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Chapter 25. Balance of the Planet

Chapter 25. Balance of the Planet

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Values

By far the most difficult question, though, concerned the problem of values. It was easy to build equations for air

pollution deaths and such, but in the end, how important were those deaths compared to the economic benefits of the

factories that create the air pollution? In effect, I had to put a price on human life, something that makes all of us

queasy. Environmental problems are difficult precisely because they force us to confront such uncomfortable issues.

My design problem was to integrate values into the design without creating something that would be dismissed as

narrow-minded.

I spent too much time trying to figure out how to be politic in this delicate matter; it seemed that, no matter how I

approached the problem, somebody would be able to accuse me, with some justification, of bias. There is no

objective, balanced approach to the myriad complexities of environmental issues; ultimately, the core issues are

matters of personal values.

It's funny how some of the toughest problems in game design can have ridiculously simple solutions. The challenge is

to step far enough back from the problem to be able to see the simple solution. In this case, my problem was

embarrassingly easy to solve: I need merely throw the problem right back into the user's face. In effect, I needed to

say to the user, "If you have an opinion, put your numbers where your mouth is. You declare the values to be applied

in the simulation!"

This simple insight dramatically changed the design and catapulted it into a higher level of simulation. It freed me of

the responsibility to be absolutely unbiased, although there remained an expectation of being fair. Instead of providing

the user with unchallengeable numbers, I needed only provide a range of reasonable numbers from which to choose.

It also changed the simulation in a more profound and more exciting manner. Instead of presenting the simulation itself

as the truth, I was presenting it as just that: a simulation. This new design concept challenged the user to understand it

as a simulation, not as truth handed down from the almighty computer. It also brought the user into the simulation,

thereby providing more direct and intimate interaction between user and computer. In a larger sense, this design

innovation allowed me to present my thinking on a deeper level. Instead of coddling the user with false assurances that

the simulation was correct, I was making it clear to him that much of what we believe is based on the assumptions

behind our thinking.

Another unforeseen benefit of this approach was that it applied broadly, not just to values, but to judgments about

scientific truth itself. Who is to say precisely how dangerous a nuclear power plant is? There are plenty of studies that

give us an inkling of the answer, but no definitive answer can be found. Why then, could not a user declare any reason

able value for this supposedly objective truth?

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Implementing a Value System

There remained the problem of designing some system that would permit the user to assign numeric values to the

components of the simulation while preventing psychotic behavior in the system. It's one thing to design a balanced

system of equations when the coefficients are all stable—but preserving balance when the user can change the

coefficients seemed beyond the realm of possibility.

An even more serious implementation problem loomed beyond this one: How was I to design a system of equations

that would be accessible to the average user? I had handled games with complex internal systems of equations, but a

system that the average user could handle? That seemed completely out of the question.

The design problem clearly called for linear equations of this form:

Result = Adjustable CoefficientxInput Factor

There might also be a need for additive equations of this form:

Result = Input #1 + Input #2 + Input #3

Such equations permitted the user to adjust the single coefficient to increase or decrease the severity of the

phenomenon.

The interesting design problem here is, how did I decide which variables to include and which to reject? A variety of

factors affected my decision. Obviously, I needed some form of point system reflecting what might constitute success

or failure; this required variables for various forms of points. I also needed to include obvious factors such as nuclear

power, coal power, and various forms of pollution. Ultimately, however, the choice of variables to include rested on

my familiarity with the issues underlying environmental problems. There was no cookbook method that I can offer

you; I simply had to apply my judgment based on my expertise. As it happens, I spent several years working on

environmental policy issues during the 1970s, so I required little more than a few books' worth of reading to bring my

expertise up to date. Had I lacked such expertise, I would have been reluctant to attempt the design.



LESSON 73

Know your topic inside and out.

Few game topics are closely tied to reality; by placing games in a fantasy environment, designers seldom need to

worry about the constraints of reality. There are plenty of exceptions, of course, flight simulators being the most

obvious. As the industry advances, game designers will be required to integrate more real-world knowledge into their

work. This will in turn make it ever more important that designers bring some real-world expertise into their work.

Content experts are invaluable, but they're not enough; the knowledge they offer must be integrated into the overall

design, and that integration process can take place only inside the designer's mind. Thus, content experts must be

treated as teachers, not direct contributors.

I created the following list of 154 variables for my simulation:

AcidRain

AveEnergyPrice

BasicResearch

BeefProduction



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The Politics of the Game

At this point, I must point out that here is where I got to ply my own political agenda. One cannot develop expertise

in a field without developing some opinions, and I'm no exception. While I wanted to be fair to all sides, I had to

plunk down some values for the coefficients, and those values represented my own opinions about environmental

problems. Although my own values are obvious in this game, the fact is that every game we produce reflects the

values of the designer. The physicist who designs a hydrogen bomb can't shuck off responsibility by claiming that he

was only following orders; there is an undeniable element of personal approbation required to work on any project.

The same thing goes with game designers: The games that you design reveal your values. You simply can't avoid it, so

you'd better learn to live with it and take responsibility for your actions.

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Higher Levels of Play

The great majority of players were happy to play the game with my own value system; I encouraged them to try

higher levels of participation and some took me up on that offer. The next higher level of play involved the concept of

political bias. I offered the player several other sets of values representing certain common points of view, such as

Environmentalist, Oil Company, Pro-Nuclear, and so forth. Players who tried out these value sets quickly discovered

that the only way to win was to follow the kind of policies embraced by proponents of each bias. For example, in the

Pro-Nuclear bias, nuclear power plants are safe, clean, and cheap; building lots of nuclear power plants generates lots

of cheap energy that propels the world economy to new heights. By contrast, other forms of energy are polluting or

dangerous; using these energy forms will lead to much suffering the world over.

The highest level of play invited the player to alter the coefficients in the model. This provided the greatest challenge

and required the deepest thought. For example, I am particularly proud of the two formulae shown in Figure 25.3.

25.3. Formulas for starvation and lung disease points.



Forcing the player to explicitly assign point values for human life puts a lot of stress on people, stress that most people

avoid by refusing to think about it. This game shoved the problem right into their faces. It turns out that, if you assign

equal values to both forms of death, you can never win the game, because the number of people who die from

starvation each year is overwhelming.



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Balancing the Equations

Balancing large mathematical systems is one of the killer problems in game design. Every game has plenty of

interacting subsystems; all too often some unforeseen interplay between two such subsystems can result in psychotic

behavior from the system as a whole.

Let's use an imaginary first-person shooter as an example. Suppose that this shooter offers the player a reward for

killing Goony-Woonies by equipping each Goony-Woony with one ammo clip, which the player gains by stripping

each dead Goony-Woony. Of course, Goony-Woonies are dangerous critters; the player sustains an average of two

health points worth of damage every time he fights a Goony-Woony. Fangybirds, by contrast, can be killed only by

shooting them with a gun. They carry medicine packets worth five health points. If the cost of shooting down a

Fangybird is less than one ammo clip, then the player has a simple strategy: Kill a Goony-Woony, use the clip

obtained to shoot down a Fangybird, use the medicine from the Fangybird to heal the injuries inflicted by the

Goony-Woony, and you're ahead in both ammo and health.

No game designer would make so glaring a blunder—but that's only because there are only two monsters here to

worry about. What if there were a hundred different monsters in the game? How can the designer get such a large

system operating with any confidence that it won't have some hidden combination that reduces the game to nonsense?



LESSON 75

Eschew sensitive functions like exponentials or hyperbolics.

This is no hypothetical problem. The first edition of Civilization suffered from precisely this strategy. Somebody

discovered a "lock on victory" in the form of a simple strategy, called "The Mongol Strategy," that always won the

game. That strategy took advantage of a slight weakness in the balancing of the system of equations at work in the

innards of the game. Now, Sid Meier is one of the great masters of complex simulation design; if this problem could

nip Sid, you can be sure that it will masticate a mere mortal like you.

Herewith, then, some lessons for balancing large systems.



NOTE

Warning! The following material is highly mathematical! If you don't care for mathematics, skip this

section!

Consider the following formula:

Apples = Oranges2

If the value of Oranges increases by 1%, the value of Apples will increase by 2%. To appreciate the danger imposed

by such a formula, think in terms of "excursions." Imagine that your system has settled down to a nice stable

configuration, and then one component, say Oranges, wiggles by 1%. That triggers an excursion. At its first step, the

excursion is only 1% in magnitude, but on the second step, it has doubled in size. If you have many more of these

sensitive functions in your system, the excursion can rapidly grow to outrageous levels.



LESSON 76

Dampen excursions with shock-absorbing functions.



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Chapter 25. Balance of the Planet

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