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Forecasting Political Outcomes with Different Methods
Thu, September 10, 2:00 to 3:30pm MDT (2:00 to 3:30pm MDT), TBASession Submission Type: Created Panel
Session Description
This panel features a set of papers that use different methods (judgment, scenarios, analytics) to forecast a variety of outcomes such as presidential primaries, Japanese elections, and Chinese relational power in South America.
Sub Unit
Individual Presentations
Applying Machine Learning Methods to Forecast Japanese Elections - Sota Kato, International University of Japan; Hirokazu Shimauchi, The Tokyo Foundation for Policy Research; Budrul Ahsan, Philips Japan; Takafumi Nakanishi, Musashino University
Presidential Nomination Forecasts: Iterative Updating for Error Reduction - Andrew J. Dowdle, University of Arkansas, Fayetteville; Karen Denice Sebold, University of Arkansas, Fayetteville; Randall E. Adkins, University of Nebraska at Omaha; Wayne P. Steger, DePaul University
Partisanship Over Friendship: Networks and Partisanship in Citizen Forecasting - Debra Lynn Leiter, University of Missouri-Kansas City; Jack Lyons Reilly, New College of Florida; Mary Stegmaier, University of Missouri
