Deception and Communication in Autonomous Multi-Agent Systems: An Experimental Study with Among Us

Abstract

As large language models are deployed as autonomous agents, their capacity for strategic deception raises core questions for coordination, reliability, and safety in multi-goal, multi-agent systems. We study deception and communication in L2LM agents through the social deduction game Among Us, a cooperative-competitive environment. Across 1,100 games, autonomous agents produced over one million tokens of meeting dialogue. Using speech act theory and interpersonal deception theory, we find that all agents rely mainly on directive language, while impostor agents shift slightly toward representative acts such as explanations and denials. Deception appears primarily as equivocation rather than outright lies, increasing under social pressure but rarely improving win rates. Our contributions are a large-scale analysis of role-conditioned deceptive behavior in LLM agents and empirical evidence that current agents favor low-risk ambiguity that is linguistically subtle yet strategically limited, revealing a fundamental tension between truthfulness and utility in autonomous communication.

Figures (9)

• Figure 1: Example discussion phase of four AI agents during a round of AmongUs. The imposter-agent (red) is unknown to the crew-agents (pink, green, blue) and is attempting to deceive them to win the game.
• Figure 2: Overview of the Among Us simulation framework. Games begin with random initialization of player roles ($X$=#Crewmates, $Y$=#Imposters) and tasks. During gameplay, agents act sequentially at discrete timesteps (movement, task completion, impostor actions). When a dead body is reported or an emergency meeting is called, a discussion phase is triggered: each surviving agent, including impostors, contributes up to $X$ rounds of utterances. After discussion, a vote is held; if a plurality is reached, the selected player is ejected. The game continues until one of three termination conditions is met: (1) impostors and crew reach parity, (2) all impostors are ejected, or (3) all crew tasks are completed.
• Figure 3: Win outcomes by role across all configurations. Each curve shows the empirical cumulative distribution (ECDF) of games ending in crew or impostor victory. Impostors win more often overall, and their advantage increases with the number of impostors in play.
• Figure 4: Empirical cumulative distributions (ECDFs) of discussions and ejections over rounds for different game configurations. Larger crews delay the onset of both discussions and ejections, flattening the cumulative curve despite more total rounds.
• Figure 5: Logistic regression coefficients predicting crew victory (vs. impostor victory). Points show log-odds estimates with 95% confidence intervals. Crew size and the number of ejections are positively associated with crew wins, whereas additional impostors sharply reduce success. Communication frequency and verbosity have little or no effect, indicating that talk alone is insufficient without decisive collective action.