Journal
BEHAVIOR RESEARCH METHODS INSTRUMENTS & COMPUTERS
Volume 33, Issue 2, Pages 124-129Publisher
PSYCHONOMIC SOC INC
DOI: 10.3758/BF03195357
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We used genetic algorithms to evolve populations of reinforcement learning (Q-learning) agents to play a repeated two-player symmetric coordination game under different risk conditions and found that evolution steered our simulated populations to the Pareto inefficient equilibrium under high-risk conditions and to the Pareto efficient equilibrium under low-risk conditions. Greater degrees of forgiveness and temporal discounting of future returns emerged in populations playing the low-risk game. Results demonstrate the utility of simulation to evolutionary psychology.
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