Can LLMs Emulate Human Belief Dynamics?

• URL: http://arxiv.org/abs/2605.18781v1
• Date: Tue, 05 May 2026 23:21:11 GMT
• Title: Can LLMs Emulate Human Belief Dynamics?
• Abstract summary: LLMs fail to capture initial human belief distributions and tend to be overall more conformist than humans.<n>They also take a nuanced approach to emulating human homophilic tendencies within networks.
• Score: 4.918358353535447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Can LLMs simulate how humans form and change beliefs in social networks? We put this to the test by replicating an established study on belief dynamics, evaluating 12 LLMs across multiple model families and parameter sizes. The answer is a clear no, and in systematic ways. LLMs fail to capture initial human belief distributions and tend to be overall more conformist than humans, shifting their responses to align with those around them. They also take a nuanced approach to emulating human homophilic tendencies within networks. Our findings carry a double payoff: they highlight fundamental properties of LLM behavior, and they raise a sharp warning against deploying LLMs as human proxies in social simulations.

Related papers

• Do LLMs Change Their Minds Like Humans? Diagnosing Human--LLM Divergence in Single-Turn Persuasion Judgments [10.717755637740948]
  Large language models (LLMs) are increasingly deployed as proxies for human participants in social simulations.<n>We conduct a systematic comparison using a naturally occurring online persuasion corpus.<n>Our results show that LLMs achieve only slight agreement with humans.
  arXiv  Detail & Related papers  (2026-08-30T14:08:06Z)
• LLMs struggle to simulate human belief updates in controlled environments [13.511441750037376]
  We test whether six LLMs can simulate individual human belief updates.<n>Some LLMs can match the human post-stance distribution, but only when given participants' initial stances.<n>Three systematic biases emerge across all models.
  arXiv  Detail & Related papers  (2026-07-30T15:19:18Z)
• Belief Coevolution in a Social Network of Generalist and Specialist Large Language Models [0.15655985886975654]
  We study the processes by which beliefs form and propagate among interacting large language models (LLMs)<n>CoevolveSim allows us to isolate and study three factors: domain specialization, social-role assignment, and social network structure.<n>We run 1,280 controlled simulations spanning four scenarios, two network structures, and 20 medical-indication statements.
  arXiv  Detail & Related papers  (2026-07-29T23:02:37Z)
• Verbalizing LLMs' assumptions to explain and control sycophancy [62.927670321859495]
  LLMs can be socially sycophantic, affirming users when they ask questions like "am I in the wrong?" rather than providing genuine assessment.<n>We present Verbalized Assumptions, a framework for eliciting these assumptions from LLMs.<n>Our work contributes a new understanding of assumptions as a mechanism for sycophancy.
  arXiv  Detail & Related papers  (2026-04-03T14:15:43Z)
• Social Simulations with Large Language Model Risk Utopian Illusion [61.358959720048354]
  We introduce a systematic framework for analyzing large language models' behavior in social simulation.<n>Our approach simulates multi-agent interactions through chatroom-style conversations and analyzes them across five linguistic dimensions.<n>Our findings reveal that LLMs do not faithfully reproduce genuine human behavior but instead reflect overly idealized versions of it.
  arXiv  Detail & Related papers  (2025-10-24T06:08:41Z)
• SocialEval: Evaluating Social Intelligence of Large Language Models [70.90981021629021]
  Social Intelligence (SI) equips humans with interpersonal abilities to behave wisely in navigating social interactions to achieve social goals.<n>This presents an operational evaluation paradigm: outcome-oriented goal achievement evaluation and process-oriented interpersonal ability evaluation.<n>We propose SocialEval, a script-based bilingual SI benchmark, integrating outcome- and process-oriented evaluation by manually crafting narrative scripts.
  arXiv  Detail & Related papers  (2025-06-01T08:36:51Z)
• Can LLMs Simulate Social Media Engagement? A Study on Action-Guided Response Generation [51.44040615856536]
  This paper analyzes large language models' ability to simulate social media engagement through action guided response generation.<n>We benchmark GPT-4o-mini, O1-mini, and DeepSeek-R1 in social media engagement simulation regarding a major societal event.
  arXiv  Detail & Related papers  (2025-02-17T17:43:08Z)
• Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina [7.155982875107922]
  Studies suggest large language models (LLMs) can exhibit human-like reasoning, aligning with human behavior in economic experiments, surveys, and political discourse.<n>This has led many to propose that LLMs can be used as surrogates or simulations for humans in social science research.<n>We assess the reasoning depth of LLMs using the 11-20 money request game.
  arXiv  Detail & Related papers  (2024-10-25T14:46:07Z)
• Cognitive phantoms in LLMs through the lens of latent variables [0.3441021278275805]
  Large language models (LLMs) increasingly reach real-world applications, necessitating a better understanding of their behaviour. Recent studies administering psychometric questionnaires to LLMs report human-like traits in LLMs, potentially influencing behaviour. This approach suffers from a validity problem: it presupposes that these traits exist in LLMs and that they are measurable with tools designed for humans. This study investigates this problem by comparing latent structures of personality between humans and three LLMs using two validated personality questionnaires.
  arXiv  Detail & Related papers  (2024-09-06T12:42:35Z)
• Do LLMs exhibit human-like response biases? A case study in survey design [66.1850490474361]
  We investigate the extent to which large language models (LLMs) reflect human response biases, if at all. We design a dataset and framework to evaluate whether LLMs exhibit human-like response biases in survey questionnaires. Our comprehensive evaluation of nine models shows that popular open and commercial LLMs generally fail to reflect human-like behavior.
  arXiv  Detail & Related papers  (2023-11-07T15:40:43Z)
• Can Large Language Models Transform Computational Social Science? [79.62471267510963]
  Large Language Models (LLMs) are capable of performing many language processing tasks zero-shot (without training data) This work provides a road map for using LLMs as Computational Social Science tools.
  arXiv  Detail & Related papers  (2023-04-12T17:33:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.