the choice captures the imperfect ability to find an optimum, where the probability of a
mistake is related to its cost.3
To sum up, this Section has described the genetic algorithm employed in the simulations
and motivated the adoption of a pairwise tournament reinforcement rule and of the individual
learning design. Within the individual learning design, we discussed the assumed memory
size of six strategies for each agent and of a pairwise tournament choice rule.4
4 Simulation results with genetic algorithm agents
In this Section, we present the result of the interaction among genetic algorithm agents in a
common property resource environment and compare them with the human agent data from
the experiment. Extensions to some other experimental designs are also discussed.5 Before
presenting the analysis of fit, we discuss the choice of some parameter values.
Parameter values. Genetic algorithm agents constantly search for better strategies through
active, random experimentation that changes the composition of the memory set.
Experimentation in characterized by a level, p, which is the expected share of strategies in the
memory set that will randomly change from one period to the next. The value of p is chosen
in order to increase the fit between the human data and the simulation results and is set in the
following way. First, the strategy space is divided into a grid and coded with binary strings of
0s and 1s of length L. Second, with probability pm∈(0,1) that each digit ‘0’ can flip to ‘1’ or
3 The score of a strategy can be interpreted as the utility of the outcome associated with that strategy. Given the
ordinality of pairwise tournaments adopted for reinforcement and choice rule, this GA is based only on the
ordinal information of the score, like the utility function of the consumer.
4 A score is assigned to every strategy in the memory set, whether the strategy was chosen to be played or not.
The score of strategy not chosen to play was assigned under the assumption that all the other agents will not
change their actions in the following period (adaptive expectations).
5 Simulations with the same GA were run also in common property resource designs with sanctions. The results
are reported in Casari (2002).