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arXiv 2408.04573econ.TH
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显示性不变偏好

Revealed Invariant Preference

Peter Caradonna, Christopher P. Chambers

AI总结:

本文利用自动定理证明工具,为满足任意不变性公理的偏好可合理化选择数据提供了充要条件,并通过推广 Dushnik-Miller 定理完整刻画了样本外预测。

摘要:

我们考虑如下问题:用满足任意一组不变性公理的偏好来合理化选择数据。此类公理的例子包括拟线性、齐次性、混合空间的独立性型公理、常相对/绝对风险与模糊厌恶公理、针对注明日期奖励或消费流的平稳性、可分性以及许多其他公理。我们借助理论计算机科学文献中关于自动定理证明的工具,通过一种新颖方法给出了不变可合理化性的充分必要条件。我们还建立了 Dushnik-Miller 定理的一个推广,并利用该推广完整刻画了在任意此类公理集合下由数据生成的样本外预测。

英文摘要:

We consider the problem of rationalizing choice data by a preference satisfying an arbitrary collection of invariance axioms. Examples of such axioms include quasilinearity, homotheticity, independence-type axioms for mixture spaces, constant relative/absolute risk and ambiguity aversion axioms, stationarity for dated rewards or consumption streams, separability, and many others. We provide necessary and sufficient conditions for invariant rationalizability via a novel approach which relies on tools from the theoretical computer science literature on automated theorem proving. We also establish a generalization of the Dushnik-Miller theorem, which we use to give a complete description of the out-of-sample predictions generated by the data under any such collection of axioms.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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