Social learning shapes collective search by influencing how individuals use peer information. Empirical and computational studies show that optimal information sharing that is neither too localized nor too diffuse, can enhance resource detection and coordination. Building on these insights, we develop a randomized search model that integrates social learning with area-restricted search (ARS) to investigate how communication distance affects collective foraging. The model includes three behavioral modes: exploration, exploitation, and targeted walk, which are governed by a single parameter, $\rho$, that balances exploration and exploitation at the group level. We quantify how $\rho$ influences group efficiency ($\eta$), temporal variability/burstiness ($B$), and agent variability/equity in resource distribution ($\sigma$), revealing a clear trade-off among these outcomes. When $\rho \to 0$, agents explore independently, maximizing collective exploration. As $\rho$ increases, individuals preferentially exploit patches discovered by others: $\eta$ first rises and then declines, while $B$ shows the opposite trend. Group efficiency is optimized at interior $\rho$ values that balance exploration and exploitation. At the largest $\rho$, equality among agents is highest, but efficiency declines and burstiness is maximized too. Finally, by introducing negative rewards, we examine how social learning mitigates risk.

翻译：社会学习通过影响个体对同伴信息的使用方式塑造集体搜索行为。实证与计算研究表明，既不过度局部化也不过度扩散的最优信息共享能够提升资源发现与协调能力。基于这些发现，我们构建了一个结合社会学习与区域限制搜索（ARS）的随机搜索模型，以探究通信距离如何影响集体觅食。该模型包含三种行为模式：探索、利用与定向行走，这些模式由单一参数ρ控制，该参数在群体层面平衡探索与利用。我们量化了ρ如何影响群体效率（η）、时间变异性/突发性（B）以及资源分配中的个体变异性/公平性（σ），揭示了这些结果间明确的权衡关系。当ρ→0时，个体独立探索，最大化集体探索范围。随着ρ增大，个体优先利用其他个体发现的资源斑块：η先上升后下降，而B呈现相反趋势。群体效率在平衡探索与利用的中间ρ值处达到最优。在ρ最大时，个体间公平性最高，但效率下降且突发性也达到最大。最后，通过引入负向奖励，我们探讨了社会学习如何缓解风险。