Bayes' rule and bias roles in the evolution of decision making

Abstract

Behavior is often plastic and the study of the functional basis of behavior should provide insights into the adaptive role of the mechanisms responsible for behavioral flexibility. Cognitive biases provide a window into the psychological mechanisms of behavior and a functional theory of behavioral mechanisms should be able to explain also the evolutionary significance of cognitive biases, that is, whether they should be seen as a solution to environmental problems or more as a by-product of adaptive rules. But how should such a theory be developed? In behavioral ecology, the "heuristics" approach has been prevailing. It proposes the rule first and then it investigates how the rule should be optimally modified to solve the problems posed by the environment, cognitive biases being a possible solution. Here, I explore an alternative approach, which derives rules from formal models of optimality. By focusing on mate choice, I present an optimal Bayesian decision-making model, based on the computation of the cumulative sums of the log-likelihood ratios that the quality of a prospective mate is either higher or lower than average. When uncertainty is high, log-likelihood ratios can be approximated to a linear function of mate quality; specifically, they may be expressed as deviations of the perceived quality from the population mean, weighted by the perceived assessment accuracy. I use this model to illustrate the different origins and, possibly, the different functional role of decision-making biases. More generally, I propose the model as an attempt to develop an evolutionary theory of behavioral mechanisms.