ArticleProceedings of the National Academy of Sciences of the United States of America2023
The emergence of economic rationality of GPT.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Simulated responses from large language models are weak proxies for human economic preferences across 74 countries.Nature human behaviour · 2026Article
- Benchmarking Large Language Model Rationality Using Measurement Axioms.Research square · 2026Article
- LLM agents overcome the machine penalty when acting fairly but not when acting selfishly or altruistically.National science review · 2026Article
- A simple threshold captures the social learning of conventions.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A conversational multi-agent AI system for automated plant phenotyping.Nature communications · 2026Article
- Quantifying uncert-AI-nty: Testing the accuracy of LLMs' confidence judgments.Memory & cognition · 2026Article
- Network formation and dynamics among multi-LLMs.PNAS nexus · 2025Article
- Using large language models to categorize strategic situations and decipher motivations behind human behaviors.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cross-lingual effects of AI-generated content on human work.Scientific reports · 2025Article
- A publicly available benchmark for assessing large language models' ability to predict how humans balance self-interest and the interest of others.Scientific reports · 2025Article
- Comparing AI and human decision-making mechanisms in daily collaborative experiments.iScience · 2025Article
- Article
- Measuring gender and racial biases in large language models: Intersectional evidence from automated resume evaluation.PNAS nexus · 2025Article
- Relative Value Encoding in Large Language Models: A Multi-Task, Multi-Model Investigation.Open mind : discoveries in cognitive science · 2025Article
- Analysing public goods games using reinforcement learning: effect of increasing group size on cooperation.Royal Society open science · 2024Article
- Can large language models help predict results from a complex behavioural science study?Royal Society open science · 2024Article
- Strategic behavior of large language models and the role of game structure versus contextual framing.Scientific reports · 2024Article
- The impact of generative artificial intelligence on socioeconomic inequalities and policy making.PNAS nexus · 2024Review
- Can Generative AI improve social science?Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- AI emerges as the frontier in behavioral science.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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4 authors.
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Abstract
As large language models (LLMs) like GPT become increasingly prevalent, it is essential that we assess their capabilities beyond language processing. This paper examines the economic rationality of GPT by instructing it to make budgetary decisions in four domains: risk, time, social, and food preferences. We measure economic rationality by assessing the consistency of GPT's decisions with utility maximization in classic revealed preference theory. We find that GPT's decisions are largely rational in each domain and demonstrate higher rationality score than those of human subjects in a parallel experiment and in the literature. Moreover, the estimated preference parameters of GPT are slightly different from human subjects and exhibit a lower degree of heterogeneity. We also find that the rationality scores are robust to the degree of randomness and demographic settings such as age and gender but are sensitive to contexts based on the language frames of the choice situations. These results suggest the potential of LLMs to make good decisions and the need to further understand their capabilities, limitations, and underlying mechanisms.
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