Risk, Time, Altruism, and Emotions

风险、时间、利他主义和情感

• 批准号: 1427355
• 负责人: James Andreoni
• 金额: $ 21.62万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1427355&HistoricalAwards=false
• 关键词: Risk; Time; Altruism; Emotions

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.

自从引入预期效用（EU）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，萨维奇提出了他对欧盟的证明，并引入了独立公理（IA），阿莱正在收集听众关于他著名的IA反例的数据。从那以后，出现了无数对IA的拒绝，许多替代模型也得到了关注。使用我以前支持的NSF的两个贡献，凸风险预算和不确定性等价，我表明，当所有结果都具有足够的风险时，EU实际上比流行的替代方案表现得更好，并且当某些结果涉及确定性时，独立性公理最危险。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测试中证明了这一点，并且没有受到其他模型或测量限制的混淆。这些发现表明，研究人员可能有“样本不足”的确定性，也就是说，我们花在探索IA如何在概率空间边缘崩溃上的努力太少了。该提案描述了一系列实验，旨在衡量当环境风险降低时，偏好是如何变化的。目标是收集必要的数据，以表征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，这样数据就不会与框架或建模假设相混淆。我将用这些结果来拟合一个新的偏好模型，我称之为预期意外模型。