# Collective learning from individual experiences and information transfer during group foraging

## TL;DR

This study uses an agent-based model to explore how individual memory and information transfer enhance collective foraging success and coordination in animal groups, especially in complex environments.

## Contribution

It introduces a model demonstrating how memory and information fluxes lead to collective learning and improved foraging in groups with diverse experiences.

## Key findings

- Memory and information transfer improve group cohesion and resource localization.
- Suppression of memory or information fluxes reduces coordination.
- The model predicts behaviors that can be tested empirically and applied in robotics.

## Abstract

Living in groups brings benefits to many animals, such as a protection against predators and an improved capacity for sensing and making decisions while searching for resources in uncertain environments. A body of studies has shown how collective behaviors within animal groups on the move can be useful for pooling information about the current state of the environment. The effects of interactions on collective motion have been mostly studied in models of agents with no memory. Thus, whether coordinated behaviors can emerge from individuals with memory and different foraging experiences is still poorly understood. By means of an agent based model, we quantify how individual memory and information fluxes can contribute to improving the foraging success of a group in complex environments. In this context, we define collective learning as a coordinated change of behavior within a group resulting from individual experiences and information transfer. We show that an initially scattered population of foragers visiting dispersed resources can gradually achieve cohesion and become selectively localized in space around the most salient resource sites. Coordination is lost when memory or information transfer among individuals is suppressed. The present modelling framework provides predictions for empirical studies of collective learning and could also find applications in swarm robotics and motivate new search algorithms based on reinforcement.