Bayesian inference and simulation approaches improve the assessment of Elo-scores in the analysis of social behaviour

1. The construction of rank hierarchies based on agonistic interactions between two individuals (”dyads”) is an important component in the characterization of the social structure of groups. To this end, winner-loser matrices are typically created, which collapse the outcome of dyadic interactions over time, resulting in the loss of all information contained in the temporal domain. Methods that track changes in the outcome of dyadic interactions (such as ”Elo-scores”) are experiencing increasing interest. Critically, individual scores are not just based on the succession of wins and losses, but depend on the values of starting scores and an update (”tax”) coefficient. Recent studies improved existing methods by introducing a point estimation of these auxiliary parameters on the basis of a maximum likelihood (ML) 1 . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/160671 doi: bioRxiv preprint first posted online Jul. 7, 2017;