Optimizing the location of the colony of foragers with Collective Learning

TL;DR

This paper models collective learning in foraging animal groups using continuous random walks, revealing how agreement levels and demographic ratios influence optimal colony placement and search efficiency.

Contribution

It introduces a novel model of collective foraging that incorporates agreement levels and demographic ratios to optimize colony location and search time.

Findings

Optimal position and time depend on agreement levels.

Search efficiency improves with certain agreement ranges.

Optimal mean time scales linearly with death-to-birth ratio.

Abstract

Animal groups collaborate with one another throughout their lives to better comprehend their surroundings. Here, we try to model, using continuous random walks, how the entire process of birth, reproduction, and death might impact the searching process. We attempt to simulate an ecosystem where the post-reproductive foragers leave their colonies to discover where the targets are while others stay and breed at the base. Actually, a group of foragers searches for a location from where they access the targets for food supply. Particularly, we have explored a hypothetical situation in which the relocation to the new position depends on the agreement level of the species as well as an additional waiting time due to this agreement level. In this backdrop, detailed numerical results reveal that searching for an optimal position at an optimal mean time can be captured for a suitable range of the agreement level. We have also shown, for a given agreement level, the optimal mean time linearly increases with the Death-to-Birth ratio.