Can agent-based simulations reliably evaluate policy outcomes?
PolicySimEval: A Benchmark for Evaluating Policy Outcomes through Agent-Based Simulation
February 13, 2025
https://arxiv.org/pdf/2502.07853This paper introduces PolicySimEval, a benchmark designed to evaluate the effectiveness of agent-based models (ABMs), particularly relevant to LLM-powered agents, in simulating and analyzing complex policy scenarios. It tests how well these models can inform real-world policy decisions.
Key points for LLM-based multi-agent systems:
- Benchmark Focus: PolicySimEval directly addresses the need to evaluate LLM-driven multi-agent systems in realistic policy scenarios, moving beyond just flexible modeling environments.
- Real-World Relevance: The benchmark uses real-world data and expert-created solutions, ensuring the evaluations are grounded in practical policy challenges.
- Comprehensive Evaluation: It employs a multi-faceted evaluation approach, covering task completion, behavior calibration (crucial for agent interactions), language quality and ethical considerations (key for LLMs), outcome alignment, and system performance.
- ReAct and ReAct-RAG Agents: Experiments used LLM-based agents (GPT-4 and Llama 3.1 70B) within the ReAct and retrieval-augmented ReAct-RAG frameworks, highlighting the applicability of these approaches to policy simulation.
- Current Limitations: Results show current LLM-based agents still struggle with the complexities of policy evaluation, emphasizing the need for further research and development in this area. This provides a clear direction for future work involving LLM-based multi-agent systems applied to real-world problems.