which is the crossover rate. Simulations in a common property environment that are not here
reported show a rather small influence of crossover on the results. Hence, we decided to set
the crossover rate to zero, pc=0, and adjust only the mutation rate.
Results. The results of the simulations of resource use with genetic algorithm agents are
now presented. Genetic algorithm agents replicate cooperation levels of humans (Result 1),
the pulsing patterns (Result 2), and to a large extent individual heterogeneity (Result 3).6
The numerical results presented are averages over 100 simulations run with different
random seeds 0.005 through 0.995. There are three different lengths T of the simulations in
order to mimic the behavior of inexperienced agents (T=32, as the actual length of a
laboratory session was 32 periods), experienced agents (T=64), which have already acquired
one session of experience, and of long term behavior (T=400).7 In all cases, the numerical
results presented in Table 1 refers just to the last 32 periods of the simulation and ignore the
previous periods. For instance, the aggregate resource use reported when T=64, is the
average of periods from 33 to 64. The reason of this choice is to be able to perform an
homogenous comparison with human agent data, where the length of an experimental session
is always of 32 periods.
Result 1 (Aggregate resource use)
The aggregate resource use X of genetic algorithm agents (GAs) is not statistically different
from humans agents’s levels. In both cases, agents cooperate less than the Nash equilibrium
level.
The aggregate level or resource use of the GA agents (XGA) closely matches the
experimental results (XH=131.32). For inexperienced GA (T=32), the cooperation level
6 The simulations were run on a PC and the GA agents were programmed in Turbo Pascal. Useful references
for the code were Goldberg(1989) and a version given by Jasmina Arifovic.
7 Simulation longer than 400 periods were performed (up to 10,000 periods) but do not change the conclusions
about long term behavior of GA.
10
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