PoliticalSim: Multi-Layer Political Dynamics Simulation by LLM Agents

Traditional political science analyzed political decision making processes, including voters’ electoral choices, legislative deliberation by selected representatives in parliament, and institutional interaction between parliament and other actors such as the bureaucracy and interest group, by modeling economically rational individuals who act to maximize their utility. These political theories are grounded in mathematically rigorous economic theories such as game theory, neglecting individual's diversity and social interactions, lacking controlled experiments because randomized intervention in real societies is almost impossible. Advances in artificial intelligence have made it possible to reproduce patterns of human interaction that mathematical theory alone could not capture by using Agent-Based Modeling. However, it remains unclear to what extent LLMs can comprehend complex political institutions and replicate human political behavior, and prior work didn't provide a simulation environment for controlled experiments that enable or disable individual mechanisms while holding other conditions constant. In this study, we propose PoliticalSim, a novel framework for simulating the political activities of voters, parliament representatives, and institutional interactions, enabling controlled experiments that enable and disable individual political factors while holding other conditions constant. By this framework, we show that LLMs can reproduce some important phenomena observed in political science in both hypothetical and real world grounded settings, and we experimentally identify which factors affect the final outcomes.