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

Figure 2: Genetic algorithms and randomness

Notes: Nash equilibrium: prediction with selfish, perfectly rational agents; Human subjects: average of 4
experimental sessions; Genetic algorithm agents: selfish, boundedly rational agents (T=64,τ=32, average over 100
simulated runs, v.5.0); Zero-intelligence agents: random draws from a uniform distribution (average over 100
simulated runs v.5.6); xi ~U[0,θ] with x_i iid, θ=50; Noisy Nash agents: are ZI with probability p and are best
responders to other Noisy Nash agents with probability (1-p).

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