发表机构
Stanford; Sante Fe Institute; MIT; Michigan; Moblab; Expected Parrot（斯坦福大学; 圣达菲研究所; 麻省理工学院; 密歇根大学; 莫布实验室; 期望鹦鹉公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种基于大语言模型的AI方法，通过调整类型向量匹配人类选择，发现人类行为可由风险厌恶、策略复杂度、信任三个维度近似，能预测不同游戏行为，为行为科学提供简约理论支持。
AI 中文摘要
我们提出了一种通用、易于实现的基于AI的方法，用于研究人类行为的结构与复杂性。我们为一个大语言模型分配一个“类型向量”，随后提示它在我们观察到人类选择的各种情境中做出行动选择。例如，类型向量（2,4）会被转化为“你是具有以下特征的参与者：利他主义得分为2/5，风险厌恶得分为4/5”，之后提示其做出选择。我们调整维度（如利他主义、公平性、信任等）和取值（如1至5），以最小化与人类选择的距离。将该方法应用于来自35个以上国家的78657名受试者在10种经典经济游戏角色中做出的119147次决策，我们发现使用三个维度即可紧密匹配人类行为：风险厌恶、策略复杂度与信任。此外，拟合不同游戏中个体所需的类型可聚类为不到十几个组别，且能在规则和可用行动不同的留存游戏中预测行为。结果表明，不同情境下的行为可通过低维、可迁移的表示来近似，这为行为科学领域构建通用且简约的理论提供了可能。更广泛而言，该方法可为多种人类行为的结构提供洞见。
英文摘要
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
