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

University Of Maryland, College Park, College Park MD

Investigators

Abstract

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.