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

XGA=131.02 cannot statistically be distinguished from the human value at a 0.05 level.
Similarly for experienced GA, XGA=130.40 and long term XGA=130.02. (Table 1, columns
(3), (4), and (5)).

Result 2 (Variability of aggregate resource use)

Genetic algorithm agents (GAs) exhibit a higher variability over time in aggregate resource
use σ(X) than human agents; such variability, however, decreases with experience.

When inexperienced GA agents interact (T=32), the variability in aggregate group use as
measured by the standard deviation of resource appropriation over time is σ(X)GA=17.50
versus σ(X)H=12.9 with humans. With experience the variability decreases to σ(X)GA=15.03
and σ(X)GA=14.04. An alternative measure of variability of aggregate resource use is the
percentages of periods in which aggregate payoffs are negative. For GA agents this statistics
goes from 19.59% (T=32), to 16.00% (T=64), to 14.97% (T=400) while it is 15.5% for
human agents (Table 1). A visual comparison between GA agents and human agents is
offered by Figure 1. The pattern for GA agents in Figure 1 is an example of four random
runs.

The same level of aggregate variability can hide widely different patterns of individual
variability. Before proceeding to outline Result 3, an example is presented to introduce the
precise definition of individual heterogeneity adopted throughout the paper. Consider
scenarios A and B in Table 2 with two players and four periods.

Table 2: Examples of two patterns of individual variability

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