MDA: A Formal Approach To Game Design And Game Research

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MDA: A Formal Approach to Game Design and Game ResearchRobin Hunicke, Marc LeBlanc, Robert Zubekhunicke@cs.northwestern.edu, marc leblanc@alum.mit.edu, rob@cs.northwestern.eduAbstractIn this paper we present the MDA framework (standing forMechanics, Dynamics, and Aesthetics), developed andtaught as part of the Game Design and Tuning Workshop atthe Game Developers Conference, San Jose 2001-2004.MDA is a formal approach to understanding games – onewhich attempts to bridge the gap between game design anddevelopment, game criticism, and technical game research.We believe this methodology will clarify and strengthen theiterative processes of developers, scholars and researchersalike, making it easier for all parties to decompose, studyand design a broad class of game designs and gameartifacts.IntroductionAll artifacts are created within some design methodology.Whether building a physical prototype, architecting asoftware interface, constructing an argument orimplementing a series of controlled experiments – designmethodologies guide the creative thought process and helpensure quality work.Specifically, iterative, qualitative and quantitative analysessupport the designer in two important ways. They help heranalyze the end result to refine implementation, andanalyze the implementation to refine the result. Byapproaching the task from both perspectives, she canconsider a wide range of possibilities andinterdependencies.This is especially important when working with computerand video games, where the interaction between codedsubsystems creates complex, dynamic (and oftenunpredictable) behavior. Designers and researchers mustconsider interdependencies carefully before implementingchanges, and scholars must recognize them before drawingconclusions about the nature of the experience generated.In this paper we present the MDA framework (standing forMechanics, Dynamics, and Aesthetics), developed andtaught as part of the Game Design and Tuning Workshopat the Game Developers Conference, San Jose 2001-2004[LeBlanc, 2004a].MDA is a formal approach tounderstanding games – one which attempts to bridge thegap between game design and development, gamecriticism, and technical game research. We believe thismethodology will clarify and strengthen the iterativeprocesses of developers, scholars and researchers alike,making it easier for all parties to decompose, study anddesign a broad class of game designs and game artifacts.Towards a Comprehensive FrameworkGame design and authorship happen at many levels, andthe fields of games research and development involvepeople from diverse creative and scholarly backgrounds.While it’s often necessary to focus on one area, everyone,regardless of discipline, will at some point need to considerissues outside that area: base mechanisms of gamesystems, the overarching design goals, or the desiredexperiential results of gameplay.AI coders and researchers are no exception. Seeminglyinconsequential decisions about data, representation,algorithms, tools, vocabulary and methodology will trickleupward, shaping the final gameplay. Similarly, all desireduser experience must bottom out, somewhere, in code. Asgames continue to generate increasingly complex agent,object and system behavior, AI and game design merge.Systematic coherence comes when conflicting constraintsare satisfied, and each of the game’s parts can relate toeach other as a whole. Decomposing, understanding andcreating this coherence requires travel between all levels ofabstraction – fluent motion from systems and code, tocontent and play experience, and back.We propose the MDA framework as a tool to helpdesigners, researchers and scholars perform thistranslation.MDAGames are created by designers/teams of developers, andconsumed by players. They are purchased, used andeventually cast away like most other consumable goods.CreatesConsumesGameDesignerThe production and consumption of game artifacts.Player

The difference between games and other entertainmentproducts (such as books, music, movies and plays) is thattheir consumption is relatively unpredictable. The string ofevents that occur during gameplay and the outcome ofthose events are unknown at the time the product isfinished.The MDA framework formalizes the consumption ofgames by breaking them into their distinct components:RulesSystem“Fun” and establishing their design esignerThe designer and player each have a different perspective.When working with games, it is helpful to consider boththe designer and player perspectives. It helps us observehow even small changes in one layer can cascade intoothers. In addition, thinking about the player encouragesexperience-driven (as opposed to feature-driven) design.As such, we begin our investigation with a discussion ofAesthetics, and continue on to Dynamics, finishing withthe underlying Mechanics.AestheticsMechanics describes the particular components of thegame, at the level of data representation and algorithms.Dynamics describes the run-time behavior of themechanics acting on player inputs and each others’outputs over time.Aesthetics describes the desirable emotional responsesevoked in the player, when she interacts with the gamesystem.Fundamental to this framework is the idea that games aremore like artifacts than media. By this we mean that thecontent of a game is its behavior – not the media thatstreams out of it towards the player.Thinking about games as designed artifacts helps framethem as systems that build behavior via interaction. Itsupports clearer design choices and analysis at all levels ofstudy and development.MDA in DetailMDA as LensEach component of the MDA framework can be thought ofas a “lens” or a “view” of the game – separate, but causallylinked. [LeBlanc, 2004b].From the designer’s perspective, the mechanics give rise todynamic system behavior, which in turn leads to particularaesthetic experiences. From the player’s perspective,aesthetics set the tone, which is born out in observabledynamics and eventually, operable mechanics.What makes a game “fun”? How do we know a specifictype of fun when we see it? Talking about games and playis hard because the vocabulary we use is relatively limited.In describing the aesthetics of a game, we want to moveaway from words like “fun” and “gameplay” towards amore directed vocabulary. This includes but is not limitedto the taxonomy listed here:1. SensationGame as sense-pleasure2. FantasyGame as make-believe3. NarrativeGame as drama4. ChallengeGame as obstacle course5. FellowshipGame as social framework6. DiscoveryGame as uncharted territory7. ExpressionGame as self-discovery8. SubmissionGame as pastimeFor example, consider the games Charades, Quake, TheSims and Final Fantasy. While each are “fun” in their ownright, it is much more informative to consider the aestheticcomponents that create their respective player experiences:Charades: Fellowship, Expression, Challenge.Quake: Challenge, Sensation, Competition, Fantasy.The Sims: Discovery, Fantasy, Expression, Narrative.Final Fantasy: Fantasy, Narrative, Expression,Discovery, Challenge, Submission.Here we see that each game pursues multiple aestheticgoals, in varying degrees. Charades emphasizes Fellowshipover Challenge; Quake provides Challenge as a mainelement of gameplay. And while there is no Grand UnifiedTheory of games or formula that details the combinationand proportion of elements that will result in “fun”, this

taxonomy helps us describe games, shedding light on howand why different games appeal to different players, or tothe same players at different times.For example, the model of 2 six-sided die will help usdetermine the average time it will take a player to progressaround the board in Monopoly, given the probability ofvarious rolls.Aesthetic ModelsThermometerUsing out aesthetic vocabulary like a compass, we candefine models for gameplay. These models help usdescribe gameplay dynamics and mechanics.RoomFor example: Charades and Quake are both competitive.They succeed when the various teams or players in thesegames are emotionally invested in defeating each other.This requires that players have adversaries (in Charades,teams compete, in Quake, the player competes againstcomputer opponents) and that all parties want to win.It’s easy to see that supporting adversarial play and clearfeedback about who is winning are essential to competitivegames. If the player doesn’t see a clear winning condition,or feels like they can’t possibly win, the game is suddenlya lot less interesting.Dynamic ModelsDynamics work to create aesthetic experiences. Forexample, challenge is created by things like time pressureand opponent play. Fellowship can be encouraged bysharing information across certain members of a session (ateam) or supplying winning conditions that are moredifficult to achieve alone (such as capturing an enemybase).Expression comes from dynamics that encourageindividual users to leave their mark: systems forpurchasing, building or earning game items, for designing,constructing and changing levels or worlds, and forcreating personalized, unique characters. Dramatic tensioncomes from dynamics that encourage a rising tension, arelease, and a denouement.Chance in 36As with aesthetics, we want our discussion of dynamics toremain as concrete as possible. By developing models thatpredict and describe gameplay dynamics, we can avoidsome common design pitfalls.Too Cold!Too Hot!ControllerA thermostat, which acts as a feedback system.Similarly, we can identify feedback systems withingameplay to determine how particular states or changesaffect the overall state of gameplay. In Monopoly, as theleader or leaders become increasingly wealthy, they canpenalize players with increasing effectiveness. Poorerplayers become increasingly poor.MoveRollLosers Winners Pay Up!Cash In!The feedback system in Monopoly.As the gap widens, only a few (and sometimes only one) ofthe players is really invested. Dramatic tension and agencyare lost.Using our understanding of aesthetics and dynamics, wecan imagine ways to fix Monopoly – either rewardingplayers who are behind to keep them within a reasonabledistance of the leaders, or making progress more difficultfor rich players. Of course – this might impact the game’sability to recreate the reality of monopoly practices – butreality isn’t always “fun”.Mechanics22 33 4 55677 8 99 1010 1111 1212Die RollsProbabilistic distribution of the random variable 2 D6.Mechanics are the various actions, behaviors and controlmechanisms afforded to the player within a game context.Together with the game’s content (levels, assets and so on)the mechanics support overall gameplay dynamics.

For example, the mechanics of card games includeshuffling, trick-taking and betting – from which dynamicslike bluffing can emerge. The mechanics of shootersinclude weapons, ammunition and spawn points – whichsometimes produce things like camping and sniping. Themechanics of golf include balls, clubs, sand traps andwater hazards – which sometimes produce broken ordrowned clubs.Adjusting the mechanics of a game helps us fine-tune thegame’s overall dynamics. Consider our Monopolyexample. Mechanics that would help lagging players couldinclude bonuses or “subsidies” for poor players, andpenalties or “taxes” for rich players – perhaps calculatedwhen crossing the Go square, leaving jail, or exercisingmonopolies over a certain threshold in value. By applyingsuch changes to the fundamental rules of play, we might beable to keep lagging players competitive and interested forlonger periods of time.Another solution to the lack of tension over long games ofMonopoly would be to add mechanics that encourage timepressure and speed up the game. Perhaps by depletingresources over time with a constant rate tax (so peoplespend quickly), doubling all payouts on monopolies (sothat players are quickly differentiated), or randomlydistributing all properties under a certain value threshold.TuningClearly, the last step our Monopoly analysis involves playtesting and tuning. By iteratively refining the value ofpenalties, rate of taxation or thresholds for rewards andpunishments, we can refine the Monopoly gameplay untilit is balanced.When tuning, our aesthetic vocabulary and models help usarticulate design goals, discuss game flaws, and measureour progress as we tune. If our Monopoly taxes requirecomplex calculations, we may be defeating the player’ssense of investment by making it harder for them to trackcash values, and therefore, overall progress or competitivestandings.Similarly, our dynamic models help us pinpoint whereproblems may be coming from. Using the D6 model, wecan evaluate proposed changes to the board size or layout,determining how alterations will extend or shorten thelength of a game.MDA at WorkNow, let us consider developing or improving the AIcomponent of a game. It is often tempting to idealize AIcomponents as black-box mechanisms that, in theory, canbe injected into a variety of different projects with relativeease. But as the framework suggests, game componentscannot be evaluated in vacuo, aside from their effects on asystem behavior and player experience.First PassConsider an example Babysitting game [Hunicke, 2004].Your supervisor has decided that it would be beneficial toprototype a simple game-based AI for tag. Your player willbe a babysitter, who must find and put a single baby tosleep. The demo will be designed to show off simpleemotive characters (like a baby), for games targeted at 3-7year-old children.What are the aesthetic goals for this design? Explorationand discovery are probably more important than challenge.As such the dynamics are optimized here not for“winning” or “competition” but for having the babyexpress emotions like surprise, fear, and anticipation.Hiding places could be tagged manually, paths betweenthem hard-coded; the majority of game logic would bedevoted to maneuvering the baby int

design a broad class of game designs and game artifacts. Towards a Comprehensive Framework Game design and authorship happen at many levels, and the fields of games research and development involve people from diverse creative and scholarly backgrounds. While it s often necessary to focus on one area, everyone,

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