arXiv Computation and Language By Rasika Muralidharan, Haewoon Kwak, Jisun An

Behavior is Not Enough: A Mechanism-Based Evaluation of Social Norm Emergence in LLM Societies

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The paper argues that observing only behavior is insufficient to identify social norms in large language model (LLM) societies. It introduces an evaluation framework that also measures agents’ reported empirical and normative expectations, revealing that expectation elicitation boosts cooperation, social learning stabilizes behavior, and social selection identifies cooperators but offers limited reinforcement. The study shows that similar cooperative outcomes can stem from distinct underlying mechanisms and that expectations can be used to attribute each mechanism’s contribution.

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arXiv Computation and Language
Sep 3

AI agents reshape consensus formation in human groups

The study investigates how large language model (LLM) agents influence consensus formation in mixed human‑AI groups during a collaborative description game. Three regimes emerge: low agent proportions lead to human‑led consensus, intermediate proportions disrupt convergence, and high proportions produce strong, agent‑led consensus. The resulting consensus differs in semantic grounding and communicative form, with human‑led consensus being concrete and holistic, and agent‑led consensus being abstract and geometrically segmented.

By Lin Chen, Ziyi Liu, Xia Hu, Yong Li