Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?

Fuente: arXiv
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Autores principales: Horton, John J., Filippas, Apostolos, Manning, Benjamin S.
Formato: Preprint
Publicado: 2023
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author Horton, John J.
Filippas, Apostolos
Manning, Benjamin S.
author_facet Horton, John J.
Filippas, Apostolos
Manning, Benjamin S.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2301_07543
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?
Horton, John J.
Filippas, Apostolos
Manning, Benjamin S.
General Economics
Economics
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.
title Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?
topic General Economics
Economics
url https://arxiv.org/abs/2301.07543