Inverse Simulation and Genetics-Based Validation
for Social Interaction Analysis via Multiagents
Takao Terano, Setsuya Kurahashi
terano@gssm.otsuka.tsukuba.ac.jp
The validation of agent-based simulation is quite important
to convince the results to various audiences. To address the issues,
we have developed a new agent-based simulation environment using Genetic
Algorithms (GAs). The basic principles are summarized as follows:
(1) Set various agent parameters as an individual of GAs; (2) Run the agent-based
model in parallel so that the runs form the population of GAs; (3) Based
on a given criteria or an objective function, each simulation result is evaluated;
(4) Genetic operators are then applied to generate the other set of agent
parameters; and (5) After the convergence or when we have 'desired' results,
the variances among the parameters are evaluated to validate the results
in the sense of the sensitivity analyses.
TRURL is such a simulation environment, which evolves
artificial worlds of multiagents to socially interact with each other.
The agents in TRURL solve simple multi-attribute decision problems via the
message communication among them. The micro-level agent activities
are determined by both predetermined and acquired parameters. The former
parameters have constant values during one simulation epoch, however, the
latter parameters change during the interactions. TRURL utilizes
the above principles to evolve the societies by changing the predetermined
parameters to optimize macro-level socio-metric measures, which can be observed
in such real societies as e-mail oriented organizations and electronic commerce
markets. Thus, using TRURL, we automatically tune the parameters
up and validate the results from both micro- and macro-level phenomena grounding
on the activities of real worlds.