Social learning moderates the tradeoffs between efficiency, stability, and equity in group foraging

TL;DR

This study models collective foraging to show that an intermediate social learning range optimizes efficiency and stability, balancing exploration and exploitation, with implications for biological and robotic systems.

Contribution

It introduces a minimal model demonstrating how social learning range influences foraging trade-offs, revealing optimal regimes for efficiency and equity.

Findings

Optimal social learning range maximizes efficiency

Intermediate range reduces resource intake burstiness

Increasing range improves equity but reduces efficiency

Abstract

Collective foragers, from animals to robotic swarms, must balance exploration and exploitation to locate sparse resources efficiently. While social learning is known to facilitate this balance, how the range of information sharing shapes group-level outcomes remains unclear. Here, we develop a minimal collective foraging model in which individuals combine independent exploration, local exploitation, and socially guided movement. We show that foraging efficiency is maximized at an intermediate social learning range, where groups exploit discovered resources without suppressing independent discovery. This optimal regime also minimizes temporal burstiness in resource intake, reducing starvation risk. Increasing social learning range further improves equity among individuals but degrades efficiency through redundant exploitation. Introducing risky (negative) targets shifts the optimal range upward; in contrast, when penalties are ignored, randomly distributed negative cues can further enhance efficiency by constraining unproductive exploration. Together, these results reveal how local information rules regulate a fundamental trade-off between efficiency, stability, and equity, providing design principles for biological foraging systems and engineered collectives.