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.

翻译：从动物群体到机器人集群，集体觅食者必须在探索与利用之间取得平衡，以高效定位稀疏资源。尽管已知社会学习有助于维持这种平衡，但信息共享的范围如何影响群体层面的结果仍不明确。本文构建了一个最小化集体觅食模型，其中个体行为融合了独立探索、局部利用和社会引导移动。研究表明，在中等社会学习范围下觅食效率达到最大化，此时群体既能开发现有资源，又不会抑制独立发现。这种最优机制同时最小化了资源获取的时间突发性，从而降低了饥饿风险。进一步扩大社会学习范围虽能提升个体间公平性，却会因重复利用导致效率下降。引入高风险（负收益）目标会使最优范围上移；相反，当忽略惩罚机制时，随机分布的负向线索可通过限制无效探索进一步提升效率。这些结果共同揭示了局部信息规则如何调控效率、稳定性与公平性之间的根本权衡，为生物觅食系统与工程化集群提供了设计原则。