Can Generative AI agents behave like humans? Evidence from laboratory market experiments

• URL: http://arxiv.org/abs/2505.07457v1
• Date: Mon, 12 May 2025 11:44:46 GMT
• Title: Can Generative AI agents behave like humans? Evidence from laboratory market experiments
• Authors: R. Maria del Rio-Chanona, Marco Pangallo, Cars Hommes,
• Abstract summary: We explore the potential of Large Language Models to replicate human behavior in economic market experiments.<n>We compare LLM behavior to market dynamics observed in laboratory settings and assess their alignment with human participants' behavior.<n>These results suggest that LLMs hold promise as tools for simulating realistic human behavior in economic contexts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We explore the potential of Large Language Models (LLMs) to replicate human behavior in economic market experiments. Compared to previous studies, we focus on dynamic feedback between LLM agents: the decisions of each LLM impact the market price at the current step, and so affect the decisions of the other LLMs at the next step. We compare LLM behavior to market dynamics observed in laboratory settings and assess their alignment with human participants' behavior. Our findings indicate that LLMs do not adhere strictly to rational expectations, displaying instead bounded rationality, similarly to human participants. Providing a minimal context window i.e. memory of three previous time steps, combined with a high variability setting capturing response heterogeneity, allows LLMs to replicate broad trends seen in human experiments, such as the distinction between positive and negative feedback markets. However, differences remain at a granular level--LLMs exhibit less heterogeneity in behavior than humans. These results suggest that LLMs hold promise as tools for simulating realistic human behavior in economic contexts, though further research is needed to refine their accuracy and increase behavioral diversity.

Related papers

• Arbiters of Ambivalence: Challenges of Using LLMs in No-Consensus Tasks [52.098988739649705]
  This study examines the biases and limitations of LLMs in three roles: answer generator, judge, and debater.<n>We develop a no-consensus'' benchmark by curating examples that encompass a variety of a priori ambivalent scenarios.<n>Our results show that while LLMs can provide nuanced assessments when generating open-ended answers, they tend to take a stance on no-consensus topics when employed as judges or debaters.
  arXiv  Detail & Related papers  (2025-05-28T01:31:54Z)
• Comparing Exploration-Exploitation Strategies of LLMs and Humans: Insights from Standard Multi-armed Bandit Tasks [6.355245936740126]
  Large language models (LLMs) are increasingly used to simulate or automate human behavior in sequential decision-making tasks.<n>We focus on the exploration-exploitation (E&E) tradeoff, a fundamental aspect of dynamic decision-making under uncertainty.<n>We find that reasoning shifts LLMs toward more human-like behavior, characterized by a mix of random and directed exploration.
  arXiv  Detail & Related papers  (2025-05-15T02:09:18Z)
• 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)
• LLM Agents Display Human Biases but Exhibit Distinct Learning Patterns [0.0]
  We investigate the choice patterns of Large Language Models (LLMs) in the context of Decisions from Experience tasks.<n>We find that on the aggregate, LLMs appear to display behavioral biases similar to humans.<n>However, more nuanced analyses of the choice patterns reveal that this happens for very different reasons.
  arXiv  Detail & Related papers  (2025-03-13T10:47:03Z)
• 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)
• Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models [0.0]
  We reproduce a human laboratory experiment in behavioral strategy using large language model (LLM) generated agents. Our results show that LLM agents effectively reproduce search behavior and decision-making comparable to humans.
  arXiv  Detail & Related papers  (2024-10-09T14:26:20Z)
• DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation [75.81096662788254]
  Large Language Models (LLMs) are scalable and economical evaluators.<n>The question of how reliable these evaluators are has emerged as a crucial research question.<n>We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
  arXiv  Detail & Related papers  (2024-05-24T08:12:30Z)
• 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)
• Explaining Large Language Models Decisions Using Shapley Values [1.223779595809275]
  Large language models (LLMs) have opened up exciting possibilities for simulating human behavior and cognitive processes. However, the validity of utilizing LLMs as stand-ins for human subjects remains uncertain. This paper presents a novel approach based on Shapley values to interpret LLM behavior and quantify the relative contribution of each prompt component to the model's output.
  arXiv  Detail & Related papers  (2024-03-29T22:49:43Z)
• A Theory of LLM Sampling: Part Descriptive and Part Prescriptive [53.08398658452411]
  Large Language Models (LLMs) are increasingly utilized in autonomous decision-making.<n>We show that this sampling behavior resembles that of human decision-making.<n>We show that this deviation of a sample from the statistical norm towards a prescriptive component consistently appears in concepts across diverse real-world domains.
  arXiv  Detail & Related papers  (2024-02-16T18:28:43Z)
• 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)
• 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)

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.