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Representational Sensitivity in A Simple Agent-Based Computational Economics Experiment
 

Summary: Representational Sensitivity in A Simple Agent-Based
Computational Economics Experiment
Daniel Ashlock
Nicole Leahy
August 30, 2000
Abstract
This article investigate the sensitivity of a simple experiment in Agent-based Computational Eco-
nomics(ACE) to the choice of representation of the agents. We demonstrate that significant differences
in experimental outcomes result from varying the representation, even when other experimental factors
are left as close to constant as possible. All experimental parameters other than agent representation and,
necessarily, variation operators are left constant. In learning the classic Divide the Dollar game proposed
by Nash the agents exhibit distinct behaviors. The implications for requiring that representations be
normed against experimental results obtained with human agents are discussed.
1 Introduction
Evolutionary computation(EC) is one potential technique for training agents for use in Agent-based Com-
putational Economics(ACE). Among its advantages are the ability to train agents based on solely on per-
formance measures and the ability to create a large number of distinct, boundedly rational agent behaviors
within a single software environment. Evolutionary computation is controversial because the variation op-
erators used to modify agents during learning, inspired by biological sex and mutation, are not thought to
reflect what happens when a participant in an economic system learns. For a survey of various types of

  

Source: Ashlock, Dan - Department of Mathematics and Statistics, University of Guelph

 

Collections: Mathematics