ICES: Small: Decision Making with Bounded Categorization

ICES：小：有界分类的决策

• 批准号: 1101147
• 负责人: Vivek Goyal
• 金额: $ 32.88万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1101147&HistoricalAwards=false
• 关键词: ICES; Small; Decision; Making; Bounded

Classical economic theory models humans as acting rationally through the optimization of their expected utilities. The paradigm of bounded rationality takes a step toward greater realism by placing computational limitations on agents' abilities to determine optimal decisions. Behavioral and cognitive studies reveal that humans are also categorically bounded, meaning that they use a finite categorization of the set of decision problems that may be posed, with a small number of categories. This project will focus on the use of quantization theory and information theory to establish foundations for the interplay between categorization and decision making. The researchers aim to understand the impact of categorization on individual decision making, team decision making through majority voting, and sequential decision making. The theory developed in the project will include both analysis of situations in which the categorization is fixed and optimal design of categorizations. In addition to the behavioral justification for the study of categorization, informational limitations on learning suggest that categorization into classes of decision problems has ramifications for engineering design.This project has the potential to influence economic theory and the understanding of certain social and organizational phenomena. Specifically, the project offers a way to understand the decision making performance of teams, with the incorporation of certain human limitations and the potential for differing preferences (e.g., between Type I and Type II errors) among teammates. Differences in preferences lead to a quantifiable penalty of team discord, even when the team shares the common goal of making correct decisions. There is also a quantifiable advantage from team diversity in the sense of obtaining better performance when teammates apply different categorizations. These new concepts could contribute to principles for team formation and for how data gathering policies can be optimized with the goal of fair decision making.

古典经济学理论将人类建模为通过优化其预期效用来理性行事。 有限理性的范式通过对代理人确定最优决策的能力进行计算限制，向更大的现实主义迈进了一步。行为和认知研究表明，人类也是有范畴界限的，这意味着他们使用有限的分类可能会提出的决策问题集，具有少量的类别。 本计画将著重于量化理论与资讯理论的运用，以建立分类与决策之间互动的基础。 研究人员的目的是了解分类对个人决策，团队决策通过多数表决，和顺序决策的影响。 该项目中开发的理论将包括对分类固定的情况的分析和分类的最佳设计。 除了分类研究的行为理由外，学习的信息限制表明，将决策问题分类对工程设计有影响。这个项目有可能影响经济理论和对某些社会和组织现象的理解。具体来说，该项目提供了一种了解团队决策性能的方法，并结合了某些人类限制和不同偏好的可能性（例如，类型I和类型II之间的错误）。 偏好的差异会导致团队不和谐的可量化惩罚，即使团队有做出正确决策的共同目标。 团队多样性也有一个可量化的优势，即当队友应用不同的分类时，可以获得更好的绩效。这些新的概念可以有助于团队组建的原则，以及如何优化数据收集政策，以实现公平决策的目标。