Comparing Apples and Oranges: Supporting Collaborative Decision-Making
Jacob Baskin, Brown University
Conference program committees, graduate admissions committees, and even many social Web sites, are in the business of ranking content. However, they have too much content for everyone to review all of it. It therefore becomes necessary to order content based on partial information. Simply comparing numerical scores is error-prone because different reviewers see different portions of the content and score in different ways.
This talk presents a decision support system that instead incorporates reviewers' relative preferences. It produces a fair and accurate picture of which content is likely to be regarded good, poor, and in conflict. This system has been integrated into two working applications: Resume, for job applications, and Continue 2.0, for conference papers.
Jacob, who hails from Toronto, is a graduating computer science major. This year, in addition to conducting research into decision-making and stress-testing Flapjax to build two working applications, he has been studying the effect of baseball's DH rule on interleague play. After graduating, Jacob will be working for Google in New York City.
