Risk, Time, Altruism, and Emotions

自期望效用理论引入以来，风险偏好理论一直是一个悬而未决的问题。在1952年的同一次会议上，萨维奇提出了他对EU的证明，介绍了独立公理（IA），阿莱正在从听众那里收集关于他著名的IA反例的数据。 从那时起，出现了无数对IA的拒绝，许多替代模型受到了关注。 使用两个贡献，从我以前的NSF支持，凸风险预算和不确定性等价物，我表明，欧盟实际上比流行的替代品时，所有的结果都有足够的风险，独立公理是最危险的，当一些结果涉及确定性。当然，我们不是第一个注意到这一点的人，但我们进行的两个实验都在非参数检验中证明了这一点，并且没有受到其他模型或测量限制的干扰。这些发现表明，研究人员可能有"欠采样"的确定性，也就是说，我们花了太少的精力探索IA如何在概率空间的边缘分解。 我们的目标是收集必要的数据来描述整个选择空间中对偏好的限制，并以完全非参数的方式这样做，以便数据不会与框架或建模假设混淆。 我将用这些结果来拟合一个新的偏好模型，我称之为“期待意外”模型。

Since the introduction of Expected Utility (EU) the theory of preferences over risk has been an unsettled matter. At the same meeting in 1952 at which Savage presented his proof of EU, introducing the Independence Axiom (IA), Allais was gathering data from the audience on his famous counter-examples to IA. Since then, countless more rejections of IA have appeared, and many alternative models have gained attention. Using two contributions from my prior NSF support, Convex Risk Budgets and Uncertainty Equivalents, I show that EU actually performs better than popular alternatives when all outcomes are sufficiently risky, and that the independence axiom is most at peril when some outcomes involve certainty. We are, of course, not the first to notice this, but both of the experiments we have conducted demonstrate this in non-parametric tests, and are not confounded by other model or measurement restrictions. These findings suggest researchers may have "under-sampled" certainty, that is, we have spent too little effort exploring how IA breaks down at the edges of the probability space.This proposal describes a series of experiments designed to measure how preferences shift as the environment becomes less risky. The objective is to collect data necessary to characterize the restrictions on preferences throughout the choice space, and to do so in a completely non-parametric way such that the data are not confounded with framing or modelling assumptions. I will use these results to fit a new model of preferences which I call a model of Expecting the Unexpected.