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.

古典经济理论通过优化其预期的公用事业来理性地行事。 有限合理性的范式通过将计算局限性确定确定最佳决策的能力来迈向更大的现实主义。行为和认知研究表明，人类也具有绝对的界限，这意味着他们使用可能构成的一组决策问题的有限分类，而少数类别。 该项目将着重于使用量化理论和信息理论来为分类和决策之间的相互作用建立基础。 研究人员旨在了解分类对个人决策，多数投票的团队决策以及顺序决策的影响。 项目中开发的理论将包括对分类固定的情况和分类最佳设计的情况分析。 除了进行分类研究的行为依据外，对学习的信息限制表明，分类为决策问题的分类对工程设计的影响也有影响。该项目有可能影响经济理论以及对某些社会和组织现象的理解。具体而言，该项目提供了一种理解团队决策绩效的方法，并结合了某些人类限制以及在队友中的不同偏好（例如I型和II型错误之间）的潜力。 偏好的差异会导致团队不和谐的可量化惩罚，即使团队共同做出正确决策的共同目标。 当队友采用不同的分类时，团队多样性也有可量化的优势。这些新概念可能有助于团队成立的原则以及如何以公平决策的目标来优化数据收集政策。