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In animal contests, winners of previous social contests are more likely to win in future fights, while losers similarly keep losing. These ‘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 underlying cognitive mechanisms 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. We implement an agent-based model to compare Bayesian updating to other possible strategies and show that Bayesian updating reproduces empirical results of behavior, while also providing testable predictions that can be used in future empirical work.

Link for zoom: https://ucdavis.zoom.us/j/92718892837