources and fixed sinks. This is the lowest power. (x^0) 
  • Linear: Trickle sources, Grind sources, Repeatable sinks. (x^1) 
  • Exponential: Investment sources, Exponential sinks. This is the highest power. (x^1+)
  • Adaptive: Competitive sinks (A>B). These are special cases. 

When balancing a value chain, it is immensely helpful to have lower power sources feed into equal or higher power sinks.

The challenge of overabundance

A slight amount of periodic scarcity or feelings of abundance can provide necessarily emotional variation to your experience. However, if you don’t balance your sink types, then as time goes on, a resource starts to accumulate in a pool somewhere and can’t be spent. This creates overabundance

When there is no chance of scarcity or  when there’s no use for those pooled resources, people stop caring. 

  • It may be trivial to satisfy or even exhaust their motivational anchors. If you are motivated by status and you can instantly buy all the high status clothing, why bother continuing to exercise that value chain? 
  • There is no longer a pull on the earlier nodes in your value chain that produce that resource. If you have all the sticks you’ll ever need, why bother ever harvesting another dirt pile?

For example

  • In CRPGs where you can sell things to vendors, you end up with millions of gold. But you have max level equipment. So what is the point?
  • In MMOs, players eventually have access to a 1000 useless +10 swords. This is known as mudflation and creeps up on games over the course of years. Why keep killing rats?

Of course not all value chains need to last forever. 

  • If you’ve created a fixed sink for a given value chain and thus have planned its eventual obsolescence, then it may be fine to let resources accumulate.
  • Or the player may enjoy messing about with a large amount of resources in a creative mode. They find this intrinsically rewarding and don’t need the careful scaffolding of motivations that a taut value chain provides. 

But intrinsic motivation is something that most people need to slowly work their way towards. They benefit from practicing an activity for long enough to know they enjoy it and eventually understanding how it serves their needs. This onboarding via explicit affordances and feedback seems particularly important when a player is operating within the artificial cartoon value structure of a video game. 

So reducing overabundance, at least in the early portion of a game and especially in games without creative anchors, is usually a good starting point for balancing your economy. 

The good news here is that big desirable sinks make it trivial to balance most faucet and drain economies. They create a need for resources which in turn causes the players to engage in actions all the way down the value chain. 

Balancing a fixed-length game

The easiest example is the fixed-length game. These are ones where you can map out the economy for the entire play experience, from start to some finite completion. These are good learning projects for new economy designers. 

  • List your sources in a spreadsheet for each node of the chain: If you have a fixed-length game composed of a series of capped sources, make a spreadsheet and add up those resources the players will encounter over the course of the game. You can also sum up trickle sources since you know when the game will end. 
  • List your sink for each node of the chain: Now make sure you have a set of fixed sinks that consume those resources. Include repeated sinks as well. Again, add them up!  
  • Golden path modeling: For trickle source and repeatable sinks you’ll need to decide how many times an ideal player interacts with each. This ‘golden path’ won’t be followed by every player, but it helps you approximate how much they’ll earn and spend. 

When picking which sources and sinks to pair, I use the general rules of thumb

  • Capped sources can be paired with fixed sinks. 
  • Trickle sources can be paired with repeatable sinks. 
  • For a fixed length game, investment sources always turn into capped or trickle sources. So you can use the previous two rules. You can be paired with exponential or competitive sinks. These are higher power and will sop up your sources. However they can be overkill, introducing mechanical complexity you may not need. 

Balancing an ongoing game

For an ongoing game, you again set up your per chain spreadsheet of sources and sinks. 

  • Since time keeps going, you don’t know how long a player will play. 
  • One trick is to think in terms of balancing within a period of time. How do flows add up within a week or a month or season of play? If you can find that repeatable ‘long session’, you can model that out and find how sources and sinks balance. 

When picking which sources and sinks to pair, I use the general rules of thumb

  • Capped sources can be paired with fixed sinks. These usually show up only during fixed-length sub-games within the live game. Examples include tutorials and limited-time events. You may want to avoid fixed sinks outside of these situations. Time is infinite and long-term players will overwhelm fixed sinks. 
  • Trickle sources should be paired with strong repeatable, exponential or competitive sink. 
  • A grind or investment source will always swamp a fixed or repeatable sink. Instead pair them with exponential or competitive sinks. 

You almost never want a true exponential source. Long term, these make your life painful. They are hard to model mentally and small mistakes result in extreme resources pooling. 

  • Try capping your investment sources. Any exponential power upgrades in an RPG are usually controlled with a hard level cap. Another common technique is to limit the number of investment slots you can use. Both these options turn an investment source into a more manageable trickle source. 
  • Idle games pairing an exponential source with a slightly higher power exponential sink. Even these structures don’t last forever since exponential investment waits start becoming boring. So they rely on hard resets via ascension mechanism to escape the trap of their exponential sources. 

Lean towards taut chains

When balancing sources and sinks, you typically want the sinks to be a little larger than the sources. 

  • Not too much or that results in dead points in the spend where players suffer from painful scarcity. 
  • Not too little because then you get pooling of resources and lack of pull on activity nodes within your value chain. The chain should always be pulled somewhat taut. 

If you’ve done your work matching source and sink power and you’ve isolated your value chains (see multi-chain architecture below), the exact balance numbers are less important than you might imagine. 

The emotions of scarcity and abundance

Once you get your general economy balance under control, there’s a huge amount of emotion you can extract from relatively minor variations in tautness. 

  • Scarcity: When players feel scarcity, they’ll be highly motivated to search out and harvest scarce resources. They’ll experience anxiety and a tendency to horde. 
  • Variability: When players feel high variation in availability, they experience similar emotions. 
  • Abundance: However if you give them abundance, they’ll momentarily feel a sense of freedom and can invest in non-scarcity driven behaviors. Note this is different from the extreme overabundance mentioned above when we discussed imbalance economies.
  • Hedonic adaptation: However, if they experience abundance for too long, your value chains grow slack. Players stop finding meaning in earlier nodes and just rely on their pooled resources. The joy of abundance returns to a baseline. 

The best games create an ebb and flow between scarcity and abundance within a narrowly controlled band of economic outcomes. A well balanced economy is a tool for driving rich player experiences. 

You can play with these much like playing notes or pacing on a music instrument. For example, in Cozy Grove, we made harvesting very reliable. Most sources were capped daily so they acted as slow trickle sources. And in general, we leaned towards abundance. Not always. Various events or quests would suck up resources from the player’s hoarded supplies so abundance wasn’t 100% reliable. This helped combat hedonic adaptation. The result is a very low stress, cozy economic game pacing. 

Note: Mimicking sink power by increasing sink magnitude

In a fixed length game, you can always approximate a higher power sink with a large magnitude low power sink. 

For example, I have a game that lasts 10 turns. It has a trickle source of gold that produces 5 gold per turn. 

  • Option A: I could pair the trickle source with a repeated gold sink that lets me buy 1 victory for every 10 gold. By the end of the game I’ll be able to purchase 5 victory points with no left over gold. 
  • Option B: However, I could also create a single fixed sink that lets me purchase 5 victory points for 50 gold. I’ve taken a simple fixed sink and just increased the magnitude enough that it sops up my gold income. 

On the surface, these look like the same end result. But they aren’t the same experience. 

  • Large sinks can feel grindy since players have to put in a lot of effort before they can spend. There’s a music-like pacing to how players interact with economies. Beware of large gaps where players lose track of the tune. 
  • If you end up changing the length of your game, you need to immediately go back in and rebalance all your sinks (or sources if you want to approach it from earlier in the chain.) In general, perfectly pairing sources and sinks of the same power that are balanced only by magnitude adds brittleness to your economic architecture. 
  • In a long-term ongoing game, you cannot mimic a higher power sink with a much higher magnitude sink. In the long run, a higher power source will always swamp a lower power, yet high magnitude sink. Just the way the math works (feel free to graph it!) 

In general, I try to avoid replacing sink power with sink magnitude. It is a bad habit to get into. 

Issue: Content treadmills

Long term, heavy use of capped sources and sinks lead to content treadmills. A content treadmill is when you need repeated injections of new content to keep your Game-as-a-service (GaaS) running. 

From an economic perspective, In order to extend the game, you need to add more sources and more matching sinks. Each of these requires a fixed amount of content. It can be better to invest in repeatable sinks. 

Issue: Marginal value erodes over time with repeated actions

Even trickle sources with strong sinks can wear out. Imagine you get one apple a day and you eat one apple a day. Trickle source, repeatable sink. What is the value of the apple to the player on the second day? 

In a simple model, the player has zero memory for the previous day. So they should look at the apple on the second day, realize they are hungry and be absolutely delighted to get a new apple. 

Burnout: In practice, each new apple provides a decreasing psychological benefit. Players slowly get bored with yet another apple. Repetition matters in experiential goods. More of the same, even though it provides the same functional benefit will provide less novelty or mastery benefit. 

Leverage: High leverage content are actions within your value chain that can be repeated many times without burnout. Most actions can only be repeated a small number of times before players get bored. The term ‘leverage’ comes from content that results in high ratio of gameplay relative to the cost of producing that content. 

For example, the classic leverage on exploiting a weak point in a Nintendo boss is ‘three’. The first time you learn their weakness. The second time you practice exploiting the weakness. And the last time you demonstrate your mastery. But that action starts to get boring if you are asked to repeat it the fourth time because it is usually just rote pattern execution. 

For more information on how to build high leverage content architectures see Designing Game Content Architectures

Solution – content recharging: The good news is that humans forget. An apple every single day might be low value. But if you let people forget about apples, and then give them an apple two months from now, that apple might again have high value. You can recharge content and regain some degree of leverage at the rate it takes for players to forget about that content. 

CHAPTER 3 – ARCHITECTURE OF MULTIPLE VALUE CHAINS

Most games have multiple value chains. If you were to lay out all the value chains on a single piece of paper, you’d find that certain nodes are present in multiple chains. This creates a crisscrossing spaghetti of resource flows that is the complete value network for your game.  

It is helpful to organize this ball of spaghetti in a fashion that is easy to understand and manipulate. Patterns for organizing your value network are your value architecture

There are an infinite number of value architectures out there. But we want to focus on sorting our chains in ways that best satisfy the following goals

  • Independence: Each individual chain is easy to independently balance so that it doesn’t accidentally unbalance other chains. 
  • Modularity: In GaaS, you want the option to easily retire old chains and add new ones as the game ages. 

The most common architecture: Parallel value chains 

The safest structure is to keep your value chains parallel to one another so they don’t overlap. Each set of action nodes is served by a set of unique resources and the player doesn’t need to make trade off between each chain. You can still feed multiple chains into the same motivational anchor as long as the anchor is multi-dimensional enough to be better satisfying by a little variety. 

Benefits

This mostly satisfies our goals

  • This lets you balance each chain in isolation. 
  • It is easy to add a new value chain for an event and then remove it when the event is over. Or if there’s a piece content that players are burning out on, we can retire it without upsetting the balance of the rest of the game

Issues

  • Lots of bespoke resources. Each value chain needs its own resources that are not used in other value chains. As parallel chains multiply, so do resources and you’ll need a reasonable inventory system to track them all. This can add a lot of cognitive load for new players. 
  • Fewer emergent interactions between systems: Since economic systems are isolated, you get fewer ‘interesting’ feedback loops. This is an intended outcome of the architecture, but worth acknowledging what you give up by adopting it. 

Most long term GaaS evolve towards some flavor of architecture that contains multiple parallel value chains. It shows up again and again in various MMOs and F2P games. Players may not enjoy the explosion of currencies and resources that result, but they serve a real architectural need. 

Architecture for applying buffs

Chain B creates a buff that enhances the efficiency of the gathering action in Chain A

A very common architectural structure folks build with value chains is to create a buff or boost that increases the efficiency or effectiveness of some other node in a different action chain. 

In Jesse Schell’s terminology, these often take the form of ‘virtual skill’. A player purchases a +10 sword of smiting that boosts the amount of damage they can do to an enemy, thus allowing them to kill it faster. 

Efficiency anchor: New designers often think that this virtual skill primarily serves a skill mastery anchor, like getting better at fighting in real life. 

In practice, it serves as an efficiency motivation. (Or a power fantasy. The exact anchor depends in large part upon theming and audience) It has little to do with player learning and everything to do with a number being modified. The action in the economic node becomes cheaper to perform. 

There are lots of possible efficiency boosts you can trivially build into your value chains. Basically any form of cost (time, money, resources, complexity) can be reduced by a boost. 

Adding sinks to boosts: And you can add additional sinks into the Apply Boost node. For example, you might get a spell that increases damage output. But it requires mana to cast. Remember, every node is an opportunity to add another sink if you need one. 

This is a very flexible and useful pattern that once you understand it, you’ll start seeing it everywhere. 

Issue: Multiple undifferentiated inputs to node

Sometimes however, you have to cross value chains. There are helpful and unhelpful ways of doing this. 

Consider the following unhelpful scenario that is unfortunately baked into most RPG systems. Here we have multiple nodes producing an undifferentiated resource. 

In this example, we have two value chains that merge into one. 

  • Value chain A: You can spend time and health killing Monster A
  • Value chain B: Or you spend those same resources killing Monster B. 
  • In both cases, you get XP that you spend on leveling up. The value chain continues on after that, but we’ll just look at this snippet of the whole. 

We see two things happen in this design pattern when a long term player groks the full value chain topology

  • First they realize they can make a choice. They can invest their time in killing either monster A or monster B. 
  • Next they realize that if both monster A and monster B are plentiful, their time is always limited. So for efficient play, they should focus on killing the monster that gives the most XP for time spent. Let’s assume that’s Monster A. 
  • As the player gains expertise, they’ll start to completely ignore Monster B. Even though it has book value, the marginal value comparison means that it is in practice valueless to the player. 

This pattern has major implications on your economic design. In MMOs you may create 100s of enemies. Or hundreds of raids or quests. Yet, players will insist on playing only one or two. All that content you spend so much time and money developing is essentially wasted. 

This structure is very difficult to balance since players only care if the two sources are perfectly equal. And it never is. Even in cases of mathematical equality, there are cultural, habitual or aesthetic factors that cause players to prefer one path over another. This architectural decision ends up invalidating big swathes of content. 

Solutions

  • Cap each source: If there are a limited number of times you can exercise Source A and also a cap on Source B and you need to to engage with both in order to satisfy the subsequent node. This is the most common answer for single player games. Here you have a fixed budget of content and can plan out exactly how much players should consume before they unlock the next elements. 
  • Multiple currencies: For more complex economies, it can be far more robust to use multiple differentiated input resources. The next section goes into more detail. 

Pattern: Multiple differentiated inputs to node

Now let’s consider an alternative topology

Same as before you spend time and health killing Monster A. But this time, you get a unique resource, horns. And monster B gives gems. And in order to level up, you need both Horns AND Gems. 

This setup has a very different set of player choices

  • The player must engage with both Monster A and Monster B to level up. If they only kill Monster A, they’ll lack Gems. If they only kill Monster B, they’ll lack Horns. 
  • The level up node creates a strong pull on the subsequent action nodes, giving these actions clear value.  

Players can choose the order that they engage with Monster A or Monster B, but they cannot ignore them. If there’s substantial content associated with those earlier nodes, you guarantee that it will be seen as valuable and that players are incentivized to exercise it. 

Pattern: Overflow from one chain to another

Suppose you want a player to pursue one value chain for a while and then switch over to a different value chain later in the game’s progression. 

In this example

  • Killing a monster gives gems
  • Players can spend gems to level up.
  • However leveling up is capped at level 10
  • Once players finish leveling, they can pour excess gems into crafting decorations. 

This overflow pattern is useful when you have fixed sinks. You set up cascading pools so that when one is filled, the excess can flow into others. 

This can be a useful pattern as well if your game is serving multiple player motivations. Which is almost always the case since any sufficiently large player population will contain multiple playstyles driven by multiple motivation. 

  • Say you have some players who love to decorate and others who like to progress. 
  • You want both to keep performing the core loop of killing monsters. 
  • So you use this structure to pull gems from the core activity, but then give them a choice on how they want to spend their hard won resources. Each path is anchored on a different motivation. 

Pattern: Lock-and-key choices

You train a fixed number of key resources. Player makes a choice on how to spend those resources. In this case, we are mimicking a worker placement pattern and producing consumable buffs of different types.

Earlier we covered how a single currency can lead to choices where players pick the most efficient path and ignore the rest. In a large, loosely controlled economy, this can cause major balance issues. However, there are more controlled variants where the player’s choice of how to spend esources are the most interesting part of the game. 

The common elements of this pattern include

  • Key resource: There’s a capped source producing a resource. This is the metaphorical ‘key’ in a ‘lock-and-key’ node-resource pair. 
  • Lock node: Gated nodes are unlocked with key resources. 
  • Choice: There are always more available lock nodes than key resources, so players need to make clear choices about which option to invest in.
  • Opportunity costs: By selecting a node to invest in, you lose the option of gaining resources from the other lock nodes. 

Common examples of this

  • Worker placement: The player gains access to a very limited number of workers. Those workers may be assigned to limited jobs to produce other resources or buffs. Or other workers! Sometimes there is a cost to place the worker. Or a cost to remove the worker. But critically, there are never enough workers to fill all the possible production stations so choices must be made. 
  • Skill trees: The player gains access to a very limited number of skill points. Those points are assigned to unlock skills in a predictable skill tree. This creates both a buff for the player and opens up the chance to unlock future skills further down the tree. 

Designers have a lot of control using this value chain pattern. They can change the benefits of each lock node and balance them against one another. They can control how many choices are valid by altering the amount of key resources. Content is invalidated when players make a choice, but the amount and impact of that is up to the designer.

New designers often mistake multiple undifferentiated inputs as the serving the same role as lock-and-key choices, but once you know the structure of the value chains, you can see they are quite different.  Lock-and-key choices always ensure a strong pull (and a taut chain) while multiple undifferentiated inputs result in dangling chains that are left unexercised.

CHAPTER 4 – ENDOGENOUS VALUE NETWORKS

“According to the dictionary, one definition of endogenous is “caused by factors inside the organism or system.” Just so. A game’s structure creates its own meanings. The meaning grows out of the structure; it is caused by the structure; it is endogenous to the structure.”

Greg Costykian, “I Have No Words & I Must Design

This quote has stayed with me for almost two decades. Value chains are a method of formalizing this fundamental truth into a useful design tool. They start to get at the heart of how meaning is constructed within a game. 

Value modeled as value networks

“Meaning” and “Value”are vague terms that we need to define more clearly in order to design . Value chains model “value” in terms of common elements of a game (actions, resources) arranged in a network topology. This allows us to get far more explicit about what value we are designing into our game. We gain visible levers and knobs we can manipulate. 

Value networks are internally self-supporting

Most elements in a game have value due to their relative relationships with other elements in the game. 

  • If you take away the other elements earlier or later in the value chain, the game loses meaning
  • Change the balance or nature of the relationship between elements, the game loses its meaning. 

Games as artificial spaces

Most game value networks are artificial. They are arbitrary and cut off from reality. This artificial space is often called the ‘magic circle’ within which gameplay exists. This artificiality provides such creative freedom! We are building cartoon worlds that don’t need to mimic the difficult-to-work-with structures found in natural economies. 

For example, when designing a giraffe refuge in the natural world. 

  • Does anyone even want a giraffe refuge? How are you going to pay for it? 
  • Then designers need to take into account years of law, history, logistical issues associated with limited physical space, connections to adjacent spaces, and whether or not your neighbor is allergic to giraffes. 
  • There are an immensity of constraints and unexpected feedback loops that are impossible to fully capture in any simplified model. 

None of those rules apply in a game about building a giraffe refuge. 

  • We can set up artificial rules where giraffes are plentiful, land is plentiful and everyone loves giraffes. 
  • We can create grokkable linear value chains and eliminate undesired feedback loops. 
  • We can intentionally design an artificial world where it is easier to build playful giraffe-centric activities within. 

Ultimately a game’s magic circle is anchored in reality

The “magic circle” is the conceptual boundary where a player opts into the value structure inside a game. Players opt into the magic circle of a game by saying “You know what? I know this virtual stick isn’t real. But I’m going to play along and act as if it has meaning.” 

But in the end, we should never forget that the reason why the player participates in the game’s value network is because they are seeking real-world value. This is why every value chain ends with a motivational anchor. Personal needs fulfillment always pierces the boundary of the magical circle. 

  • Unmet player needs: Play is a seeking behavior. You have unmet needs, but you don’t know how to fill them. So you experiment in a safe fashion to understand your options. This last step is the definition of play. 
  • Game makes player promise: A new game makes a promise to the player, usually rooted in the meeting of some need. Diablo promises power and mastery. Animal Crossing promises a relaxing respite. World of Warcraft promises mastery and friendship. This is the hook that gets you sucked into a game. 
  • Onboarding: And there’s a grace period. Because the point of play is to wander about for a bit and figure out how to meet your needs. Even players know that need fulfillment can’t happen immediately. Mastery can take many hours. Social bonding can take weeks. Players need to build up the tools. They need to understand the path forward. So players willingly run through tutorials. They willingly follow the chain of quests. Onboarding runs on goodwill that their needs will be eventually met. This step is introducing players to the early stages of the value chain. 
  • Understanding the path towards: That goodwill runs out. As soon as the promise is made, a timer is ticking and the player is thinking in the back of their mind “How is this game going to fulfill its promise?”  The job of the game is to paint that path. And demonstrate real progression towards it. If the game doesn’t help the player understand how all this (expensive) playful activity will ultimately fulfill a key motivational drive, they will stop playing. The game must connect the dots. This is making the value chain visible to the player. 
  • Demonstrating need fulfillment: In as short a timeframe as possible, the game should provide player experiences that fulfill the needs as they were promised. This is the end anchor of the value chain. 

So all of our elaborate value scaffolding does need to serve the player’s needs in the end. Every cartoon, hyper-designed endogenous game system contains a connection to the real world. Because games are played by real humans with real human needs. 

CONCLUSION

Value chains should give you a strong framework for planning and balancing your game economy. You’ll be able pinpoint issues and communicate targeted balance fixes using a common language. The technique targets faucet-and-drain economy designs, but since this remains the dominant method used across most popular genres, you should be well equipped. 

Next steps

Game economy design is a much richer topic of technique and practice than I could possibly cover in this paper. Many modern designers find they devote years of their career to learning the nuances specific to their genre and their community’s needs. If you are interested further in this topic, I highly recommend the following: 

  • Breakdowns: Take one of your favorite games. Identify the individual value chains. Be sure to include the anchors! Make notes on the architectural elements such as branching or choice built into the chains. Ask yourself what you could have done differently to serve your unmet needs better. Also do this exercise with one of your least favorite games. 
  • Game jams: Very few large teams will give an unproven designer the responsibility to design an economy from scratch. However, many of the fundamentals can be practiced on smaller game jam-sized projects. Limit the number of length of your value chains. But try them out! Try out strange new architectures. Playtest! Balance these tiny games. The lessons you learn scale to larger projects. 

Open questions

There are also many further areas of investigation for those interested in extending value chains as a design tool. 

  • Trade: How do value chains map to more open economies with features like player-to-player trade? 
  • Visualization: Is there value in reconstituting value chains into a more traditional spaghetti diagram? Such visualization tools don’t yet exist. But you should be able to composite value chains together automatically and perhaps even summarize them. 
  • Ethics: Can we use economy design for good? The use of value anchors deliberately centers human needs as the primary driver of value. Yet the world is rife with reductive, selfish ideologies that flatten the richness of humanity to mere numbers (homo economicus, libertarianism, much of current crypto.) Economy design is an amoral tool. It requires ethics, compassion and a keen eye for spotting externalities in order to avoid causing immense systemic harm.

References

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