A common rule for decision-making in animal collectives across species

TL;DR

This paper proposes a unified Bayesian decision-making rule that explains diverse animal collective behaviors across species, showing that different counting systems emerge from a common social information use principle.

Contribution

It introduces a single Bayesian-based decision rule that accounts for various species-specific decision-making systems in animal collectives.

Findings

A unified decision model explains diverse animal behaviors.

The model fits data from zebrafish, ants, and sticklebacks.

Different counting rules are derived from a common Bayesian principle.

Abstract

A diversity of decision-making systems has been observed in animal collectives. In some species, choices depend on the differences of the numbers of animals that have chosen each of the available options, while in other species on the relative differences (a behavior known as Weber's law) or follow more complex rules. We here show that this diversity of decision systems corresponds to a single rule of decision-making in collectives. We first obtained a decision rule based on Bayesian estimation that uses the information provided by the behaviors of the other individuals to improve the estimation of the structure of the world. We then tested this rule in decision experiments using zebrafish (Danio rerio), and in existing rich datasets of argentine ants (Linepithema humile) and sticklebacks (Gasterosteus aculeatus), showing that a unified model across species can quantitatively explain the diversity of decision systems. Further, these results show that the different counting systems used by animals, including humans, can emerge from the common principle of using social information to make good decisions.