A Workshop on Information Aggregation in Decision Making, University of Maryland May 1-3, 2003

决策中的信息聚合研讨会，马里兰大学，2003 年 5 月 1-3 日

• 批准号: 0326715
• 负责人: Thomas Wallsten
• 金额: $ 1.64万
• 依托单位: University of Maryland, College Park
• 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: 2003-04-15 至 2004-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0326715&HistoricalAwards=false
• 关键词: Workshop; Information; Aggregation; Decision; Making

Virtually all decisions are preceded by gathering and combining information. Such information aggregation, as this process is called, occurs regardless of whether it is an individual, a group, or a computer algorithm that is making the decision. For example, individuals choosing among cancer treatments, selecting investment portfolios, or purchasing homes seek information from various sources and consider what tradeoffs to make prior to arriving at their decisions. Groups go through the same process, but in addition, must combine the separate opinions of their members to arrive at a single group decision. And optimal models or efficient algorithms for real-world decisions, such as scheduling or allocating resources, or making a choice based on the outputs of distinct detectors, balance multiple resource demands against multiple criteria. Despite the formal similarities across individual, group, and computational decision making, researchers in the three areas rarely communicate with each other. Part of the reason for the relative lack of contact between the individual-information-aggregation, group-decision-making, and optimization areas is that traditionally research in these domains has been conducted according to strict disciplinarily lines. The purpose of this workshop is to foster cross-area collaboration, as well as exchange of ideas, models, and methods. It will accomplish these goals by bringing together eminent scientists whose research is focused on information aggregation in group and individual decision-making, or in associated topics in operations research, distributed detection, statistics, and formal decision science, so that the various areas of inquiry may benefit from their similarities and learn from their differences. This workshop, built around both empirical and theoretical contributions, will have a significant positive influence toward de-compartmentalizing the research in the important areas of individual, group, and optimal decision-making.

几乎所有的决定都是在收集和综合信息之后作出的。这个过程被称为信息聚合，无论是个人、群体还是计算机算法做出决策，都会发生。例如，在癌症治疗中选择，选择投资组合或购买房屋的个人会从各种来源寻求信息，并在做出决定之前考虑做出什么样的权衡。团体也要经历同样的过程，但除此之外，还必须联合收割机将其成员的不同意见结合起来，以达成一个单一的团体决策。用于现实世界决策的最佳模型或有效算法，例如调度或分配资源，或根据不同检测器的输出进行选择，根据多个标准平衡多个资源需求。尽管个人、群体和计算决策在形式上有相似之处，但这三个领域的研究人员很少相互交流。个体信息聚合、群体决策和优化领域之间相对缺乏联系的部分原因是，传统上这些领域的研究是根据严格的学科界限进行的。本次研讨会的目的是促进跨领域合作，以及思想，模型和方法的交流。它将通过汇集杰出的科学家来实现这些目标，这些科学家的研究重点是群体和个人决策中的信息聚合，或运筹学，分布式检测，统计学和正式决策科学中的相关主题，以便调查的各个领域可以从它们的相似性中受益，并从它们的差异中学习。本次研讨会，围绕经验和理论的贡献，将有一个显着的积极影响，对去划分的研究在个人，群体和最佳决策的重要领域。