Tuesday, September 26, 2006

Complexity, Risk, and Challenge (WTP 3)

One of the powers of the Situation Model is to be able to accurately defined the concept of Challenge.

Really, it's fairly simple. First, let us divide the Situation into Domains, each named for the three actions that the player is capable of taking. So there is the Reason domain, the Ability domain, and the Comprehension domain.

But, before we can deal with Challenge itself, we need to define two other terms.

Let Risk be the relative potential for the player's actions in the Situation to produce negative consequences towards achieving the player's goal.

There are two degrees of Risk. One degree is the quality of the negative consequences. IE: how badly does it hurt the player to fail? The other degree is the quantity of the negative consequences. IE: how likely is the player to fail? How much of the Mapping Table is dedicated to paths that lead to failure rather than success? Or, put another way, how many Game Inputs are there that lead to success rather than failure?

Let us define the first degree to be Risk:Pain. The second degree will be Risk:Extent.

Risk, in either degree, is a fundamental quantity of a Situation. It is not affected by the player's skill set or body of accumulated wisdom. As such, it is not enough to be able to define what a Challenging Situation is. After all, what is hard when you first begin to play is not nearly as hard at the end.

Since the player's Reason, Ability, and Comprehension are all fixed by the Situation Model, only Knowledge and Experience can explain the changing nature of Challenge. So, now we introduce Complexity.

A Situation's Complexity is a descrition, for each domain as defined above, of the body of Knowledge and/or Experience needed to feed into the player's attributes to successfully deal with the Situation at hand. The more Knowledge and/or Experience, the greater the Complexity.

Complexity exists in each of the three domains, and it should be looked at separately for each.

Complexity in terms of Reason means that it requires pulling in disperate Knowledge in order to device a correct plan that will create the desired Result. The greater the body of Knowledge required to find a viable solution, the more Complex the task of using that Knowledge becomes.

However, reason has the honor of being the only domain fed by both Knowledge and Experience. So it gets a second pass. Reason must also estimate the likelihood of being able to execute the Player Input into Game Input, and the player's Experience informs this. However, greater Experience serves to decrease this form of Reason-based Complexity. Greater Experience means that the player is capable of doing more, providing more complex input, and doing so more accurately. Thus making more complex input easier for the player to achieve.

This also requires a degree of Risk analysis. The player must use the Knowledge of the Risks (which may not be complete, of course) to determine if the player's Experience is enough to be able to hit one of the correct solutions. Risk:Pain makes one wary of confronting the Situation at all (if the player has a choice), while Risk:Extent puts a stress on evaluating one's Experience for confonting the Situation.

Reason-based Complexity, therefore, has two degrees: Complexity:Reason:Knowledge and Complexity:Reason:Experience.

Ability-based Complexity is fed only by Experience. The decision of what to do has been made, and now it must be effected and transformed into Game Input. This Complexity represents the quantity of Experience one needs in order to properly manipulate the user interface to turn Player Input into Game Input. Or, in simpler terms, it represents how skilled a person must be at using the UI in order to command a particular Game Input. Thus, giving rise to Complexity:Ability.

Comprehension-based Complexity is fed only by prior Knowledge. Complexity in Comprehension means that it requires a large quantity of accumulated Knowledge to properly Comprehend a particular Result. The phrase "properly Comprehend", is intensionally nebulous.

Particularly of the Player Input that was used to produce this Result. From that, the player can actually reduce later Complexity (which we will discuss more later), by being able to apply previous victories in new Situations. Rather than later having to use Reason to cobble together some attempted solution, the player need only take an already proven solution and modify it for a new Situation. This proven solution, of course, is represented as Knowledge and/or Experience.

Which gives us Complexity:Comprehension.

So, what is Challenge? Well, quite simply, Challenge is any and all of these. A Situation is said to have a degree of Challenge when any of the 6 aforementioned degrees is involved to any significant extent.

Challenge therefore has 6 pimary degrees that a designer can theoretically adjust. Each of those degrees can be adjusted in a myriad of ways.

Risk of all forms provides the player with negative Results. The player does not (presumably) want negative results, so the player attempts to avoid them.

A high degree of Risk:Pain means that the player has a strong wish to avoid failure. It means that the player is going to be in a mental state of stress on the possibility of failure. Planning to avoid the pain is therefore very crucial; this degree plays towards the player's Reason and Comprehension, as well as Knowledge and Experience.

A high degree of Risk:Extent means that there are many pitfalls. The player must take extra care to provide the correct Game Input to select the right Result. This degree plays towards the player's Ability and Experience.

Complexity, of all forms, confounds the player. It makes the player's task harder by placing greater demands on the players attributes.

Complexity:Reason:Knowledge means that the player must use a great deal of accumulated Knowledge be successful in this Situation. Devising strategies is one example of this.

Complexity:Reason:Experience is all about stressing the player's accumulated skill at manipulating the user interface. The player must plan for his/her shortcomings if there are degrees of Experience that he/she is missing. The player may choose to play it safe rather than attempting a plan with a greater degree of Risk:Pain or Risk:Extent.

Complexity:Ability requires that the player be able to substantially manipulate the user interface. The player must have a great deal of prior experience in performing the actions that the player is being required to perform.

Complexity:Comprehension requires that the player gather large quantites of Knowledge from other Situations in order to be able to understand what is being presented by the game.

There are also 3 extra degrees of Challenge. And they are pretty obvious when you think about it. For a given Situation, there is a base "quantity" of Reason, Ability, and Comprehension that is required to be able to confront the Situation and/or learn from it. The more the Situation requires from the player in these domains, the more difficult the Situation (or later Situations in the case of Comprehension, due to lack of Knowledge and/or Experience) is. We will call these degrees of Challenge Difficulty:Reason, Difficulty:Ability, and Difficulty:Comprehension.

So, in total, there are 9 degrees of Challenge, each with a virtually infinite number of ways of being expressed to the player.

We will discuss how Challenge changes over the course of multiple Situations in later articles.

Wednesday, September 20, 2006

Rethinking Game Design

The original formulation of the Situation Model specified that the list of possible Game Inputs to Results for a Situation was the totality of the game's Design.

That's not the case. That listing is called something, but I want the term Game Design to mean something else. And here's why.

Chess is a game (and, when played on a computerized system, a videogame). Basically, Chess the game is just a list of rules. The rules state what the game board is, what the pieces are called and what they can do (not necessarily the look of the pieces), a few special-case rules (castling and en-passant), and what the victory conditions are.

Nowhere in that is any mention of Situation.

Actually, that's a lie: there's one thing I forgot to mention. The rules of Chess state what the initial arrangement of the board is. Or, in our terms, what the initial Situation is.

That's it. The rules of Chess give you a starting Situation and tell you how other Situations resolve from there. In short, the game rules of Chess define how to derive the Game Input and Results.

The Situation model is focused on what a particular player experiences when playing through a game. The player doesn't experience the rules so much as the situation and what can result from them. Now, with Chess, if you don't know the game rules, you can't be expected to perform well; the Knowledge of the game's mechanics is the minimum set of Knowledge needed to play. Then again, that Knowledge is, effectively, Player Input (as well as an understanding of what your opponent can do against you), which as previously stated was vital to the player having any real ability to play the game.

As a player plays through games of Chess, certain facts start becoming evident; Knowledge is gained that is not immediately obvious from the basic rules of Chess. For example, the Queen ought to be the most powerful piece, for she can capture in 8 directions at any range. But her value is only potential value in the beginning of the game. You can move a pawn out of the way such as to be able to deploy your Queen significantly, but then you used a move to do that. Your opponent may counter this.

A Queen stuck in a corner can be far less useful than a Bishop in a more viable region of the play field. In fact, most pieces lose significant power along the edges of the board; that hard barrier cuts off movement possibilities and hamstrings how a piece can move.

These kinds of "emergent Knowledge", where you start to understand something about the game that the rules create, are vital towards effectively being able to play the game. They are a form of Knowledge, and as such, are a part of the Situation Model.

So, in effect, we have a set of game rules that define how the game gets played, but the player doesn't really see that. Instead, the player simply has what the player believes are the possible inputs. The player's understanding of the game comes from being able to determine the emergent Knowledge that the game produces. What are good strategies, etc. Indeed, the player may even ferrit out the game rules themselves, if they are not obvious from the player inputs.

The game rules don't say explicitly that "Queens in corners are bad" or that "Pieces can be pinned in place by other attacking pieces" or that "You can potentially protect a piece from an attack by a valuable piece by moving a piece so that it threatens the square that your piece resides in, thus promising the opponent that he/she will lose that valuable piece". However, each of these is fundamentally built into the rules of the game; they exist because certain rules create these specific effects.

What happens if the Chess board were wrap-around lengthwise? All kinds of things change from that. What if Knights couldn't hop over other pieces? Other things change because of that. And so forth.

So, what is a game's Design? Simple: it is the set of rules, the game's initial Situation, and potentially state information (about what has happened before, etc) that defines the mechanism for generating the Game Input to Result mapping for a Situation. The Game Design fully defines how Situations move from one to another, thus the Game Design is what make the Interactive Loop. Game Design is what the game designer creates; the rest (particular Situations, etc) is built implicitly based on the Game Design.

If that's Game Design, then what is the set of Game Input to Result mapping in a Situation? Well, to be technically accurate, the best way to model it in the Situation Model is to have the Game Input pass through the Game Design and then a Result is output. However, I find it useful to consider this to be a potentially enumerable mapping table rather than some black-box process. Therefore, we will simply call it the Game Input to Result Mapping Table.

Tuesday, September 12, 2006

Knowledge Pathologies 1 (WTP 2)

Given our current model of game design, we now have a way to express certain disfunctions in games.

By disfunction, or pathology, I mean a place where the player's natural progression from one Situation to another is interrupted in a way that is either not called for by the game's design or could be considered significantly annoying to a player.

Given the Situation Model, we have an understanding that Knowledge is a fundamental part of the Interactive Loop. Indeed, it is Knowledge (and Experience) that makes this loop actually function, where each iteration builds upon the last. As such, when a pathology arises with regard to Knowledge, it can break the Interactive Loop.

Though the Situation Model is focused far more on the player than the game design, we will look at these pathologies from the point of view of the game designer; what the designer intends and/or expects.

In this article, I will examine pathologies surrounding Knowledge generation.

In the Comprehension phase of the Interactive Loop, the game's Result is processed by Comprehension, informed by prior Knowledge, to generate new Knowledge. A pathology can result in this phase from one of the following ways.

Vital information, information necessary for the player's later progression, may not be available. That is, the game's design did not actually provide a Result to the player that any form of Comprehension and prior Knowledge could transform into the appropriate Knowledge. This is more than a mere pathology: this is a clearly broken game.

Now, a game exhibiting this pathology isn't always technically unprogressable. If the game is progressible, what must happen is that the Reason phase needs to find a way to generate this Knowledge by taking a bold exploration of the known Player Inputs. In essence, in order to proceed, the player must try everything until something works. And the Knowledge of how to proceed is generated by inference; when progression occurs, the player understands that this was the correct answer.

A game example of this kind of pathology would be when a player is presented with a standard 10-key number pad and a locked door. The door is otherwise impassable, and the player knows that it requires an 8-digit password to proceed. The key piece of Knowledge that is missing is the password itself. If the game does not provide any Result where this password appears, then this pathology results. The player can proceed by trying all possible codes until one works.

Let us call this pathology Unfairness. I use this term because the game is not playing fair with the player; it has failed to provide certain Knowledge that it later expects the player to possess.

This pathology is often confused with a second Knowledge pathology. This one occurs when the game provides a Result that is intended by the designer to produce certain Knowledge, but for the player fails to do so. This can happen in one of two ways. In one way, it happens because the player simply isn't good enough at Comprehension to be able to follow the game's logical consequence from Result to Knowledge. The other is that the player doesn't have a certain piece of Knowledge to inform their Comprehension process in order to follow the logical consequence from Result to Knowledge.

The latter form is one of two things. It is either an expression of a prior Knowledge pathology, or the player has simply not yet encountered the Situation that is intended to provide the player with the specific Knowledge. So we will ignore this one for now, and focus on a lack of Comprehension.

This form of pathology is simply that the game expects too much out of this particular player. Perhaps the player is unsuited to playing this game. It is important to note, however, that it is easy to confused this pathology with Unfairness; in order for this one to apply, there must be some logical consequence that leads from Result to Knowledge. Even then, one might suggest that it is Unfair to expect too much in the way of Comprehension from a player; however, I consider it perfectly reasonable to have a game require a high degree of Comprehension from a player.

In general, a lack of Comprehension isn't really a pathology with the game; it's a problem with the player. The player is simply not suited to this particular game. Given the number-pad example, it could be the case that the 8-digit password was scattered and hidden about the game world, and the player simply didn't have the Comprehension to pick up on it.

To the player, there is no difference between these two forms of Unfairness. A wise player might use a FAQ to determine how the Knowledge was supposed to be deduced, and then judge whether the game was Unfair or if the player simply didn't have sufficient Comprehension to put it together. But, more often than not, the player will simply put it down to the game itself being the problem and leave it at that.

All of the above pathologies are what I call Negative Knowledge pathologies. This means they fundamentally arise from the player not actually Knowing something. But there is another kind of Comprehension Knowledge pathology that is equally, if not more, important: False Knowledge.

That is, the game provides a Result, and the Knowledge produced from Comprehending this result is inaccurate to what the game design actually is. The player has gained some Knowledge, but that False Knowledge will impede or degrade the player's progress.

The reasons that False Knowledge is often worse than Negative Knowledge are legion. First, it is much easier for the game designer to accidentally tell the player the wrong thing than it is to tell the player nothing about something. Second, it is often more difficult for the player to unlearn something than it was to learn it in the first place, particularly if the False Knowledge was bound in some way to real Knowledge. Third, a lack of Knowledge generally breeds failure; False Knowledge can more often perpetuate the cycle, breeding new False Knowledge from later iterations of the Interactive Loop.

False Knowledge can be very sneaky, and it can be very easy to create False Knowledge by accident. For example, if you have a game where performing an action will lead to a reward, but only rarely. And additionally, there is no other indication that the action will succeed or fail on a particular location without trying it, and there are huge numbers of places to try in the game. This is a strong setup for False Knowledge generation.

There is a very good chance that the player will learn that taking that action is totally meaningless. The player might try it in a few places, but since it is rare, it will more than likely fail in all cases. Thus, the player will learn that this action never does anything. This False Knowledge would become pathological if said action was later required in order to progress.

False Knowledge, much like Negative Knowledge, happens either from poor Comprehension, a Result that more reasonably leads to the False Knowledge than the real Knowledge, or from prior Knowledge that helps lead to the False Knowledge.

This kind of pathology is called Deception; the game is actively Deceiving the player, whether intentionally or not.

Deception is particularly dangerous because of the ease at which it can happen. A game designer must take special care when designing a game, so that False Knowledge is not readily generated. And the more the developer focuses on creating difficulty in the Comprehension stage of Situations, the more likely it is that False Knowledge will be generated.

Despite the dangers of Deception, it can often be corrected fairly easily. Given the above example, the most reasonable way to correct this is to make sure that the player encounters those places where the action will result in a positive outcome early on in the game. Or to outright tell the player and point the player in the direction of such a place where the action will work.

Now, if part of the difficulty (and potentially enjoyment) in the game is to actually discover this Knowledge on one's own, then the game still has the responsibility of providing appropriate clues as to the viability of this action. The action should have some realistic backing, such that it would seem viable just from the player's understanding of the workings of the real world. There can also be certain subtle indicators that one might want to try using the ability in certain locations.

There are a myriad number of ways of suggesting the viability of the action; the key thing to remember is that there should be something that lets the player know that it will work. And the most effective way is for the player to experience it working him/her self. All the game needs to do is hint the player to a location where it will work.

Monday, September 11, 2006

Theory of Design

My current Theory of Design stands as the Situation Model. A more in-depth discussion of it is available here: , where it was first introduced.

This model is useful for classifying how well a game works and, as it is further analysed, as a means towards evaluating design elements.

What's the Point? (WTP 1)

So, I've created a fairly interesting model of a game (the Situation Model), one that can be applied to games on virtually any scale. It can be applied to virtually any kind of game, and probably some things you don't think of as games.

(Let's assume you buy all of that, as I haven't actually proven or even demonstrated it yet)

Yay for me. So I can apply them to games. Big deal, right?

Scientific models aren't useful in and of themselves. Their usefulness is when they can actually predict something of value (or of potential value. Or even just something).

As such, the key to how truly valuable this model is will be in what it can actually tell us about a game. Can it explain why one game works and another one doesn't? Can it actually tell us what effective games and non-effective games are?

To me, the most important thing to get out of any Theory of Design is a way to objectively assess a game's design. To be able to say where a game is functional or not just by an examination of its structure. And, because functionality is person-specific, such a theory needs to be able to at least potentially address what kind of people will find a design functional and what kind won't.

That's part of the reason I'm not going to (yet) attempt to justify the theory by showing how it applies to actual games. Instead, I'm going to examine the theory itself, explain certain pathologies and functionalities that it indicates are possible, and then show that there are games that exhibit these pathologies and/or functionalities. I will, in essence, examine the theory in a vacuum and then show that it does predict certain things about games.

Maybe after that, I'll see about showing how well it applies to games in general.

The Situation Model

The Situation Model is defined via the following diagram:

The Situation Model

The diagram describes a game Situation, as defined in the provided link. The topmost region, colored in light blue, represents the player's accumulated Wisdom. The second region, in light green, defines the player's Attributes. The third region represents the player's mental workspace; what the player intends to communicate to the game*. The fourth region represents the game itself: the rules by which the game maps the Input the player actually provides to the particular Result that it offers.

A Situtation is a looping construct, in essence. It starts with the player becoming aware of a Situation, likely from a prior iteration of the loop. Then, the player uses Reason coupled with Knowledge and Experience to formulate a plan. This plan takes the form of a series of Inputs to the game that the player intends to send: the Player Input.

The player then attempts to deliver this Player Input to the game. The player invokes Ability, which is informed by Experience. This generates the Input that the game sees, the Game Input.

The game's Mapping Table is the set of all possible Game Inputs and their mapping to a game output. This Table need not be fully deterministic; randomness or chaos may enter into the outcome. So, via some means, the Game Input is mapped to the Result.

The player then processes the Result using Comprehension informed by Knowledge to generate new Knowledge and Experience. Not every situation will generate Knowledge or Experience, nor is there any guarentee as to whether the player actually understood all of the Knowledge or Experience that the game expected to deliver with a particular Result.

As a side note that will be important later on, the player's body of Knowledge includes some knowledge of what inputs the game can take at this time. However, this Knowledge may be incomplete or inaccurate.

Let the set Ig be the set of all valid Game Inputs. Let the set Ik be the set of inputs that the player has Knowledge of. There is no formal relationship between Ig and Ik, though it is usually a good idea if the there is some overlap between the two sets.

* The information in this region may be expanded upon later as the model is improved.

Situation Model Terms

Situation: This is the particular set of Game Input that the game allows and the Results that the game will provide for those Inputs at the current state of the game. It involves the player confronting a particular state of the game and deciding how to interact with that state so as to produce a Result that the player considers favorable.

The Situation Model

Player Accumulated Wisdom

A player, when approaching a Situation, brings with him/her the following accumulated attributes:

Knwoledge: This represents the accumulated information that the player has about the game up until this point. This represents, among other things, the player's understanding of the current Situation, the player's understanding of what Input the player may provide to the game, and the player's knowledge of what happened previously. Knowledge may be obtained through the application of Comprehension (and previous Knowledge) to the Result of a prior Situation. It may also be obtained from extra-game sources (online FAQ, hints from a friend, etc).

Experience: This represents the accumulated ability of the player to manipulate the game's user interface. Experience may be acquired through the application of Comprehension and Knowledge to the Result of a prior Situation. Experience may be acquired through other extra-game sources (playing a similar game with a similar user interface, etc).

Fundamental Player Attributes

The player, in addition to accumulated wisdom, brings with him/her certain fundamental attributes of that player. These attributes do not change from Situation to Situation; they represent basic constructs of the particular person playing the game. As part of the Situation Model, these attributes fully define a specific person playing the game. They also do not change over time.

Reason: This attribute is used to process Knowledge and Experience to produce the Player Input that the player wishes to use in a particular Situation.

Ability: This attribute represents the player's skills at manual dexterity. This attribute is used, when coupled with the player's accumulated Experience, to transform Player Input into Game Input.

Comprehension: This attribute represents the player's skill at understanding what he/she is being presented with. It is used to generate new Knowledge and Experience, but previous Knowledge informs this process as well.

Player's Mind

These constructs take place entirely within the player's mind.

Player Input: This is the specific sequence of commands that the player would like to tell the game in order to meet the Situation and achieve a Result. Player Input is generated by applying the player's Reason to the player's body of Knowledge and Experience. Player Input is then entered into the game by applying the player's Ability to the player's body of Experience.

The Game

Game Input: This is the valid input that the game receives from the player. The player uses his/her Ability, informed by Experience, and the player's Player Input to generate this information. The game outputs a Result based on this input and the specifics of its game design.

Result: This is the output from the game's processing of the Game Input. It is based on the Game Input and the game's design. It can be transformed into the player's Knowledge and Experience when the player's Comprehension is applied to the Result, informed by the player's current Knowledge.

Mapping Table: For a specific situation, the Mapping Table is the set of all possible mappings from Game Input to Results in this Situation. These mappings may not be 1:1, and there can be random or chaotic elements in the Design, such that it makes it difficult for the player to predict whether a particular Game Input will produce a particular Result.

Saturday, September 09, 2006

Situation-Focused Model

Forget everything you've read in previous Game Design discussions on this Blog; this superceeds them all.


When a player is playing a game, the player will be confronted with a Situation. A Situation is a moment of the player's playthrough of a game that allows the player to take action, or to take inaction.

When the player approaches any particular Situation in a game, the player brings with him any Knowledge and Experience that the player has acquired, either from prior Situations in the game or from external sources (previous games, life experiences, an online FAQ, etc).

Knowledge represents all information acquired by the player up to this point. Experience, alternatively, represents the player's ability to manipulate the user-interface to carry out the player's wishes. A player can have acquired all kinds of Knowledge about a game from reading a FAQ, but this is separate and distinct from being able to correctly interface with the game.

Situations may be of any scope. Being confronted by a creature in a game may be a situation. Confronting a level in that same game is also a situation. Situations may have sub-situations within them. The primary distinguishing factor that makes something a Situation is that the player must make a choice that bears some significance. If the player makes a wrong choice, then the player may not acquire some item, may not see a particular path, or may be killed off. Or maybe the player's avatar is put in some kind of peril.

Basically, a Situation happens when there is something at stake. Or when the player believes that something may be at stake.

An important part of the body of the player's Knowledge is the Knowledge of what the Situation is. What the player is being confronted with. Note that this may be different from what the Situation is to the game. The player may not have recognized some facet of the Situation. The player may also be considering things which are not important to the Situation. This will be covered in a later section.


For any given Situation, the game may accept some number of Inputs. These are the instructions that the player uses to act upon the game. An Input need not be a single fundamental control (pressing up on a game pad); it may be a sequence of commands (press up, then left; press down-to-forward and Punch; etc).

The player's Knowledge plays a key role with regard to Inputs. The game, for a given Situation, provies a certain set of Inputs that we will call Iall. However, the player only has Knowledge of a set of Inputs called Ik. There is no formal relationship between the two sets. Usually, the player knows some subset of Iall, and that would be a requirement for the player being able to effectively manipulate the game. However, the player may not know that certain Inputs exist, or the player may believe that there are other Inputs besides the ones that the game provides.

A player's Experience is also important. Givne Ik, there is a subset of this Ie that the player has sufficient Experience to be able to perform correctly.

Reason and Ability

When confronted with a Situation, the player must decide what Input to enter. The player applies their Reason to their Knowledge and Experience; the result is the Input that the player wants to perform. Knowledge informs this process, both in terms of what the player expects to result from that Input as well as a weighing of other known factors. Experience informs this process primarily by the player selecting either Inputs that the player can perform, or knowing that there will be some uncertainty in the outcome and potentially planning for it.

Reason is not a body of information; it is a fundamental attribute of the person playing the game. It is the mechanism by which the player uses Knowledge and Experience to make a decision about which Input to use. The game itself has little-to-no influence upon the player's Reason. For the purposes of this model, we will assume that the player's Reason does not change over time.

Once the player has decided what Input to provide the game, the player must now attempt to provide this Input. This is governed by what the Input is, the Experience of the player, the game's set Iall (which determines if the Input is valid in this Situation), and the player's Ability. These factors influence whether the correctly provides the Input and what the game will do with that Input.

Ability, like Reason, will be considered a fundamental part of the player's skill set. It will not change over time.

Game Input is the Input that is actually provided to the game. This may differ slightly or substantially from the input that the player intended to provide, based on the player's Ability and accumulated Experience.

Result and Game Design

The game will process the Input that was given to it and produce a Result. This Result takes many forms, depending on the game.

The game decides what Result to give for a given Input based on its own internal rules; we will call these rules the game's Design. The game may have its own internal storage, such that it retains information for later. As such, not everything that changes in the game's state from a given Input will be provided directly to the player as a Result. It may be provided later, once other Input is given. However, unless some Result, either from this Situation or from a later one, actually uses this internal storage, then it is completely invisible to the player and therefore unimportant.

We will only consider that which goes in and that which goes out; what happens in the mean time is important only to the extent that it affects a result.


The game's Result is the total output of the game. However, the player may not fully understand what this total output is, much as the player may not be fully aware of what the possible Inputs are and so forth.

When the game provides a Result, the player must then apply their Knowledge to their ability to Comprehend. This process results in the possible acquisition of Knowledge and/or Experience. Among the Knowledge that may be acquired is the Knowledge of being confronted with a new Situation. Because Comprehension was involved, this new Knowledge may be imperfect; the game's Result may have intended to communicate some information, but that information may not have been properly Comprehended.

Much like Reason and Ability, Comprehension does not change with time. It is a fundamental skill of a particular player.

Interactive Loop

The Interactive Loop is the sequence of progression from one Situation to the next, where the Knowledge and Experience provided by one or more previous situations is required to get past a later Situation.

Friday, September 08, 2006

The Breaking of DAR/RAI Theory

It's only fair to point out that the last main game design article, where I put forth the DAR/RAI theory, was composed, for the most part, as I typed. That is, I started writing an article looking at how DAR interacted with the player, and I wasn't entirely sure where it was going before I started typing.

This is par for the course for this blog, seing as how that's how DAR came to be. To be honest, when I started writing on game design, I had intended to put forth an entirely different theory of design, one based on GNS theory and other ideals from table-top role-playing game design. I thought they were nice theories, and they (when appropriately modified) do have some applicability to videogame design.

But then I thought up DAR. And RAI came out of that. Now, I have a whole bunch of ideas swimming around in my head that no modified GNS theory could touch, ideas and analyses that are useful to explaining why some games work and some don't. In essence, my modified GNS theory would have explained gaming from a top-down approach, while DAR theory operates from the lowest level of game design and scales up.

Because I thought up DAR and RAI basically on the fly, I'm still revising them fairly substantially. Take DAR, for example.

At first, it was designed to model both what the game presents and what the player does. The player's decision, the player's action, and the game's response. As I started on RAI theory, however, I realized that this was all wrong. It's actually the game's decision, and the game's available action(s). The game presents one or more DARs to the player, and the player uses Reason to select among them. In a particular DAR, the player uses Ability to act and receive the response. And, as a correction to the last article, the player uses comprehension (thus turning RAI into RAC; it sounds better to me) to take that response and turn it into knowledge.

I'm still somewhat concerned by one, seemingly glaring, omission in the DARRAC theory: the fact that Ability and Action have no third component. Knowledge feeds Reason, which leads to a Decision. And Response feeds Comprehension, leading to Knowledge. But Ability stands alone, fed only by the decision and never changes from one iteration of the same DAR to the next. There is no knowledge analog that represents the possibility for the player to improve in this domain.

In fact, I've really glossed over Action in discussions about the DAR model. This mostly stems from the fact that I'm fairly uncertain of it and its future. By all rights, making a decision and acting upon it are two separate things. Forming a plan and executing it. However, I don't really deal with it much. The Interactive Loop is based on decision and response; there just happens to be this middle section where you have to act on a decision in order to get the response.

The main problem is that the simple DAR model doesn't allow for the action to fail. See, you don't decide to fall into a pit in SMB; that certainly wouldn't be anything involving Reason. But it happens. It happens because you made an error in your actions; you jumped at the wrong time and couldn't get over the gap.

Then again, that last line can be reframed into a decision, rather than an action. The problem is that you decided to jump at the wrong time. And the knowledge of that failure would, when properly Comprehended, results in the player improving over time.

When phrased like that, it sounds like you don't need action. Yet action is clearly a part of game design. Any number of games have failed totally because of poor user interface. Given the above, just because you know when to jump doesn't mean you can do it all the time. That makes it seem like there is something between Decision and Response.

Not only that, I've come to realize that the DAR/RAC model is wrong more fundamentally. Elements are the wrong way to look at a game; it should be looked at from the perspective of a player playing the game. And a player doesn't get a DAR, a player is confronted with many DARs from which the player uses Reason to choose.

In short, I'm revamping DARRAC theory somewhat. The new model will become known as the Situation model, and will be forthcoming.

Navigating This Blog

The best way, particularly if you're new, to catch up on this blog is as follows.

First, take a look at the introduction article. It will explain the topic and goal of this blog.

From there, you should start reading the commentaries on game design theories. These articles are found under the "Game Design" Category. These articles are cronologically arranged, first at the top, last at the bottom. They give a snapshot of my thinking in terms of these theories. Regardless of what alterations/improvements/abandonments that happen to theories after these posts are made, these particular blog entires will not change to protect the innocent/guilty/idiotic.

If you want to see my current theories on game design, the "Nomenclature" and "Theory" sections are for you. They will not justify how these theories were arrived at (that's what "Game Design" is for); they exist to explain the coherant theories of design. There will be one page under that category (entitled "Theory of Design") that will never be removed (if you want to link to my current theories, that's the page to link to). Other pages in this category can be added, removed, drastically modified, or whatever, as needed.

The main theory page will also have it's own category (the only article under that category) entitled, "Current Theory". It will link to the appropriate theory pages that are current.

Reviews of games, for the purpose of justifying game design or just because I think they're interesting design-wise, will be filed under the "Reviews" Category.

Tuesday, September 05, 2006

The Player, the DAR Model, and the Interactive Loop

We now have a way to define the interaction between the player and the game: The DAR Model. The next step is to begin codifying how the player interacts with the DAR elements.

Let us visualize the DAR model as follows:


Decisions lead to actions, which lead to the response.

When the player makes a decision, that decision happens because of some information. The player applies their own reason to various knowledge that they have in order to arrive at a particular decision. Thus, the combination of knowledge and the player's reason combine to create the decision, as shown here:


Given a specific decision, the player must now carry out that decision; they must take action. Action is informed by the decision of course, but it is also informed by the player's ability to effectively communicate that decision to the game. You can know all you can about how to pass though a Mario level, but that doesn't mean that you can do it:


Only the game's design informs a response. For now, let us assume that this is a black box: the player does something, and a response happens. It is now upon the player to interpret this response.

The important question, and the one that is vital to understanding the interactive loop, is this: what is gained by this interpretation for the player? Simply put: knowledge. It could be, depending on the element in question, something as simple as updating the position of the other entities in the world, or it could be some valuable insight on what happens when two entities interact in some way.

Knowledge is an important reward for a reason that you may have already guessed. But first, let's diagram this:

Full DAR//RAI Model

What we have is a feedback loop. Knowledge both fuels the system and is its output. Note that the response need not be only knowledge; some elements can provide other responses. But knowledge is what makes this feedback loop work. In essence, the player started this element knowing some things (potentially nothing), applied his reason to them, took action, and discovered something.

Let us define the Interactive Loop element as a game element that provides significant knowledge that will be of value to how the player reasons later on, possibly to the point of being vital to the player's later success. The idea is that the significant knowledge feeds into either this same element again (as when one is playing a game like Asteroids or Defender) or into a new element that the player may or may not have encountered previously.

Even so, it is very important to note something about the diagram. The three red boxes represent things beyond the game designer's control. A game designer can determine what decisions are possible (and not possible), what actions are available, and what the response to these will be. A designer cannot determine what the player's reason is, what the player's ability is, and what they interpret from that response. As such, the game designer can never be 100% assurred that the player has received the appropriate knowledge that a particular response is intended to evoke, because the player may have misinterpreted that knowledge.

So, what do we have now? We have the DAR model, which represents what the game defines. Now, we have the RAI model, which respresents how the player interacts and responds to the game. And we have a cross-connecting piece between them, which is the player's knowledge, which can be built up over time from prior game elements to inform the player's decisions.

We'll talk more about the RAI model in future issues.

Revised DAR Theory

DAR Theory: Interactions between the player and the non-player portions of the game can be defined as the following. The player makes a decision. The player then takes action on that decision by communicating it to the game. The player then receives a response from the game.

This is the Decision-Action-Response (DAR) Theory.

Element: An Element is a discreet DAR section of a game that involves a significant decision by the player and/or provides a significant, specific response by the game, whether positive or negative.

Game elements are hierarchical; an entire game is an element composed of many sub-elements, each of which can have sub-elements. This hierarchy continues until the loss of significance of the three parts of DAR. Analysing an insignificant DAR element into a game is pointless.


After spending some more time thinking about my initially proposed DAR theory (Decision-Action-Reward), I decided to make some small modifications to it.

The principle change is that we define DAR as Decision-Action-Response, thus preserving the term "Reward" for positive responses, and semantically allowing for negative or neutral responses to decisions and actions.

Friday, September 01, 2006

Game Pathologies

A Pathology is a disfunction within a game. A Pathology happens when the game designer intended one effect to happen, but something else did that significantly changes the progression of the game.

Note that a true Pathology is one that is fundamental to the game, built into the game design. As such, under the Situation Model, a Pathology only ever arises from things born of either the Game Input or the Results of some prior input.

A Faux Pathology is not a true Pathology. Because analysing a game's design is virtually impossible without considering some player's involvement, it is possible for a player to consider something a Pathology that isn't. For example, it might simply be a puzzle that the player lacks sufficient Reason to solve, but feels to that player like there is a critical piece of Knowledge missing. The line between a Faux Pathology and a real Pathology is somewhat blury.

A Critical Pathology is a Pathology that causes the game to no longer be functional at all. A non-critical Pathology may allow the game to still be interesting or fullfill its goal; a Cricial Pathology does not. Note that the game can still be progressable, but the intent of the game design is drastically damaged if not destroyed. Typically, this happens in one of the following ways:

  • The game does not allow the player to progress, thus frustrating the player and eventually forcing the player to stop playing.

  • The game forces the player to engage in unreasonable activity in order to progress. The player may continue playing, but the player will lose a significant quantity of enjoyment for the game.

  • The game becomes incredibly easy for the player, thus stripping out all challenge from the game.

  • The player is forced to use external aids (online FAQs, asking someone, etc) in order to progress.

  • A Knowledge Pathology is a Pathology in a game based on the Knowledge domain of the Situation Model. The game's design provides certain expectations about the player's Knowledge base, for later Situations will test those expectations. A Pathology develops if the player's body of Knowledge differs significantly from what the game expects the player to have at that particular Situation.

    There are two forms of this Pathology. A Negative Knowledge Pathology is merely when the game has not provided the player with a certain piece of Knowledge. Because the player's Comprehension is also a factor, it can inspire a Faux Pathology whereby the player simply lacks the comprehension necessary to deduce the Knowledge. The true form of the Pathology is when the game does not provide any Result that has some form of logical consequence that leads from that Result to Knowledge.

    A slightly alternate form of this Pathology is when the game provides a result, but it requires a Game Input that the player is not likely to provide. This is often due to a Knowledge Pathology that has already occurred, however, and is rarely encountered in the absense of other errors.

    The second form of Knowledge Pathology is the False Knowledge Pathology. False Knowledge happens as a true Pathology when the game expects the player to reason one thing, but given what the player knows, some other thing that is actually untrue seems more reasonable based on that Result. The Faux Pathology analog of this is when one's Comprehension is simply erronous, or otherwise different from what the game expects, and the player's logical deduction came to the wrong conclusion.