Assessing Uncertainty

评估不确定性

• 批准号: 9408684
• 负责人: Barbara Tversky
• 金额: $ 42.07万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-09-01 至 1997-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9408684&HistoricalAwards=false
• 关键词: Assessing; Uncertainty

We live in an uncertain world where the consequences of our actions are not always predictable. Therefore, the decisions to invest in the stock market, to undergo a medical treatment, or to go to court depend on our assessment of the chances that the market will go up, the treatment will be successful, or the court will decide in our favor. Because in general we do not have objective methods for computing the probabilities of such events, we must rely on human judgment as the major instrument for assessing uncertainty. Hence, the question of how people evaluate evidence and assess uncertainty is highly relevant to many aspects of our lives, from the diagnosis of a patient to the evaluation of expert judgment. An extensive body of research on judgment under uncertainty indicates that intuitive judgments of both laypeople and experts are often at variance with accepted normative principles of probability and statistics (see, e.g., Arkes & Hammond, 1986; Camerer, 1993; Dawes, 1988; Kahneman, Slovic, & Tversky, 1982). These findings have commonly been attributed to cognitive limitations, and explained in terms of judgmental heuristics or simplifying strategies. This proposal presents a new approach to subjective probability based on the notion of evidential support. It gives rise to a formal representation that is compatible with heuristic process-based accounts, and encompasses a wide range of phenomena within a unified theoretical framework. The proposal is divided into four parts. Part 1 presents a new model of belief, called support theory, in which the judged probability of an event depends on the specificity of its description. In particular, judged probability is increased by unpacking the focal hypothesis and decreased by unpacking the alternative hypothesis. Part 2 describes a series of experiments designed to test the major predictions of support theory, including the subadditivity of probability assessments, and the difference between probablity and frequency judgments. Part 3 extend s the theory to the analysis of conditional probability and evidential support. In this account, judgments of conditional probability depend not only on the co-occurrence of the relevant events; it also depends on their correlation. This prediction will be tested in both knowledge-based and data-based judgments. Part 4 addresses the problem of ambiguity or vagueness, and provides a method for assessing the imprecision of belief in terms of upper and lower probability judgments. These will be compared to standard measures of reliability, and their implications to decision making will be explored. Theoretical and practical implications of the present approach to the representation and the elicitation of belief are discussed throughout.

我们生活在一个不确定的世界，我们行为的后果并不总是可以预测的。因此，决定投资股市，接受治疗，还是诉诸法庭，取决于我们对市场上涨、治疗成功或法院做出有利于我们的裁决的可能性的评估。因为一般来说，我们没有客观的方法来计算这类事件的概率，所以我们必须依靠人的判断作为评估不确定性的主要工具。因此，人们如何评估证据和评估不确定性的问题与我们生活的许多方面高度相关，从对患者的诊断到对专家判断的评估。大量关于不确定条件下判断的研究表明，外行人和专家的直觉判断往往与公认的概率统计规范原则不一致(参见Arkes&Amp；Hammond，1986；Camerer，1993；Dawes，1988；Kahneman，Slovic，&Amp；Tversky，1982)。这些发现通常被归因于认知限制，并从判断启发式或简化策略的角度进行解释。这一建议提出了一种基于证据支持概念的主观概率的新方法。它产生了一种形式表示，它与启发式的基于过程的描述相兼容，并在统一的理论框架内涵盖了广泛的现象。该提案分为四个部分。第一部分提出了一种新的信念模型，称为支持理论，在该模型中，事件的判断概率取决于其描述的特殊性。特别地，判断概率通过解包焦点假设来增加，而通过解包替换假设来降低。第二部分描述了一系列实验，旨在检验支持理论的主要预测，包括概率评估的次可加性，以及概率判断和频率判断之间的差异。第三部分将S的理论扩展到条件概率和证据支持的分析中。在这种情况下，对条件概率的判断不仅取决于相关事件的共同发生，还取决于它们之间的相关性。这一预测将在基于知识的判断和基于数据的判断中得到检验。第四部分讨论了歧义或含糊的问题，并提供了一种根据上下限概率判断来评估信念的不精确性的方法。这些将与可靠性的标准衡量标准进行比较，并将探讨它们对决策的影响。本研究通篇讨论了本方法对信念的表征和启发的理论和实践意义。