# Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models

arXiv: https://arxiv.org/abs/2410.06932 · HTML: https://mlsanity.com/paper/2410.06932/

## Key facts

- Cited by 2 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 2 of its 71 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## 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, 2 of 2)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors](https://mlsanity.com/paper/2408.08811/) (2024)

## Built on by (most cited first, 2 of 2)

- [EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants](https://mlsanity.com/paper/2502.20309/) (2025)
- [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025)

## Abstract (arXiv)

In this study, we propose LLM agents as a novel approach in behavioral strategy research, complementing simulations and laboratory experiments to advance our understanding of cognitive processes in decision-making. Specifically, we reproduce a human laboratory experiment in behavioral strategy using large language model (LLM) generated agents and investigate how LLM agents compare to observed human behavior. Our results show that LLM agents effectively reproduce search behavior and decision-making comparable to humans. Extending our experiment, we analyze LLM agents' simulated "thoughts," discovering that more forward-looking thoughts correlate with favoring exploitation over exploration to maximize wealth. We show how this new approach can be leveraged in behavioral strategy research and address limitations.