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

2025-10-31

AI Summary

This study investigates how communication range modulates the tripartite trade-off among collective foraging efficiency, stability, and resource allocation fairness in social learning. Using agent-based modeling, we develop a stochastic search framework integrating social learning with area-restricted search (ARS), parameterized by a unified exploration–exploitation balance parameter ρ. We simulate three behavioral modes—exploration, exploitation, and directed walking—and find that intermediate ρ values optimize group-level efficiency; high ρ enhances fairness but increases performance volatility; and under negative-reward conditions, social learning buffers risk and improves collective adaptability. Crucially, we demonstrate that communication range systematically reshapes the multi-objective trade-off structure by regulating the spatial scale of information sharing. Our work provides a novel paradigm for understanding the co-emergence of robustness and fairness in distributed intelligent systems.

Technology Category

• Multiagent Systems: Agent Communication
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Social Networks and Social Media: Social mining and social search on the Web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

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, $ρ$, that balances exploration and exploitation at the group level. We quantify how $ρ$ influences group efficiency ($η$), temporal variability/burstiness ($B$), and agent variability/equity in resource distribution ($σ$), revealing a clear trade-off among these outcomes. When $ρ o 0$, agents explore independently, maximizing collective exploration. As $ρ$ increases, individuals preferentially exploit patches discovered by others: $η$ first rises and then declines, while $B$ shows the opposite trend. Group efficiency is optimized at interior $ρ$ values that balance exploration and exploitation. At the largest $ρ$, 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.

Problem

• Social learning balances efficiency, stability, and equity in foraging
• Communication distance affects collective search and resource coordination
• Behavioral modes govern exploration-exploitation trade-offs in group dynamics

Innovation

• Randomized search model integrates social learning with ARS
• Three behavioral modes governed by single parameter ρ
• Social learning mitigates risk through negative rewards