Election forecasts: The math, the goals, and the incentives (my talk this Wed afternoon at Cornell University)

At the Colloquium for the Center for Applied Mathematics, Fri 18 Sep 3:30pm:

Election forecasts: The math, the goals, and the incentives

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.

I guess Cornell’s become less anti-Bayesian since Feller’s time . . .