A quantitative behavioral framework for individual and social choice

个人和社会选择的定量行为框架

• 批准号: 0820009
• 负责人: Michel Regenwetter
• 金额: $ 38.15万
• 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: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0820009&HistoricalAwards=false
• 关键词: quantitative; behavioral; framework; individual; social

Two serious problems plague research in decision sciences. One concerns aggregation of individual choice data. Much research in individual decision making routinely aggregates data across decision makers or across repeated choices made by a given individual. Yet, as the theory of voting methods has famously shown, such aggregated choices may not match the choices of any single decision maker at any given time. This problem has been known under the heading of "voting paradoxes." A second problem in the decision sciences concerns a conceptual, mathematical, and statistical disconnect between major theories on the one hand, and empirical data on the other hand. Most theories are static, whereas behavior is highly variable. In this research we address the first of these problems through a quantitative framework that dissociates individual decision making from group or societal choice. The solution is to make variability of preferences an inherent part of the theory. The second problem is addressed by leveraging recent advances in mathematical modeling and in statistical inference. These advances allow for the conduct of quantitative contests among major decision theories using laboratory and survey data.

两个严重的问题困扰着决策科学的研究。其中一个涉及个人选择数据的汇总。许多关于个人决策的研究通常会汇总不同决策者或特定个人重复做出的选择的数据。然而，正如投票方法理论所著名的那样，在任何给定的时间，这样的综合选择可能不会与任何单一决策者的选择相匹配。这个问题已经在“投票悖论”的标题下为人所知。决策科学中的第二个问题涉及主要理论和经验数据之间的概念、数学和统计脱节。大多数理论是静态的，而行为是高度可变的。在这项研究中，我们通过一个将个人决策从群体或社会选择中分离出来的量化框架来解决第一个问题。解决方案是让偏好的可变性成为该理论的固有组成部分。第二个问题是利用数学建模和统计推断方面的最新进展来解决的。这些进展允许利用实验室和调查数据在主要决策理论之间进行量化竞争。