[2509.18052] The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies

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Computer Science > Computation and Language

arXiv:2509.18052 (cs)

[Submitted on 22 Sep 2025 (v1), last revised 6 Apr 2026 (this version, v3)]

Title:The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies

Authors:Jiaxu Zhou, Jen-tse Huang, Xuhui Zhou, Man Ho Lam, Xintao Wang, Hao Zhu, Wenxuan Wang, Maarten Sap<br>View a PDF of the paper titled The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies, by Jiaxu Zhou and 7 other authors

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Abstract:Large language models (LLMs) are increasingly deployed to simulate human collective behaviors, yet the methodological rigor of these "AI societies" remains under-explored. Through a systematic audit of 39 recent studies, we identify six pervasive flaws-spanning agent profiles, interaction, memory, control, unawareness, and realism (PIMMUR). Our analysis reveals that 89.7% of studies violate at least one principle, undermining simulation validity. We demonstrate that frontier LLMs correctly identify the underlying social experiment in 50.8% of cases, while 61.0% of prompts exert excessive control that pre-determines outcomes. By reproducing five representative experiments (e.g., telephone game), we show that reported collective phenomena often vanish or reverse when PIMMUR principles are enforced, suggesting that many "emergent" behaviors are methodological artifacts rather than genuine social dynamics. Our findings suggest that current AI simulations may capture model-specific biases rather than universal human social behaviors, raising critical concerns about the use of LLMs as scientific proxies for human society.

Comments:<br>13 pages, 9 figures, 3 tables; add more papers in our systematic audit (39 in total)

Subjects:

Computation and Language (cs.CL); Computers and Society (cs.CY)

Cite as:<br>arXiv:2509.18052 [cs.CL]

(or<br>arXiv:2509.18052v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2509.18052

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arXiv-issued DOI via DataCite

Submission history<br>From: Jen-Tse Huang [view email]<br>[v1]<br>Mon, 22 Sep 2025 17:27:29 UTC (1,771 KB)

[v2]<br>Mon, 26 Jan 2026 14:24:09 UTC (1,963 KB)

[v3]<br>Mon, 6 Apr 2026 17:12:41 UTC (1,966 KB)

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