Can genetic algorithms explain experimental anomalies? An application to common property resources

CAN GENETIC ALGORITHMS EXPLAIN EXPERIMENTAL ANOMALIES?

AN APPLICATION TO COMMON PROPERTY RESOURCES

1 Introduction

Even in simple games with a unique equilibrium, experimental results often exhibit
patterns inconsistent with the predictions of perfectly rational and selfish agents. It is not
unusual to find patterns of heterogeneity in individual behavior when there is a symmetric
equilibrium, oscillations in the aggregate outcome, significant differences between
inexperienced and experienced players, or systematic deviations from the predicted
equilibrium (Kagel and Roth, 1995). In this paper, we employ a model of adaptive learning,
based on a genetic algorithm, to explain the results from a common property resource
experiment, which, to some degree, exhibits all the mentioned patterns.

Two routes can be followed to explain the above patterns in experimental data. One is to
differentiate the goal of the agents from pure personal income maximization to include
varying degrees of other-regarding preference. The other route, followed in this paper, is to
weaken the perfect rationality assumption. More specifically, we use a model of adaptive
learning agents with a limited working memory, inability to maximize, and active
experimentation with new strategies. All agents have an identical, although bounded, level of
rationality.

Genetic algorithms were first developed by Holland (1975) as stochastic search algorithms
by looking at the biological processes of evolution. They have been employed to explain a
variety of experimental data, including data from auctions (Andreoni and Miller, 1995,
Dawid, 1999), oligopolies (Arifovic, 1994), foreign currency markets (Arifovic, 1996), and
Grove mechanisms (Arifovic and Ledyard, 2000). Experimental data offer an attractive test

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