Narrowly Rational

时间:2022-11-10         阅读:

光华讲坛——社会名流与企业家论坛第6298期


主题Narrowly Rational

主讲人新加坡国立大学 钟松发副教授

主持人工商管理学院 赵琳教授

时间11月16日 14:00—15:30

直播平台及会议ID腾讯会议,会议ID:546147166

主办单位:工商管理学院 科研处

主讲人简介:

钟松发,经济学博士,新加坡国立大学副教授,纽约大学阿布扎比分校经济学访问教授。钟松发的研究方向为行为经济学、实验经济学、神经经济学等,已在American Economic Review, Econometrica, Journal of European Economic Association, Journal of Economic Theory, International Economic Review, Management Science, Review of Economics and Statistics, Proceedings of the National Academy of Sciences, Neuron, and Psychological Science等国际权威期刊发表多篇论文。2018至2019年,钟松发曾是斯坦福大学行为科学高级研究中心成员,目前担任Management Science期刊和Frontiers in Behavioral Economics期刊的associate editor,也是Theory and Decision期刊的coordinating editor。

内容提要:

The revealed preference analysis allows the inference of underlying preferences from observable choices, and numerous studies have shown that choice data are generally rationalizable by some utility function for the given settings. This study examines whether choice data can be rationalized across settings. In an experiment, we compare portfolio allocations in one setting between two equiprobable Arrow securities, and in another setting between one risk-free asset and one with risky asset that delivers either positive return or nothing with equal probability. We show that choice data is rationalizable within settings, but inconsistency is pervasive across settings, and diversification heuristic rules may underpin the inconsistency across settings. In two additional experiments, we further show that the observed inconsistency across settings can be reduced by framing the choice situations but not by changing the likelihoods. Our study contributes to the literature on the interplay among revealed preference, choice complexity and heuristic rules.

显示性偏好分析有助于从现有选择中推断出潜在的偏好,许多研究表明,在给定的环境下选择数据通常可以利用某些效用函数进行合理化。本研究探讨了在不同的环境下选择数据是否可以被合理化。我们进行了一项对比实验,在第一个实验环境中比较了两个等概率Arrow证券之间的投资组合配置;在第二个实验环境中比较了一个无风险资产和一个有风险资产之间的投资组合配置,其中有风险资产配置以等概率提供正收益或无收益。我们的结果表明,在给定环境下的选择数据是合理的,但是多样化的启发式规则可能导致不同环境下的不一致问题。在另外两个实验中,我们进一步表明,跨环境的不一致性可以通过构建选择情景,而不是改变可能性来减少。我们的研究对于分析显示性偏好、选择复杂性和启发式规则之间的相互作用研究有所贡献。

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