SocialDropout: Dynamic Agent Dropout for Social Simulation

Large language model driven multi-agent social simulation frameworks enable realistic modeling of complex societal dynamics but incur substantial computational overhead due to dense agent participation and extensive interaction costs. To address this limitation, we propose SocialDropout, a reinforcement learning-based agent selection strategy within the AgentSociety framework, inspired by the AgentDropout paradigm, which dynamically identifies and samples informative agent subsets for each simulation round. Each agent is assigned an adaptive importance weight optimized to jointly minimize agent sparsity and computational cost measured by LLM calls, token consumption, and execution time - while preserving social interaction intensity within the environment. Extensive performance evaluation demonstrates that the proposed method significantly improves simulation efficiency and scalability. Moreover, ablation studies verify that high-level behavioral realism and outcome consistency are largely maintained despite substantial agent reduction. Our approach offers a practical and general optimization mechanism for large-scale LLM-based multi-agent social simulations under constrained computational budgets.