用 AI 与类型向量建模人类行为

Economics > Theoretical Economics arXiv:2608.18265 (econ) [Submitted on 18 Aug 2026 (v1), last revised 17 Sep 2026 (this version, v3)] Title:Modeling Human Behavior with Type Vectors Using AI Authors:Matthew O. Jackson, Benjamin S. Manning, Yutong Xie, Walter Yuan, Qiaozhu Mei View a PDF of the paper titled Modeling Human Behavior with Type Vectors Using AI, by Matthew O. Jackson and Benjamin S. Manning and Yutong Xie and Walter Yuan and Qiaozhu Mei View PDF HTML (experimental) Abstract:We introduce a general, easy-to-implement AI-based modeling technique for analyzing human behavior. A key feature of this approach, which contrasts with existing modeling techniques, is that it combines the flexibility and interpretability of natural language with a mathematical structure that can be fitted to data and easily analyzed. We assign a large language model a vector of trait intensities-a type vector-and then ask it to choose actions across settings in which we observe human choices. For instance, the type vector (2,4) could correspond to "You are a player characterized by the following profile: Altruism: 2 out of 5, Risk Aversion: 4 out of 5," after which it is asked to make choices. We can then vary the traits (e.g., Altruism, Fairness, Trust,...) and values (e.g., 1-5) to minimize distance to human choices. We illustrate the method by applying it to model 119,147 decisions made by 78,657 subjects from more than 35 countries across 10 classic economic game roles. We find that human behavior can be closely matched using three dimensions: Risk Aversion, Strategic Sophistication, and Trust. The type vectors needed to fit individuals across games cluster into fewer than a dozen groups, with substantial variation in fit across subjects. Moreover, the individual type vectors can predict behavior in held-out games with different rules and available actions. More broadly, this new modeling method is highly generalizable and interpretable: we can input any vector of traits and use them to model behavior across any setting Subjects: Theoretical Economics (econ.TH); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.18265 [econ.TH] (or arXiv:2608.18265v3 [econ.TH] for this version) https://doi.org/10.48550/arXiv.2608.18265 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Matthew O. Jackson [view email] [v1] Tue, 18 Aug 2026 19:30:59 UTC (760 KB) [v2] Sat, 29 Aug 2026 02:12:00 UTC (760 KB) [v3] Thu, 17 Sep 2026 07:56:28 UTC (765 KB) Full-text links: Access Paper: View a PDF of the paper titled Modeling Human Behavior with Type Vectors Using AI, by Matthew O. Jackson and Benjamin S. Manning and Yutong Xie and Walter Yuan and Qiaozhu Mei View PDF HTML (experimental) TeX Source view license Current browse context: econ.TH new | recent | 2026-08 Change to browse by: cs cs.AI econ References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)