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.

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.

Traditional policy analysis often focuses on predicting proposal passage, overlooking deeper implications.

Forward consideration, not a verified fact.