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

agents are by design identical in their goal of income maximization and in their limited
rationality level. Yet, they do not fully account for the individual heterogeneity of human
subjects. Hence, the implication to draw is that the experimental data are in fact generated by
different types of agents and hence a descriptive model must explicitly include more than one
type of agents. Agent diversity can take two non-mutually exclusive dimensions. Agents
could intentionally deviate from the maximization of personal income. In particular, they
might exhibit varying degrees of other-regarding preferences. On the other hand, agents
could differ in their problem-solving skills. For instance, not everybody necessarily has the
same memory constraints or computational limitations. The latter path constitutes an
interesting extension of this work.

Third, the evolutionary process underlying a genetic algorithm is fundamentally different
from noisy best reply. A simple model with trembling hand fares considerably worse than a
genetic algorithm in explaining the data. For a start, notwithstanding a comparable level of
noise, noisy best reply can explain less than one-sixth of the individual heterogeneity of
human data vis-à-vis about two-thirds of the genetic algorithm. Then, it simply makes a static
prediction. On the contrary, with genetic algorithm agents, their experimentation through
random search interacts with bounded rationality and, with experience, moves the outcome
closer to the Nash equilibrium.

Finally, predictions relative to different experimental designs of common property resource
appropriation are put forward. When the strategy space is restricted while leaving the Nash
equilibrium unchanged, the cooperation level among genetic algorithm agents raises.
Experimental results from Walker, Gardner, and Ostrom (1990) support this prediction.
Consider also a situation where after having decided his own exploitation level of the
common property resource, each agent has the option of selecting other individuals for
sanctioning. Simulation of genetic algorithm interactions under two treatments of such a

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