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

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.