mlsanityData to 7 Oct 2026

Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models

arXiv 2410.06932 ↗First posted 9 Oct 2024econ.GN cs.AI q-fin.ECcited by 2

On the map: LLM agents and tool use → Social simulation and multi-agent debate

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.

Key facts. Cited by 2 papers in the map (data through 7 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.

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2 of 71 references.

References · 2

  1. 2017Attention Is All You Needcited by 59,932
  2. 2024Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investorscited by 8

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