PolicySimEval: A Benchmark for Evaluating Policy Outcomes through Agent-Based Simulation
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arXiv
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| Format: | Preprint |
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2025
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| author | Kang, Jiaju Han, Puyu Zhang, Tian Gong, Luqi |
| author_facet | Kang, Jiaju Han, Puyu Zhang, Tian Gong, Luqi |
| contents | With the growing adoption of agent-based models in policy evaluation, a pressing question arises: Can such systems effectively simulate and analyze complex social scenarios to inform policy decisions? Addressing this challenge could significantly enhance the policy-making process, offering researchers and practitioners a systematic way to validate, explore, and refine policy outcomes. To advance this goal, we introduce PolicySimEval, the first benchmark designed to evaluate the capability of agent-based simulations in policy assessment tasks. PolicySimEval aims to reflect the real-world complexities faced by social scientists and policymakers. The benchmark is composed of three categories of evaluation tasks: (1) 20 comprehensive scenarios that replicate end-to-end policy modeling challenges, complete with annotated expert solutions; (2) 65 targeted sub-tasks that address specific aspects of agent-based simulation (e.g., agent behavior calibration); and (3) 200 auto-generated tasks to enable large-scale evaluation and method development. Experiments show that current state-of-the-art frameworks struggle to tackle these tasks effectively, with the highest-performing system achieving only 24.5\% coverage rate on comprehensive scenarios, 15.04\% on sub-tasks, and 14.5\% on auto-generated tasks. These results highlight the difficulty of the task and the gap between current capabilities and the requirements for real-world policy evaluation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_07853 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PolicySimEval: A Benchmark for Evaluating Policy Outcomes through Agent-Based Simulation Kang, Jiaju Han, Puyu Zhang, Tian Gong, Luqi Multiagent Systems Computers and Society With the growing adoption of agent-based models in policy evaluation, a pressing question arises: Can such systems effectively simulate and analyze complex social scenarios to inform policy decisions? Addressing this challenge could significantly enhance the policy-making process, offering researchers and practitioners a systematic way to validate, explore, and refine policy outcomes. To advance this goal, we introduce PolicySimEval, the first benchmark designed to evaluate the capability of agent-based simulations in policy assessment tasks. PolicySimEval aims to reflect the real-world complexities faced by social scientists and policymakers. The benchmark is composed of three categories of evaluation tasks: (1) 20 comprehensive scenarios that replicate end-to-end policy modeling challenges, complete with annotated expert solutions; (2) 65 targeted sub-tasks that address specific aspects of agent-based simulation (e.g., agent behavior calibration); and (3) 200 auto-generated tasks to enable large-scale evaluation and method development. Experiments show that current state-of-the-art frameworks struggle to tackle these tasks effectively, with the highest-performing system achieving only 24.5\% coverage rate on comprehensive scenarios, 15.04\% on sub-tasks, and 14.5\% on auto-generated tasks. These results highlight the difficulty of the task and the gap between current capabilities and the requirements for real-world policy evaluation. |
| title | PolicySimEval: A Benchmark for Evaluating Policy Outcomes through Agent-Based Simulation |
| topic | Multiagent Systems Computers and Society |
| url | https://arxiv.org/abs/2502.07853 |