Conditionally Risky Behavior vs Expected Value Maximization in Evolutionary Games

Inspired by much empirical evidence of human decisionmaking under risk that does not coincide with expected value maximization, much effort has been invested into the development of descriptive theories of human decision-making involving risk (e.g. Prospect Theory, Regret Theory, SP/A Theory). An open question is how behavior corresponding to these descriptive models could have been learned or arisen evolutionarily, as the described behavior differs from expected value maximization. We believe that the answer to this question lies, at least in part, in the interplay between risk-taking and sequentiality of choice in evolutionary environments. We provide simulation results for evolutionary game environments where sequential decisions are made between risky and safe choices. Our results show there are evolutionary games in which agents that are sometimes risk-prone and sometimes risk-averse can outperform agents that make decisions solely based on the maximization of the local expected values of the outcomes.