Game Development Reference
In-Depth Information
that CMSs are designed to support. If the players are sharing a game world and
competing for the same resources, such as land or minerals hidden in the environ-
ment, the game becomes a race to see who can grab the most, ignoring the other
aspects of play. If the players are operating in separated game worlds and have sym-
metric starting and victory conditions, the game tends to be about optimizing
efficiency. If the conditions are asymmetric, the game will be difficult to balance.
CMSs let the player be playful, to build and experiment in the world you've given
him. That's seldom consistent with competition. One major exception is in hybrid
games, those that have a military element as well as construction and management
elements. The section “Hybrid Games” later in this chapter discusses these.
Simulating Individuals
Many CMSs simulate the behavior of a group of people (or ants in the case of
SimAnt
) within an environment managed by the player. Games such as the original
SimCity
, which handle a large number of people, model behavior statistically rather
than keeping separate values for each person. However, you might want to simulate
the actions of particular individuals that the player can see moving around, as the
modern versions of
SimCity
do. This will make your game a good deal more enter-
taining because the player can take an interest in the actions and progress of
specific people. It appeals to a voyeuristic impulse and makes the consequences
of the player's decisions seem more personal. It's particularly effective when the
player can actually see unhappy people packing up and leaving.
Modeling individuals rather than statistical aggregates adds considerably to your
design job. You will need to create a behavioral model and determine what aspects
of the individual's condition the player will be trying to optimize. For example,
many such games include a single-valued variable that tracks a character's degree of
happiness or unhappiness and a set of needs that the simulated character desires to
fulfill. Fulfillment may come as the result of the character's autonomous action
(driving from home to work fulfills the need to get to work) or from action taken
by the player (building a school fulfills the characters' need for educational oppor-
tunity). If a need goes unfulfilled, either through a problem that arises within the
simulation (traffic jams prevent the person from getting to work) or because the
player fails to act (no school has been built), there should be a negative conse-
quence of some kind (the simulated person becomes unhappy).
Modeling individuals relieves you of the job of creating a statistical model because
the behavior of the individuals collectively provides the statistics, but balancing
such a game is a more intricate task. You will probably discover emergent behav-
iors; that is, unanticipated consequences of design decisions. Some of these will be
fascinating and almost seem like intelligence, but others will clearly be degenerate:
simulated people locked in a tight behavioral loop, for example, only ever doing
one or two things because your needs mechanism isn't balanced properly.
