Investigating Prosocial Behavior Theory in LLM Agents under Policy-Induced Inequities

TL;DR

This paper introduces ProSim, a simulation framework to study how large language model agents exhibit prosocial behavior under various social conditions and policy-induced inequities, revealing impacts on norm propagation.

Contribution

The paper presents ProSim, a novel simulation framework for modeling prosocial behavior in LLM agents, and provides empirical insights into how inequities influence prosocial norms and behaviors.

Findings

LLM agents exhibit human-like prosocial behaviors in diverse scenarios.

Agents respond systematically to variations in inequity and enforcement costs.

Policy-induced inequities suppress prosocial behavior and facilitate norm erosion.

Abstract

As large language models (LLMs) increasingly operate as autonomous agents in social contexts, evaluating their capacity for prosocial behavior is both theoretically and practically critical. However, existing research has primarily relied on static, economically framed paradigms, lacking models that capture the dynamic evolution of prosociality and its sensitivity to structural inequities. To address these gaps, we introduce ProSim, a simulation framework for modeling the prosocial behavior in LLM agents across diverse social conditions. We conduct three progressive studies to assess prosocial alignment. First, we demonstrate that LLM agents can exhibit human-like prosocial behavior across a broad range of real-world scenarios and adapt to normative policy interventions. Second, we find that agents engage in fairness-based third-party punishment and respond systematically to variations in inequity magnitude and enforcement cost. Third, we show that policy-induced inequities suppress prosocial behavior, propagate norm erosion through social networks. These findings advance prosocial behavior theory by elucidating how institutional dynamics shape the emergence, decay, and diffusion of prosocial norms in agent-driven societies.