Our paper “Simulating Strategic Interactions with AI Agents” is now published open access in the Strategic Management Journal.
We introduce a framework for using LLM-based AI agents as synthetic subjects in strategy research. In an exploration-exploitation setting with strategic interdependence, the simulations reproduce patterns observed in human experiments and help identify boundary conditions and new hypotheses for subsequent validation.
- Read the article in Strategic Management Journal
- Download the open-access publisher PDF
- Explore the project page
- View the code on GitHub
An earlier version, Theorizing with Large Language Models, circulated as NBER Working Paper No. 33033.
Citation: Tranchero, M., Brenninkmeijer, C.-F., Murugan, A., & Nagaraj, A. (2026). Simulating strategic interactions with AI agents. Strategic Management Journal. https://doi.org/10.1002/smj.70112