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

they also reproduce some of the heterogeneity in inspection decisions that one can find in
human data. These simulations are not reported in this study.

6 Conclusions

In this paper, we study anomalous results from common property resource experiments
using a model of artificial adaptive agents. Experimental outcomes show a systematic
departure from the Nash equilibrium prediction, do not settle on a steady state, and are
characterized by a remarkable individual diversity in behavior. All three results are at odds
with the predictions of the unique, symmetric Nash equilibrium (Casari and Plott, 2003,
Rocco and Warglien, 1996, and Walker, Gardner, and Ostrom, 1990). Similar features could
be found also in public goods (Laury and Holt, 1998) and Cournot oligopoly experiments
(Cox and Walker, 1998).

We employ an individual learning genetic algorithm model to simulate behavior in a
common property resource game. Their limitations includes inability to maximize,
constrained memory, and lack of common knowledge about the rationality level of others.
Similar models have been successfully used to replicate experimental behavior in other
environments (Arifovic and Ledyard, 2000, Arifovic, 1994).

Simulations are run through individual learning genetic algorithms and evaluated using the
experimental results from Casari and Plott (2003) as a benchmark on three dimensions:
aggregate cooperation level, aggregate variability, and individual heterogeneity. There are
four main conclusions.

First, genetic algorithm agents closely reproduce aggregate level behavior of human agents
both in terms of cooperation levels and variability in aggregate cooperation over time.

Second, the interaction of genetic algorithm agents generates about two thirds of the
individual heterogeneity in experimental data. This result is remarkable because the artificial

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