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

作者：A. Filippas, J. Horton, Benjamin S. Manning · 发表于：ACM Conference on Economics and Computation · 年份：2023 · DOI：10.1145/3670865.3673513 · 被引用次数：567 · 研究领域：Computer Science、Economics

Large language models---because of how they are trained and designed---are implicit computational models of humans---a homo silicus. Social scientists can use LLMs like economists use homo economicus: LLMs can be given endowments, information, preferences, and so on, and then their behavior can be explored in scenarios via simulation. We replicate four experiments using this approach and find qualitatively similar results to the original. Benefits of this approach include trying new variations for fresh insights, piloting studies via simulation, and searching for novel social science insights to test in the real world. The full version of the paper can be accessed at https://apostolos-filippas.com/papers/hs.pdf.