Bayesian updating for self-assessment explains social dominance and winner-loser effects

In animal contests, winners of previous contests often keep winning and losers keep losing. These so-called ‘winner-loser effects’, can have dramatic impacts on future contests and seem to play a key role in stabilizing the resulting dominance hierarchies. However, the cognitive mechanisms through which these effects occur are unknown. 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 based on this newly gained information. This updating of a prior estimate is ideally described by Bayesian updating. Here we show that Bayesian updating provides a mechanism to explain why winner-loser effects occur and makes clear predictions for the behavior of individuals and social groups. We implement an agent-based model to compare Bayesian updating to other possible strategies. We first show that Bayesian updating reproduces empirical results of the behavior of individuals and groups in dominance interactions. We then provide a series of testable predictions that can be used in future empirical work to distinguish Bayesian updating from other potential mechanisms. Our work demonstrates the utility of Bayesian updating as a mechanism to explain and ultimately, predict changes in behavior after salient social experiences.