Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?
John Horton
No 31122, NBER Working Papers from National Bureau of Economic Research, Inc
Abstract:
Newly-developed large language models (LLM)—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, Knetsch and Thaler (1986), and Samuelson and Zeckhauser (1988) show qualitatively similar results to the original, but it is also easy to try variations for fresh insights. LLMs could allow researchers to pilot studies via simulation first, searching for novel social science insights to test in the real world.
JEL-codes: D0 (search for similar items in EconPapers)
Date: 2023-04
New Economics Papers: this item is included in nep-big and nep-cmp
Note: LS PR
References: Add references at CitEc
Citations: View citations in EconPapers (52)
Downloads: (external link)
http://www.nber.org/papers/w31122.pdf (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:31122
Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w31122
Access Statistics for this paper
More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().