AI 中文总结
该研究揭示语言模型生成规则与离散选择随机效用模型同构，通过12种模型的投资组合任务发现其普遍存在异质风险厌恶，且微调可实现目标风险态度的工程化构建。
AI 中文摘要
语言模型越来越多地代表委托方解决真实资源权衡问题，但其经济偏好仍未被观测到。我们证明其生成规则与离散选择的随机效用模型同构，这使得内部logit分数可从结构上识别偏好。在投资组合任务中对12种模型的风险态度进行估计，结果显示存在普遍但异质的风险厌恶。尽管模型会拒绝严格劣势选项，但其引出的偏好无法通过不变性检验，且在不同实验提示下违反无关选项独立性。最后，微调实验表明委托方可明确构建目标风险态度。
英文摘要
Language models increasingly settle real resource tradeoffs on behalf of principals yet their economic preferences remain unobserved. We demonstrate their generation rule is isomorphic to the random utility model of discrete choice. This allows internal logit scores to structurally identify preferences. Estimating risk attitudes across twelve models in a portfolio task reveals universal but heterogeneous risk aversion. Although models reject strictly dominated options, their elicited preferences fail invariance tests and violate the independence of irrelevant alternatives across varying experimental prompts. Finally, fine tuning establishes that a principal can explicitly engineer a target risk attitude.
