[2603.19649] PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization

Computer Science > Social and Information Networks arXiv:2603.19649 (cs) [Submitted on 20 Mar 2026] Title:PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization Authors:Renhong Huang, Ning Tang, Jiarong Xu, Yuxuan Cao, Qingqian Tu, Sheng Guo, Bo Zheng, Huiyuan Liu, Yang Yang View a PDF of the paper titled PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization, by Renhong Huang and 8 other authors View PDF HTML (experimental) Abstract:Social platforms serve as central hubs for information exchange, where user behaviors and platform interventions jointly shape opinions. However, intervention policies like recommendation and content filtering, can unintentionally amplify echo chambers and polarization, posing significant societal risks. Proactively evaluating the impact of such policies is therefore crucial. Existing approaches primarily rely on reactive online A/B testing, where risks are identified only after deployment, making risk identification delayed and costly. LLM-based social simulations offer a promising pre-deployment alternative, but current methods fall short in realistically modeling platform interventions and incorporating feedback from the platform. Bridging these gaps is essential for building actionable frameworks to assess and optimize platform policies. To this end, we propose PolicySim, an LLM-based social simulation sandbox for the proactive assessment and optimization of intervention...