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Simulating strategic interactions with AI agents
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DOI：10.1002/smj.70112.png)
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Abstract
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We explore how Large Language Models (LLMs) can serve as synthetic subjects to inform strategy research. We introduce a framework for designing and running simulated experiments with LLM-powered agents. We argue that this approach is useful for rapid, low-cost prototyping of human experiments and for generating novel hypotheses. We apply the framework to the exploration–exploitation dilemma and show that LLM-based experiments reproduce patterns observed among human participants. We then vary parameters and boundary conditions to illustrate how the same setup can support design iteration and surface hypotheses about when and why established results change. In the conclusion, we discuss the promise and limitations of artificial intelligence agents as “model organisms” for strategy.
Keywords:
AI agents
exploration and exploitation
Large Language Models
strategic interactions
strategy experiments
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