bed for models of bounded rationality because they present decision-makers with a well-
defined environment where decisions are made repeatedly.
In this paper, we focus on common property resource experiments with an emphasis not
only on the qualitative findings from human subjects but on the ability of the genetic
algorithm to match their quantitative levels as well. There are two main innovative features.
One is the study of individual behavior. To the best of our knowledge, no previous study has
compared the individual behavior of genetic algorithms with experimental human data.
Similar aggregate results can hide a wide diversity in individual actions. The other innovative
aspect has to do with analyses of the experimentation process with new strategies. The
experimentation process is not simply an additional element of randomness but interacts at a
deeper level with the limited cognitive abilities of the agents.
In Section 2, we outline the experimental design and results. In the following Section, we
describe the artificial adaptive agents. In Section 4, we present the results of the simulations
in reference to the level and variability of aggregate resource use as well as individual
heterogeneity. We conclude in Section 5.
2 Experimental design and evidence
This Section first describes the incentive structure of the experiment and then outlines the
results. A more detailed description of them can be found in Casari and Plott (2003).
Consider a group of agents i=1, .., 8. Each agent decides on an effort level xi∈[0, 50] of a
common property resource. An agent i’s payoff function is:
∏i = ↑ f (X) - c(χ) (1)
X