Aggregation of Probabilistic Opinions

概率意见的汇总

• 批准号: 0241434
• 负责人: David Budescu
• 金额: $ 22.5万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-03-15 至 2007-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0241434&HistoricalAwards=false
• 关键词: Aggregation; Probabilistic; Opinions

This proposal focuses on the aggregation process used by individual Decision-Makers (DMs) to combine probabilistic information from multiple, non-independent sources. Typical examples are investors who combine forecasts from various financial advisors regarding the chances of certain stocks to appreciate in value, and patients who must aggregate information from various experts about the chances of success of a given medical procedure.A model is described that assumes that (a) the DM combines information by averaging the various forecasts, and (b) the DM's confidence in the aggregate is inversely related to the variance of the (possibly weighted) mean forecast. This model is used to derive a series of predictions about the factors that affect and drive the DM's confidence. They will be tested in a series of experiments that would clarify (a) the nature of the aggregation rules used by DMs, (b) the factors that affect the DMs' confidence in the final aggregate, (c) the nature and level of dependence between the aggregates and the confidence they inspire, and (d) the factors that determine the DM's preference for certain advisors.In addition to contributing to our understanding of the basic psychological process involved in aggregating opinions, the results have immediate implications for several questions of considerable practical interest that arise in many fields including, but not restricted to, financial, military and medical decision-making. The results will provide (at least partial) guidelines for approaching questions such as:(a) What kind of, and how many, advisors should a DM consult to achieve a desired level of confidence?(b) What factors make some advisors more or less attractive in certain types of decision problems?(c) What are the sources of the surprisingly high correlation among advisors? What is the relative importance of the various factors and how can one use this information to choose advisors in an optimal fashion?

该建议的重点是聚合过程中使用的个人决策者（DM）的联合收割机的概率信息从多个，非独立的来源。 典型的例子是投资者谁联合收割机预测从各种财务顾问有关的机会，某些股票升值，和病人谁必须汇总信息从各种专家的机会，成功的一个给定的医疗程序。一个模型被描述，假设（a）DM结合信息平均的各种预测，以及（B）DM对总体的信心与（可能加权的）平均预测的方差成反比。 该模型用于得出一系列关于影响和驱动DM信心的因素的预测。 将通过一系列实验对这些数据进行检验，这些实验将澄清（a）模式制造商使用的汇总规则的性质，（B）影响模式制造商对最终汇总的信心的因素，（c）汇总与它们所激发的信心之间的依赖性质和程度，以及（d）决定DM对某些顾问的偏好的因素。除了有助于我们理解聚合中涉及的基本心理过程之外，根据这些意见，这些结果对许多领域中出现的具有相当实际意义的几个问题有直接影响，这些领域包括但不限于金融、军事和医疗决策。 结果将提供（至少部分）的指导方针，处理问题，如：（一）什么样的，有多少，顾问DM应该咨询，以达到预期的信心水平？(b)在某些类型的决策问题中，是什么因素使一些顾问或多或少地具有吸引力？(c)顾问之间惊人的高度相关性的来源是什么？各种因素的相对重要性是什么？如何使用这些信息以最佳方式选择顾问？