Bayesian updating for self-assessment explains social dominance and the winner effect

Abstract

In animal contests, winners of previous contests often keep winning and losers keep losing. This coupling of previous experiences to future success, referred to as the winner-loser effect, plays a key role in stabilizing the resulting dominance hierarchies. Despite their importance, the cognitive mechanisms through which these effects occur are unknown. Identifying the mechanisms behind winner-loser effects requires identifying plausible models and generating predictions that can be used to test these alternative hypotheses. Winner-loser effects are often accompanied by a change in the aggressiveness of experienced individuals, which suggests individuals may be adjusting their self-assessment of their abilities after each contest. This updating of a prior estimate can be effectively described by Bayesian updating, and here we implement an agent-based model with continuous Bayesian updating to explore whether this is a plausible explanation of winner-loser effects. We first show that Bayesian updating reproduces known empirical results of typical dominance interactions. We then provide a series of testable predictions that can be used in future empirical work to distinguish Bayesian updating from simpler mechanisms. Our work demonstrates the utility of Bayesian updating as a mechanism to explain and ultimately predict changes in behaviour after salient social experiences.