(a) Agents cooperate less than the Nash equilibrium (use the resource more than Nash
equilibrium). Average resource use efficiency is 28.4%, which is statistically different
than the predicted 39.5% (p=0.05).
(b) Group use fluctuates over time (pulsing patterns). The average standard deviation of
group use over time within a session is 12.95 with an average resource use of 131.32. An
interval of one standard deviation around the average corresponds to an efficiency range
of [0.0%, 58.5%].
(c) Individual behavior is persistently heterogeneous. For instance, the difference between
the average use of the agent who used the resource the most and the average use of the
agent who used the resource the least within each session, [maxi{ xi} - mini{ xi}] = 28.35
out of a potential maximum of 50 and a predicted value of 0.
Similar findings in a common property resource environment are documented also by
Rocco and Warglien (1996), and Walker, Gardner, and Ostrom (1990). We will compare the
simulation results from genetic algorithms with the above results from human subjects.2
3 The artificial adaptive agents
Genetic algorithm (GA) agents interacts in the environment that was described in the
previous Section. While this Section introduces the GA decision makers along with the
parameter values used in the simulations, a full description of the working of a genetic
algorithm is given in Holland (1975), Goldberg(1989), Back (1996), and Mitchell (1996). For
issues specific to Economics see the excellent study of Dawid (1996).
2 Other six sessions were run under an experimental design with sanctions, where agents first decided a level of
resource use and then had the option to monitor other users and sanction those who exceeded a given threshold
of resource use (i.e. free riders). In one sanction treatment the cooperation level is above the Nash equilibrium
level (opposite than (a)). In all treatments (b) and (c) are observed. The experimental designs and results are
reported in Casari and Plott (2003).