Understanding Our Creations:
Using Machine Learning to Understand Machine models.
CPM Report No.: 02-96
By: David Hales
Date: 2nd May 2002.
Artificial Society and Artificial Life computer models often contain many
exogenously defined parameters. Such parameters may take a number of possible values.
Alternative values represent alternative model assumptions. The space of all possible
values comprises a parameter space of model variants. Results of experimentation with
such models are often based on execution of the model for a fixed set of (arbitrary)
parameter values. Given some (possibly) emergent phenomena of interest, for which the
model has been constructed to investigate, such a method gives a simple "existence proof".
An existence proof demonstrates that at least some parameter values are sufficient to
produce the phenomena of interest. However, a deeper understanding of the model can be
achieved by exploring the parameter space.
In this paper an artificial society model (the StereoLab) is outlined which contains
cooperating interacting agents. Cooperation between agents is mediated by stereotypes
that are held by agents. The stereotypes evolve memetically via cultural interaction
and mutation. The model specifies a large number of exogenously defined parameters
that determine various aspects of agent and environmental behaviour. Two methods of
exploring this very large parameter space are applied in the search for regions
which produce high levels of agent cooperation. By using these techniques a region
is located in which a novel cooperation forming process is identified based on the
formation of agent groups.