title: The evolution of inefficiency in a simulated stag hunt.
abstract: 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.
authors.affiliation: Department of Psychology, University of North Carolina, Chapel Hill, NC 27514, USA. neil@unc.edu
authors.forename: J N
authors.initials: JN
authors.lastname: Bearden
mesh_terms: D000465:Algorithms; D005716:Game Theory; D005717:Games, Experimental; D005823:Genetics; D006801:Humans; D007858:Learning