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-