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

arXiv: https://arxiv.org/abs/2505.07457 · HTML: https://mlsanity.com/paper/2505.07457/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 12 May 2025.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 10 of 10)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities](https://mlsanity.com/paper/2310.10436/) (2023)
- [The Political Preferences of LLMs](https://mlsanity.com/paper/2402.01789/) (2024)
- [Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena](https://mlsanity.com/paper/2310.05746/) (2023)
- [Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4](https://mlsanity.com/paper/2309.17277/) (2023)
- [CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents](https://mlsanity.com/paper/2310.17512/) (2023)
- [Algorithmic Collusion by Large Language Models](https://mlsanity.com/paper/2404.00806/) (2024)
- [Epidemic Modeling with Generative Agents](https://mlsanity.com/paper/2307.04986/) (2023)
- [Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations](https://mlsanity.com/paper/2504.03274/) (2025)

## Built on by (most cited first, 10 of 10)

- [This human study did not involve human subjects: Validating LLM simulations as behavioral evidence](https://mlsanity.com/paper/2602.15785/) (2026)
- [Simulating Society Requires Simulating Thought](https://mlsanity.com/paper/2506.06958/) (2025)
- [LLM-Based Social Simulations Require a Boundary](https://mlsanity.com/paper/2506.19806/) (2025)
- [Strategic Exploitation in LLM Agent Markets: A Simulation Framework for E-Commerce Trust](https://mlsanity.com/paper/2605.10059/) (2026)
- [Competitive Market Behavior of LLMs](https://mlsanity.com/paper/2609.02580/) (2026)
- [Recalibrating the Compass: Integrating Large Language Models into Classical Research Methods](https://mlsanity.com/paper/2505.19402/) (2025)
- [LLM Consumer Behavior Theory: Foundations of a Novel Research Field](https://mlsanity.com/paper/2606.18005/) (2026)
- [Will Scaling Improve Social Simulation with LLMs?](https://mlsanity.com/paper/2607.02464/) (2026)
- [Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races](https://mlsanity.com/paper/2608.01193/) (2026)
- [Monocultural Biases: Correlated biases in large language models lead to unequal systemic exclusion rates in hiring](https://mlsanity.com/paper/2609.22169/) (2026)

## Abstract (arXiv)

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.