Modelling collective decision-making: Insights into collective anti-predator behaviors from an agent-based approach

Abstract

Collective decision-making is a widespread phenomenon across organisms. Studying how animal societies make group decisions to the mutual benefit of group members, while avoiding exploitation by cheaters, can provide unique insights into the underlying cognitive mechanisms. As a step toward dissecting the proximate mechanisms that underpin collective decision-making across animals, we developed an agent-based model of antipredatory alarm signaling and mobbing during predator-prey encounters. Such collective behaviors occur in response to physical threats in many distantly related species with vastly different cognitive abilities, making it a broadly important model behavior. We systematically assessed under which quantitative contexts potential prey benefit from three basic strategies: predator detection, signaling about the predator (e.g., alarm calling), and retreating from vs. approaching the predator. Collective signaling increased survival rates over individual predator detection in several scenarios. Signaling sometimes led to fewer prey detecting the predator but this effect disappeared when prey animals that had seen the predator both signaled and approached it, as in mobbing. Critically, our results highlight that collective decision-making in response to a threat can emerge from simple rules without needing a central leader or needing to be under conscious control.