Speaker: Alexandria Volkening
Dec12
Time: 12 - 1:30 p.m.
Date: December 12, 2018
Location: 301 Pomerene Hall

TDAI’s complexity community of practice will host speaker Alexandria Volkening, a postdoctoral fellow at Ohio State’s Mathematical Biosciences Institute. Pizza and salad will be provided. RSVP

Title: “Forecasting U.S. elections using compartmental models of infection”

Authors: Alexandria Volkening (MBI, Ohio State University), Daniel F. Linder (Augusta University), Mason A. Porter (University of California, Los Angeles), and Grzegorz A. Rempala (Ohio State University)

Abstract: U.S. election prediction involves polling likely voters, making assumptions about voter turnout, and accounting for various features such as state demographics and voting history. While political elections in the United States are decided at the state level, errors in forecasting are correlated between states. With the goal of shedding light on the forecasting process and exploring how states influence each other, we develop a framework for forecasting elections in the U.S. from the perspective of dynamical systems. Through a simple approach that borrows ideas from epidemiology, we show how to combine a compartmental model with public polling data from HuffPost and RealClearPolitics to forecast gubernatorial, senatorial, and presidential elections at the state level. Our results for the 2012 and 2016 U.S. races are largely in agreement with those of popular pollsters, and we use our new model to explore how subjective choices about uncertainty impact results. We conclude by comparing our forecasts for the senatorial and gubernatorial races in the U.S. midterm elections of November 6, 2018, with those of popular pollsters.

Bio: Alexandria Volkening is a postdoctoral fellow at Ohio State’s Mathematical Biosciences Institute. Prior to joining the Ohio State community in 2017, she received her undergraduate degree in mathematics from UMBC and her Ph.D. in applied mathematics with Bjorn Sandstede at Brown University. Her broad research interests are in applied dynamical systems and data-driven modeling of complex systems. She is particularly interested in understanding collective dynamics and patterns that emerge from interacting agents in biological or social applications. Applications include election forecasting and color pattern formation on the skin of zebrafish. More

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