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

Note: D=difference between maximum and minimum, SD=standard deviation

The two scenarios are identical when considering both aggregate production Xt=Σi xit and
overall indexes of variability of individual actions, such as the mean of the difference, period

by period, between the maximum and minimum individual productions,

, or the standard deviation of individual actions xit (SD1). The

differences in the patterns of individual variability between scenario A and B can be captured
by splitting the overall individual variability into variability across agents (D2 and SD2) and
over time (SD3). In order to calculate agent-specific variability, first we compute the average
.-1

individual production over time xi = — ∑ x_it and, using those data, compute the difference
T t =1

D2=max {Xi} - min {Xi} and the standard deviation for xi (SD2) (Table 2). Scenario A rates
ii

highly in terms of variability across agents, and that is referred to here as high individual
heterogeneity, while scenario B rates highly in terms of variability over time but exhibits no
individual heterogeneity.

When the same statistics developed for the example in Table 2 are applied to the simulation
results (Table 1), a remarkable level of individual heterogeneity emerges from the interaction
of ex-ante identical genetic algorithm agents (Result 3).

Result 3 (Individual heterogeneity with resource use)

Identical genetic algorithm agents (GAs) use the resource at significantly different rates.
Depending on the level of eXperience and of the measure adopted, between 45% and 80% of
the human individual heterogeneity is reproduced by GA agents. In particular, ineXperienced
GA agents have an heterogeneity levels in resource use SD2 not statistically different from
human agents.

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