ORIE Colloquium: Brad Sturt (Illinois Chicago)

Description

Improving the Security of United States Elections with Robust Optimization

For more than a century, election officials across the United States have inspected voting machines before elections using a procedure called Logic and Accuracy Testing. This procedure consists of election officials casting a test deck of ballots into each voting machine and confirming the machine produces the expected vote total for each candidate.

In this talk, I will bring a scientific perspective to LAT by introducing the first formal approach to designing test decks with rigorous security guarantees. Specifically, we propose using robust optimization to find test decks that are guaranteed to detect any voting machine misconfiguration that would cause votes to be swapped across candidates. Out of all the test decks with this security guarantee, the robust optimization problem yields the test deck with the minimum number of ballots, thereby minimizing implementation costs for election officials. To facilitate deployment at scale, we developed a practical exact algorithm for solving our robust optimization problems based on mixed-integer optimization and the cutting plane method.

In partnership with the Michigan Bureau of Elections, we retrospectively applied our robust optimization approach to all 6,928 ballot styles from Michigan's November 2022 general election; this retrospective study reveals that the test decks with rigorous security guarantees obtained by our approach require, on average, only 1.2% more ballots than current practice. Our robust optimization approach has since been piloted in real-world elections by the Michigan Bureau of Elections as a low-cost way to improve election security and increase public trust in democratic institutions./p>

Bio:
Brad Sturt is an assistant professor of business analytics at the University of Illinois Chicago. His research interest is optimization under uncertainty with focus on applications in operations, revenue management, and the public sector. Recent applications have included election security, data-driven assortment planning and pricing, and high-dimensional optimal stopping. His research has received several recognitions, including second place in the INFORMS Junior Faculty Interest Group Paper Competition and second place in the INFORMS George Nicholson Student Paper Competition. Outside of academia, he is a co-founder of BallotIQ, an election administration startup.