Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trust

• URL: http://arxiv.org/abs/2507.02197v1
• Date: Wed, 02 Jul 2025 23:30:51 GMT
• Title: Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trust
• Authors: Amogh Mannekote, Adam Davies, Guohao Li, Kristy Elizabeth Boyer, ChengXiang Zhai, Bonnie J Dorr, Francesco Pinto,
• Abstract summary: We investigate how consistently role-playing agents' stated beliefs correspond to their actual behavior during role-play.<n>We find systematic inconsistencies between LLMs' stated beliefs and the outcomes of their role-playing simulation.<n>These findings highlight the need to identify how and when LLMs' stated beliefs align with their simulated behavior.
• Score: 32.044592572217475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As LLMs are increasingly studied as role-playing agents to generate synthetic data for human behavioral research, ensuring that their outputs remain coherent with their assigned roles has become a critical concern. In this paper, we investigate how consistently LLM-based role-playing agents' stated beliefs about the behavior of the people they are asked to role-play ("what they say") correspond to their actual behavior during role-play ("how they act"). Specifically, we establish an evaluation framework to rigorously measure how well beliefs obtained by prompting the model can predict simulation outcomes in advance. Using an augmented version of the GenAgents persona bank and the Trust Game (a standard economic game used to quantify players' trust and reciprocity), we introduce a belief-behavior consistency metric to systematically investigate how it is affected by factors such as: (1) the types of beliefs we elicit from LLMs, like expected outcomes of simulations versus task-relevant attributes of individual characters LLMs are asked to simulate; (2) when and how we present LLMs with relevant information about Trust Game; and (3) how far into the future we ask the model to forecast its actions. We also explore how feasible it is to impose a researcher's own theoretical priors in the event that the originally elicited beliefs are misaligned with research objectives. Our results reveal systematic inconsistencies between LLMs' stated (or imposed) beliefs and the outcomes of their role-playing simulation, at both an individual- and population-level. Specifically, we find that, even when models appear to encode plausible beliefs, they may fail to apply them in a consistent way. These findings highlight the need to identify how and when LLMs' stated beliefs align with their simulated behavior, allowing researchers to use LLM-based agents appropriately in behavioral studies.

Related papers

• Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games [87.5673042805229]
  How large language models balance self-interest and collective well-being is a critical challenge for ensuring alignment, robustness, and safe deployment.<n>We adapt a public goods game with institutional choice from behavioral economics, allowing us to observe how different LLMs navigate social dilemmas.<n>Surprisingly, we find that reasoning LLMs, such as the o1 series, struggle significantly with cooperation.
  arXiv  Detail & Related papers  (2025-06-29T15:02:47Z)
• LLM-Based Social Simulations Require a Boundary [3.351170542925928]
  This position paper argues that large language model (LLM)-based social simulations should establish clear boundaries.<n>We examine three key boundary problems: alignment (simulated behaviors matching real-world patterns), consistency (maintaining coherent agent behavior over time), and robustness.
  arXiv  Detail & Related papers  (2025-06-24T17:14:47Z)
• Prompting is Not All You Need! Evaluating LLM Agent Simulation Methodologies with Real-World Online Customer Behavior Data [62.61900377170456]
  We focus on evaluating LLM's objective accuracy'' rather than the subjective believability'' in simulating human behavior.<n>We present the first comprehensive evaluation of state-of-the-art LLMs on the task of web shopping action generation.
  arXiv  Detail & Related papers  (2025-03-26T17:33:27Z)
• Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models [50.16340812031201]
  We show that large language models (LLMs) do not update their beliefs as expected from the Bayesian framework.<n>We teach the LLMs to reason in a Bayesian manner by training them to mimic the predictions of an optimal Bayesian model.
  arXiv  Detail & Related papers  (2025-03-21T20:13:04Z)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• Thinking Before Speaking: A Role-playing Model with Mindset [0.6428333375712125]
  Large Language Models (LLMs) are skilled at simulating human behaviors. These models tend to perform poorly when confronted with knowledge that the assumed role does not possess. We propose a Thinking Before Speaking (TBS) model in this paper.
  arXiv  Detail & Related papers  (2024-09-14T02:41:48Z)
• Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks [5.76230391989518]
  Using data from a human survey, we estimated a belief network encompassing 64 topics loading on nine non-overlapping latent factors. We then seeded LLM-based agents with an opinion on one topic, and assessed the alignment of its expressed opinions on remaining test topics with corresponding human data. Role-playing based on demographic information alone did not align LLM and human opinions, but seeding the agent with a single belief greatly improved alignment for topics related in the belief network, and not for topics outside the network.
  arXiv  Detail & Related papers  (2024-06-25T02:37:29Z)
• Evaluating Interventional Reasoning Capabilities of Large Language Models [58.52919374786108]
  Large language models (LLMs) are used to automate decision-making tasks.<n>In this paper, we evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention.<n>We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types.<n>These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts.
  arXiv  Detail & Related papers  (2024-04-08T14:15:56Z)
• Can Large Language Model Agents Simulate Human Trust Behavior? [81.45930976132203]
  We investigate whether Large Language Model (LLM) agents can simulate human trust behavior. GPT-4 agents manifest high behavioral alignment with humans in terms of trust behavior. We also probe the biases of agent trust and differences in agent trust towards other LLM agents and humans.
  arXiv  Detail & Related papers  (2024-02-07T03:37:19Z)
• Systematic Biases in LLM Simulations of Debates [12.933509143906141]
  We study the limitations of Large Language Models in simulating human interactions.<n>Our findings indicate a tendency for LLM agents to conform to the model's inherent social biases.<n>These results underscore the need for further research to develop methods that help agents overcome these biases.
  arXiv  Detail & Related papers  (2024-02-06T14:51:55Z)
• How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulation [46.42384207122049]
  We design SimulateBench to evaluate the believability of large language models (LLMs) when simulating human behaviors. Based on SimulateBench, we evaluate the performances of 10 widely used LLMs when simulating characters.
  arXiv  Detail & Related papers  (2023-12-28T16:51:11Z)
• Do LLM Agents Exhibit Social Behavior? [5.094340963261968]
  State-Understanding-Value-Action (SUVA) is a framework to systematically analyze responses in social contexts. It assesses social behavior through both their final decisions and the response generation processes leading to those decisions. We demonstrate that utterance-based reasoning reliably predicts LLMs' final actions.
  arXiv  Detail & Related papers  (2023-12-23T08:46:53Z)

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.