Risk, Ambiguity and Dynamic Choice

风险、模糊性和动态选择

• 批准号: 1426252
• 负责人: Wolfgang Pesendorfer
• 金额: $ 38.05万
• 依托单位: Princeton University
• 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: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1426252&HistoricalAwards=false
• 关键词: Risk; Ambiguity; Dynamic; Choice

This project studies two different problems in decision making under uncertainty. The first deals with the resolution of uncertainty over time. The choices that individuals make reflect not only their concern for the uncertain ultimate outcome but also their interest in how this uncertainty will resolve over time. We develop a new model of preference for temporal resolution of uncertainty that is simpler than existing models, closer to many standard dynamic models of economics and finance and, therefore, requires less data on individuals' beliefs about the future. Our second projects deals with ambiguity; that is, situations in which individuals find it difficult to quantify the uncertainty they are confronting. We propose a framework in which decision makers distinguish between unambiguous and ambiguous events and use the former to calibrate the latter. Thus, each unambiguous event, A, is calibrated by two events, the least likely unambiguous event more likely than A and the most likely unambiguous event less likely than A. Our goal is to develop a subjective (i.e., preference-based) theory of uncertainty that permits ambiguity perception and yet is amenable to suitably modified versions of probability theory and statistics. Models of temporal resolution of uncertainty and models of ambiguity both have many applications in macroeconomics and finance. Both of these lines of inquiry are limited by difficulties in developing and testing models. Our simpler models should facilitate these applications and make it easier to identify their parameters and find the relevant empirical evidence.Our model of temporal resolution of uncertainty differs from the Kreps and Porteus' model by allowing for preference for early resolution without requiring sensitivity to higher order information that does not affect fundamentals; it is analogous to standard dynamic models of choice without preference for timing of resolution. In such models, the decision maker derives utility each period from her possibly random consumption in that period. In our model, the decision maker derives utility only from her beliefs about what she will ultimately consume, not from her beliefs about how her beliefs will evolve. This part of the research has the following goals: (i) provide an axiomatic foundation of risk consumption preferences. (ii) Develop and analyze appropriate measures of preference for early/late resolution of uncertainty. (iii) Investigate the implications of risk consumption preferences in various applications. Our model of calibrated ambiguity offers a new notion ambiguity perception (which we call qualitative uncertainty relations). Our main result establishes an inner probability representation for qualitative uncertainty analogous to the Dempster-Shafer theory. To be able to distinguish ambiguity perception from ambiguity attitude, we allow the qualitative uncertainty relation to be incomplete. This incompleteness reflects the fact that when there is ambiguity, a decision maker's perception of ambiguity by itself, is not sufficient to determine her ranking of bets. The goal of this research is to provide a representation theorem for qualitative uncertainty relations and use it to study updating, inference and the relationships among existing models of ambiguity and evidence.

该项目研究不确定性下决策中的两个不同问题。第一个涉及随着时间的推移解决不确定性。个人做出的选择不仅反映了他们对不确定的最终结果的担忧，也反映了他们对这种不确定性如何随着时间的推移而解决的兴趣。我们开发了一种新的不确定性时间解决偏好模型，该模型比现有模型更简单，更接近许多标准的经济和金融动态模型，因此需要更少的个人对未来信念的数据。我们的第二个项目涉及歧义；也就是说，个人发现很难量化他们所面临的不确定性的情况。我们提出了一个框架，决策者可以区分明确事件和模糊事件，并使用前者来校准后者。因此，每个明确事件 A 由两个事件校准，最不可能的明确事件比 A 更有可能，而最可能的明确事件比 A 更不可能。我们的目标是开发一种主观（即基于偏好）的不确定性理论，该理论允许模糊性感知，但适合概率论和统计学的适当修改版本。不确定性的时间解析模型和模糊性模型在宏观经济学和金融学中都有许多应用。 这两条研究路线都受到模型开发和测试困难的限制。 我们更简单的模型应该促进这些应用，并使其更容易识别其参数并找到相关的经验证据。我们的不确定性时间解析模型与 Kreps 和 Porteus 模型不同，因为我们允许优先考虑早期解析，而不需要对不影响基本面的高阶信息敏感；它类似于选择的标准动态模型，不偏好解决时间。在这样的模型中，决策者在每个时期从她在该时期可能的随机消费中获得效用。在我们的模型中，决策者仅从她对最终消费什么的信念中获得效用，而不是从她对她的信念将如何演变的信念中获得效用。这部分研究有以下目标：（i）提供风险消费偏好的公理基础。 (ii) 制定并分析适当的偏好措施，以尽早/晚期解决不确定性。 (iii) 研究风险消费偏好在各种应用中的影响。我们的校准模糊度模型提供了模糊感知的新概念（我们称之为定性不确定性关系）。 我们的主要结果建立了类似于登普斯特-谢弗理论的定性不确定性的内部概率表示。 为了能够区分模糊性感知和模糊性态度，我们允许定性不确定性关系是不完整的。这种不完整性反映了这样一个事实：当存在模糊性时，决策者对模糊性的感知本身不足以确定她的赌注排名。本研究的目标是提供定性不确定性关系的表示定理，并用它来研究更新、推理以及现有模糊性和证据模型之间的关系。