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

thinking process of each agent along with the inertia built into the decision maker, which
leads to path-dependence in choice. In particular, the need to coordinate among many agents
might play an important role in generating a diverse behavior across agents. One might also
notice that the tendency of GA agents to converge to the Nash equilibrium at the aggregate
level seems stronger than at the individual level. In conclusion, Results 1, 2, and 3 are not
simply a consequence of the noise built into the GA.

Predictions about other experiments. Besides comparisons with data from baseline common
property resource experiments, simulations with genetic algorithm agents allow to make
predictions about the effects of different experimental designs. Two changes are here
discussed, a modification of the strategy space and the addition of a decentralized monitoring
and sanctioning system.

Consider the following three designs: (A) the individual use level strategy space is [0, 50];
(B) the individual strategy space is [0, 20]; (C) the strategy space is [0, 16]. All three designs
have the same Nash equilibrium, xi = 16, and differ only in the strategy space. When agents
are fully rational, designs A and B simply supply agents with options that are irrelevant to
their actions and there is no substantive difference with C. The baseline design considered in
this paper is A. In the context of voluntary provision of public good experiments most
environment are similar to design C while designs with interior Nash are similar to A and B.
Simulations with genetic algorithm show an decrease in aggregate resource use as the
individual strategy space reduces from A to B, and then further to C. As Table 3 shows, the
efficiency in use achieved by GA increases of about 13 points between A and B.¹¹ The
impact of off-equilibrium strategy on the aggregate outcome is driven by the tendency of
genetic algorithm agents to experiment with all available strategies. A similar “surprising”
efficiency improvement was observed by Walker, Gardner, and Ostrom (1990) in a common

¹¹ Results are less dramatic when GA agents are more experienced (T=400 instead of T=32).

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