Emergence and progression - Game Mechanics: Advanced Game Design - page 26

Games of Emergence
The use of the term emergence in games, which predates Juul's categories, originates
from the use of the term in complexity theory. There it refers to behavior of a system
that cannot be derived (directly) from its constituent parts. At the same time, Juul
cautions us not to confuse emergent behavior with games that display behavior the
designer simply did not foresee (2002). In games, as in any complex system, the
whole is more than the sum of its parts. Go and chess are famous for generating
enormous depth of play with relatively simple elements and rules. Something similar
can be said of relatively simple computer games such as Tetris, Boulder Dash, or World
of Goo . These games consist of relatively simple parts, yet the number of strategies
and approaches that they allow is enormous. No two play-throughs will feel the
same. The emergent quality of the gameplay comes not from the complexity of
individual parts but from the complexity that is the result of the many interactions
among the parts.
Simple Parts in Complex Systems
The science of complexity studies all manner of complex systems in real life. While
the active agents or active elements in these complex systems can be quite sophisti-
cated in themselves, they are typically simulated with simple models. For example,
to study the flow of pedestrians in different environments, great results have been
achieved by simulating pedestrians with only a few behavioral rules and goals (Ball,
2004, pp. 131-147). In this topic, we take a similar approach to games. Although
it is possible to create emergent games with a few complex elements, we are more
interested in the mechanics of game systems that work with simple parts but still
create emergent gameplay. The advantage of our approach is that, in the end, these
games are efficient to build, even if they are initially more difficult to understand.
probability space
in the previous chapter, we mentioned that games are often regarded as state machines:
hypothetical machines that progress from one state to another based on their current
state and the input provided by players. in games, the number of states can grow very
fast, and yet not every state is possible. not every random placement of pieces on a chess
board represents a game state that can be reached through actual play. For example, it
is not possible to have pawns in your color on the row closest to you in a real game or
to have both your bishops on a square of the same color. When the number of possible
states is very large, game scholars refer to them collectively as a probability space . The
probability space represents all the possible states that can be reached from the current
state. The probability space can be described as having a wide or a deep shape. When
the shape of the space is wide, there are many different states that can be reached from
the current state: Usually this means that players have many options. if the shape is
deep, there are many different states that can be reached after many subsequent choices.