Monday, December 31, 2012

Understanding randomness in terms of mastery

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.

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?

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. 

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.


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


Psychology of near misses

Gambling addiction as a learning disability

Sunday, July 1, 2012

Building Tight Game Systems of Cause and Effect

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

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?
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?

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?
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? 
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?

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 


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?

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

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

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.


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,

Monday, June 18, 2012

Goodbye Realm of the Mad God

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.


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.


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:

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.

Thursday, May 17, 2012

Looking to hire unicorn programmer for Spry Fox

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

take care,
Danc and David

Sunday, May 6, 2012

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

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. 


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:
  • 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 16x16 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
    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.


    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,


    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

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

    Jeiel Aranal's Model Toy - Iteration 1

    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

    • 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. 

    Sunday, April 29, 2012

    Loops and Arcs

    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.


    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' 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. 


    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,

    Thursday, March 1, 2012

    Giving a talk at plague stricken GDC 2012 on sexy-sexy innovation

    It is that time of year when I bodysurf the sweating developer crowds in San Francisco and inevitably contract to some horrible nerd-specific viral infection. Current theory: Never touch the glasses.  The past three years have resulted in the entire week of GDC being a blur of  fever and fatigue-induced hallucinations interspersed with violently explosive sneezing fits.  Here's to GDC 2012: Reliving Twelve Monkeys for the fourth year in a row.

    Somewhere in the midst of all this, I'll be giving a talk on game design.  David, who somehow manages to thrive on the additional contact with humanity, is doubling down on two talks.  His immune system must be made of titanium.  Alternatively, I hear if you eat a school teacher at the first sign of illness, you double the effectiveness of Cold-Eeze.  No wonder there is a teacher shortage.  I blame GDC.

    Here's my plan.  I'm just going to stand on some stage, deep in a fog of over-the-counter drugs and say something, anything.  Last year, people looked like rhubarb-colored elephants.  I hope my mouth movements makes sense to the mysterious minds behind those enormous loxodontal ears.  I never watch the videos afterwards so I'm blissfully ignorant of the actual outcome.

    Realm of the Counter-Intuitive God (SOGS Postmortem)
    SPEAKER/S: David Edery (Spry Fox)
    Monday 11:15-12:15 Room 135, North Hall
    Social and Online Games Summit / 60-Minute Lecture
    Description: Realm of the Mad God is a web-based f2p MMO with a penchant for breaking rules. It’s a MMO bullet-hell-shooter… in Flash. It is based on open source art. It features permadeath (the ultimate in retention challenges)! And it just so happens to be surprisingly popular and very profitable. This lecture will review some of the unusual design and business choices we made and explore which worked, which didn’t, and why. Financial and other data will be shared (and not just the stuff that makes us look good).

    Create New Genres (and Stop Wasting Your Life in the Clone Factories)
    SPEAKER/S: Daniel Cook (Spry Fox)
    Tuesday 3:00-4:00 Room 135, North Hall
    Social and Online Games Summit / 60-Minute Lecture
    Description: Re-releasing old designs with pretty new graphics means me-too titles fighting off a crowd of similar products. This is the path to mediocrity. To become a master designer, you need to break past a slavish devotion of past forms and create vibrant, new experiences. This design talk covers practical techniques for reinventing game genres. The goal is the invention of a unique and highly differentiated customer value proposition that makes both strong business sense and is also deeply creatively fulfilling. We cover designing from the root, reducing design risk, and igniting original franchises. We also cover the pitfalls of design innovation including fending off shark-like fast followers and other cloners. The presentation covers personal examples from recent titles such as Steambirds, Realm of the Mad God, Triple Town and other innovative successes.

    How F2P Games Blur the Line Between Design and Business
    SPEAKER/S: Soren Johnson (Game Developer Magazine), Ben Cousins (ngmoco Sweden), Matthias Worch (LucasArts), Tom Chick (Quarter to Three) and David Edery (Spry Fox)
    Friday 4:00-5:00 Room 2003, West Hall, 2nd Fl
    60-Minute Panel
    The free-to-play movement is here to stay and will touch every corner of the games industry. However, the format blurs the line between game design and game business, so that business decisions will become increasingly indistinguishable from design decisions. Free-to-play content must be fun enough to attract and retain players but not so much fun that no one feels the need to spend some money. Managing this tension makes free-to-play design extremely difficult, especially for traditional game designers who are used to simply making the best game possible. Our panelists will discuss this transition and best practices for building free-to-play games with soul.

    See you there.