Large Language Models Simulating Deception and Coalition in Social Deduction Game

This study examines deceptive behaviors, coalition formation, and hidden-role reasoning in Large Language Models (LLMs) playing the social deduction game Secret Hitler. Through a case-study analysis of a simulated five-player game log—with three Loyalist and two Spy agents—we dissect dialogues and actions to reveal emergent strategies in asymmetric information environments. Key findings highlight Spies' tactical deception, such as framing statements to build false trust and selectively misreporting policy draws, contrasted with Loyalists' emphasis on transparency to foster genuine alliances. Coalition dynamics arise from aligned reasoning and endorsements, enabling Spies to reinforce covert strategies, while a policy progression table illustrates how bluffs influence round-by-round outcomes and trust erosion. Although LLMs demonstrate strategic adaptation and theory-of-mind inference, they exhibit limitations in subtle, incentive-aligned deception, often relying on explicit prompting. This analysis advances AI game-playing and LLM deception modeling by integrating empirical dialogue insights with broader implications for multi-agent alignment and social simulation.