Research Summary: GPS-Bench: A Governance Policy Benchmark for Automating Policy Analysis

A new benchmark, GPS-Bench, is proposed for evaluating governance policy simulations, particularly those utilizing Large Language Models (LLMs). This benchmark aims to enhance the validity of policy simulations by grounding actor behaviors and downstream impacts in empirical evidence drawn from legislative records, lobbying disclosures, regulatory documents, and economic data. It emphasizes reconstructing actors from historical data rather than relying on archetypal prompts, thereby facilitating more realistic and verifiable policy analysis.

Why it matters

This development is crucial for improving the robustness and reliability of policy analysis, particularly as artificial intelligence tools become more integrated into strategic decision-making. By providing an evidence-based method to validate simulated policy outcomes, it enables organizations and governments to anticipate effects more accurately and develop more resilient strategies.

Key insights

• Traditional policy analysis often focuses on predicting proposal passage, overlooking deeper implications.
• Comprehensive policy analysis necessitates understanding affected actors, their reactions, and subsequent outcomes.
• LLM-based policy simulations offer scalable modeling of complex policy processes.
• Establishing the validity of LLM simulations is challenging without comparing plausible behaviors to observed outcomes.
• GPS-Bench provides an evidence-grounded framework for governance policy simulation.
• The benchmark connects policies to relevant actors, their actions, and impacts using diverse public evidence sources.
• Actors in GPS-Bench are reconstructed from dated records, moving beyond generic archetypes.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.03553