Virtual CAM Colloquium: Andrew Gelman (Columbia University) - Election forecasts: the math, the goals, and the incentives

Description

Abstract:
Election forecasting has increased in popularity and sophistication over the past few decades and has moved from being a hobby of some political scientists and economists to a major effort in the news media. This is an applied math seminar so we will first discuss several mathematical aspects of election forecasting: the information that goes into the forecasts, the models and assumptions used to combine tis information into probabilistic forecasts, the algorithms used to compute these probabilities, and the ways that forecasts can be understood and evaluated. We discuss these in particular reference to the Bayesian forecast that we have prepared with colleagues at the Economist magazine (https://projects.economist.com/us-2020-forecast/president). We then consider some issues of incentives for election forecasters to be over- or under-confident, different goals of election forecasting, and ways in which analysis of polls and votes can interfere with the political process.

Bio:
Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award three times from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina), and Regression and Other Stories (with Jennifer Hill and Aki Vehtari). Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.

Zoom Link Access:
This talk will be given via Zoom, and the link is emailed to the CAM Seminar listserv the week of the talk. If you are not on the listserv, please contact Erika Fowler-Decatur to request the link.