Beyond Preset Identities: Selective Stance Accommodation and Interaction Reorganisation in Generative Agent Societies

Abstract:Generative agent societies simulate people with assigned roles, preferences and relationships. As agents exchange arguments and choose partners, they can revise their positions and reorganise discussion. Understanding these changes requires examining what they accept and how they continue to interact. Stance-change scores and communication totals describe the extent of change. However, the same stance movement can preserve or reverse an assigned preference, and frequent communication can support either agreement or continuing disagreement. We therefore examine the content of changed positions and the exchanges that strengthen particular partnerships. Using Computational Multi-Agent Society Experiments (CMASE), we combine stance measures, source evaluations and temporal networks with individual answers and messages. Study 1 compares seven conditions across ten GPT-4o runs per condition, with a separate interview collection covering four models. Study 2 follows one 75-step GPT-4o café simulation. Environmental rational persuasion yields the largest mean stance departure ($1.30\pm0.08$ on a 7-point scale), whereas economic emotional persuasion yields the highest low-trust stance-shift rate ($17.3\%\pm11.2\%$, with standard deviations across runs). In the separate interviews, eight environmental agents shift from 7 to 6, acknowledge economic concerns and rate the source 3. Their partial acceptance preserves the assigned environmental preference. In the café, a pair with zero earlier exchanges becomes the most frequent final-phase partnership, with 25 messages. Its members develop coordination proposals while disputing their implementation. These results show that partial acceptance can coexist with low source trust, and sustained coordination with continuing disagreement.

24 pages, 7 figures. arXiv admin note: substantial text overlap with arXiv:2508.17366

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)

arXiv:2603.23406 [cs.AI]

(or arXiv:2603.23406v3 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2603.23406 Focus to learn more arXiv-issued DOI via DataCite

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