Evolutionary Foundation of Bounded Rationality in a Financial Market
This paper provides a foundation for decision making with bounded rationality in economic entities from the viewpoint of evolutionary theory. To this end, first, we conducted an investment test with participants to extract a behavioral learning model for activities with bounded rationality. We found that the decision-making model obtained from this behavioral science approach has characteristics that are frequently seen in the results of observations of instances of bounded rationality. Furthermore, the model presents some well-known biases in decision making, such as profit-and-loss asymmetry in risk avoidance, reference point dependence, and the asset effect. Next, using agent-based simulations, we examined whether our behavioral-learning model for activities had the capacity to become a stable strategy in a market environment where selection pressure exists. When, in response to maximum loss, a drawdown is set as an evaluation criterion for selection, the results of our simulations imply the following: 1) our decision-making model with bounded rationality has the capacity to become a stable evolutionary strategy and 2) entities with bounded rationality can survive in a competitive market. These results are antithetical to the evolutionary explanations used as a basis for rationality in traditional economics, and they indicate the possibility that many well-known biases in decision making can be derived evolutionarily from a single criterion.
agent-based simulation artificial market evolution experimental economies sequential investment task