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bioRxiv · 10.1101/2024.12.03.626550

Social patch foraging theory in an egalitarian group

Blum Moyse, L.·El Hady, A.
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Abstract

Foraging is a widespread behavior, and being part of a group may bring several benefits compared to solitary foraging, such as collective pooling of information and reducing environmental uncertainty. Often theoretical models of collective behavior use coarse-grained representations, or are too complex for analytical treatment, and generally do not take into account the noisy decision making process implemented by individual agents. This calls for the development of a mechanistic, analytically tractable, and stochastic framework to study the underlying processes of social foraging, tying the microscopic to the macroscopic levels. Based on an evidence accumulation framework, we developed a model of patch-leaving decisions in a large egalitarian group. Across a variety of environmental statistics and information sharing mechanisms, we were able to analytically derive optimal agent strategies. The environmental statistics considered are either two non-depleting or several successive depleting patches. The social information sharing mechanisms are either through observation of others food rewards or through belief sharing, with continuous sharing, pulsatile observation of others departures or arrivals, or through counting the number of individuals in a patch. Throughout all these conditions, we quantified how cohesive a group is over time, how much time agents spend on average in a patch and what are their group equilibrium dynamics. We found that social coupling strongly modulates these features across a variety of environmental statistics. This general modeling framework is crucial to both designing social foraging experiments and generating hypotheses that can be tested. Moreover, this framework can be extended to groups exhibiting hierarchical relations.

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Blum Moyse, L., El Hady, A.. 2024-12-04. Social patch foraging theory in an egalitarian group. https://doi.org/10.1101/2024.12.03.626550

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