arXiv:2301.07543econ.GN
Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?
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Abstract
We argue that newly-developed large language models (LLMs), because of how they are trained and designed, are implicit computational models of humans -- a Homo silicus. LLMs can be used like economists use Homo economicus: they can be given endowments, information, preferences, and so on, and then their behavior can be explored in scenarios via simulation. Experiments using this approach, derived from Charness and Rabin (2002), Kahneman et al. (1986), Samuelson and Zeckhauser (1988), Oprea (2024b), and Horton (2025), show qualitatively similar results to the original, and when they differ, it is often generative for future research. We discuss potential applications, conceptual issues, and why this approach can inform the study of humans.
>The difference is that an LLM isn't 1000 different intelligences. It's one intelligence, being asked to pretend to be 1,000 different people. No it's not. It does no good for a Language Model to configure a global persona. It needs to be able to predict text from wildly varying…
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