Who’s the Liar? Multi-Agents' Message Strength and User Judgment Under Ambiguity

Abstract

As multiple AI-generated or AI-mediated voices across digital environments, what may matter is not only the informational content of individual AI statements, but also how users interpret convergent AI opinions under ambiguity. In this pilot study, we investigate whether the message strength of convergent AI utterances shapes user perceptions and decision-making in a controlled multi-agent discussion task based on the Liar Game paradigm. Participants interacted with multiple AI agents and identified a “liar” who did not know the target keyword, while the agents’ messages during the discussion were manipulated to be either high-strength (confident, explicitly reasoned) or low-strength (hedged, hesitant). Results showed directional patterns suggesting that high-strength convergent messages increased perceived persuasiveness and social pressure, promoted switching toward the AI-designated target, and increased post-discussion confidence. Attitude clarity moderated these effects: users with lower clarity were more easily persuaded, whereas users with higher clarity showed lower trust toward strong AI consensus. These findings provide initial evidence that the rhetorical strength of convergent AI speech may function as a social influence mechanism, with implications for understanding AI-mediated opinion dynamics and designing safeguards that protect user autonomy in multi-agent systems.