Large Language Models as Simulated Economic Agents: References#
2. Affiliation:#
None
3. Keywords:#
Large Language Models, simulation, economics, homo silicus, experiments
4. Urls:#
arXiv preprint arXiv:2208.10264, Github:None
5. Summary:#
(1): The article explores the potential use of Large Language Models (LLMs) as simulated economic agents for research purposes.
(2): Past methods in economics have commonly relied on the homo economicus model, which has limitations. The proposed approach using LLMs as simulated agents provides a more realistic and flexible way to study economic behavior.
(3): The article outlines how LLMs can be trained and designed to simulate human behavior in economics experiments, similar to the approach of Charness and Rabin (2002), Kahneman, Knetsch and Thaler (1986), and Samuelson and Zeckhauser (1988).
(4): The article suggests that LLMs provide a promising new approach to study economic behavior through simulation, but further research is necessary to fully understand their potential and limitations. No specific task or performance is mentioned.
6. Conclusion:#
(1): The significance of this piece of work lies in the potential use of Large Language Models (LLMs) as simulated economic agents for research purposes, providing a more realistic and flexible way to study economic behavior.
(2): Innovation point: The proposed approach using LLMs as simulated agents provides a new and promising way to study economic behavior through simulation, overcoming limitations of past methods. (3): Performance: The experiments using GPT3 AIs as experimental subjects showed promising results in recovering findings from experiments with actual humans in a cheap and scalable way. (4): Workload: The article suggests that further research is necessary to fully understand the potential and limitations of LLMs as simulated economic agents.