For example, the mechanics of card games include shuffling, trick-taking and betting – from which dynamics like bluffing can emerge. The mechanics of shooters include weapons, ammunition and spawn points – which sometimes produce things like camping and sniping. The mechanics of golf include balls, clubs, sand traps and water hazards – which sometimes produce broken or drowned clubs.
Adjusting the mechanics of a game helps us fine-tune the game’ s overall dynamics. Consider our Monopoly example. Mechanics that would help lagging players could include bonuses or“ subsidies” for poor players, and penalties or“ taxes” for rich players – perhaps calculated when crossing the Go square, leaving jail, or exercising monopolies over a certain threshold in value. By applying such changes to the fundamental rules of play, we might be able to keep lagging players competitive and interested for longer periods of time.
Another solution to the lack of tension over long games of Monopoly would be to add mechanics that encourage time pressure and speed up the game. Perhaps by depleting resources over time with a constant rate tax( so people spend quickly), doubling all payouts on monopolies( so that players are quickly differentiated), or randomly distributing all properties under a certain value threshold.
Tuning
Clearly, the last step our Monopoly analysis involves play testing and tuning. By iteratively refining the value of penalties, rate of taxation or thresholds for rewards and punishments, we can refine the Monopoly gameplay until it is balanced.
When tuning, our aesthetic vocabulary and models help us articulate design goals, discuss game flaws, and measure our progress as we tune. If our Monopoly taxes require complex calculations, we may be defeating the player’ s sense of investment by making it harder for them to track cash values, and therefore, overall progress or competitive standings.
Similarly, our dynamic models help us pinpoint where problems may be coming from. Using the D6 model, we can evaluate proposed changes to the board size or layout, determining how alterations will extend or shorten the length of a game.
MDA at Work
Now, let us consider developing or improving the AI component of a game. It is often tempting to idealize AI components as black-box mechanisms that, in theory, can be injected into a variety of different projects with relative ease. But as the framework suggests, game components cannot be evaluated in vacuo, aside from their effects on a system behavior and player experience.
First Pass
Consider an example Babysitting game [ Hunicke, 2004 ]. Your supervisor has decided that it would be beneficial to prototype a simple game-based AI for tag. Your player will be a babysitter, who must find and put a single baby to sleep. The demo will be designed to show off simple emotive characters( like a baby), for games targeted at 3-7 year-old children.
What are the aesthetic goals for this design? Exploration and discovery are probably more important than challenge. As such the dynamics are optimized here not for“ winning” or“ competition” but for having the baby express emotions like surprise, fear, and anticipation.
Hiding places could be tagged manually, paths between them hard-coded; the majority of game logic would be devoted to maneuvering the baby into view and creating baby-like reactions. Gameplay mechanics would include talking to the baby(“ I see you!” or“ boo!”), chasing the baby( with an avatar or with a mouse), sneaking about, tagging and so on.
Second Pass
Now, consider a variant of this same design – built to work with a franchise like Nickelodeon’ s“ Rugrats” and aimed at 7-12 year-old-girls. Aesthetically, the game should feel more challenging – perhaps there is some sort of narrative involved( requiring several“ levels”, each of which presents a new piece of the story and related tasks).
In terms of dynamics, the player can now track and interact with several characters at once. We can add time pressure mechanics( i. e. get them all to bed before 9 pm), include a“ mess factor” or monitor character emotions( dirty diapers cause crying, crying loses you points) and so on.
For this design, static paths will no longer suffice – and it’ s probably a good idea to have them choose their own hiding places. Will each baby have individual characteristics, abilities or challenges? If so, how will they expose these differences to the player? How will they track internal state, reason about the world, other babies, and the player? What kinds of tasks and actions will the player be asked to perform?
Third Pass
Finally, we can conceive of this same tag game as a fullblown, strategic military simulation – the likes of Splinter Cell or Thief. Our target audience is now 14-35 year old men.
Aesthetic goals now expand to include a fantasy element( role-playing the spy-hunting military elite or a loot-