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Activity Number: 339
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Defense and National Security
Abstract - #305076
Title: A Sequential Procedure for Aggregation of Expert Judgment Forecasts
Author(s): John Irvine*+ and Srinivasamurthy R. Prakash and John Regan and Drazen Prelec
Companies: Draper Laboratory and Draper Laboratory and Draper Laboratory and MIT
Address: 555 Technology Square, Cambridge, MA, 02139-3563, United States
Keywords: Forecasting ; expert judgments ; aggregation

Policy makers rely on forecasts from experts to inform the decision making process. Merging or aggregating the judgments from multiple forecasters poses methodological challenges. New research is exploring ways of combining judgments from multiple experts to arrive at a better overall decision. For forecasts involving a small set of possible categorical outcomes where expert judgments accumulate over time, we propose a sequential procedure that chooses the best single forecast as soon as the expert judgments indicate sufficient evidence for the specific outcome. We present the formulation of this approach for binary forecasting problems. Using forecasting data for a set of real world events, we demonstrate and evaluate this new method. Comparing the performance of the proposed method to the standard unweighted linear average from the pool of subjects demonstrates the benefits of this app

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