The genetic algorithm decision maker can be described as follow. A strategy is identified
by a single real number. It is encoded as a binary string, a so-called chromosome, and has
associated with it a score (measure of fitness) that derives from the actual or potential payoff
from this strategy. In a social learning (single-population) basic GA, each agent has just one
strategy (chromosome) available, which may change from one period to the next. In an
individual learning (multi-population) algorithm, which is the version adopted in this study,
each agent is endowed with a set of strategies, and each set may change independently from
other sets from one period to the next. The changes are governed by three probabilistic
operators: a reinforcement rule (selection), which tends to eliminate strategies with lower
score and replicate more copies of the better performing ones; crossover, which combines
new strategies from the existing ones; and mutation, which may randomly modify strategies.
In a basic GA, the strategies (chromosomes) created by crossover and mutation are directly
included in the next period’s set of strategies (population).
The three operators are stylized devices that are meant to capture elements involved in
human learning when agents interact. The reinforcement rule (selection) represents
evolutionary pressure that induces agents to discard bad strategies and imitate good
strategies; crossover represents the creation of new strategies and the exchange of
information; mutation can bring new strategies into a range that has not been considered by
the agents.
Most of the parameters of the genetic algorithm were chosen exogenously, based on
considerations external to the data here analyzed and not based on fit improvement
considerations. On the contrary, the next Section will discuss the two free parameters,
mutation and crossover rates.
The description of the exogenous features of the genetic algorithm begins with the
reinforcement rule. GA agents are adaptive learners in the sense that successful strategies are
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