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

decentralized sanctioning system were run but not reported in this study. Such simulation
results match many of the experimental data pattern reported in Casari and Plott (2003).

To conclude, we find that genetic algorithm agents exhibit many of the same patterns
observed in common property resource experiments. Alongside its evolutionary nature, the
ability to generate individually distinct patterns of behavior originating from identical goals
and identical rationality levels may be the most interesting feature of an individual learning
genetic algorithm.

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