Combatting Gerrymandering with Social Choice: the Design of Multi-member Districts (via Zoom)

Abstract: Every representative democracy must specify a mechanism under which voters choose their representatives. The most common mechanism in the United States -- winner-take-all single-member districts -- both enables substantial partisan gerrymandering and constrains `fair' redistricting, preventing proportional representation in legislatures. We study the design of multi-member districts (MMDs), in which each district elects multiple representatives, potentially through a non-winner-takes-all voting rule. We carry out large-scale analyses for the U.S. House of Representatives under MMDs with different social choice functions, under algorithmically generated maps optimized for either partisan benefit or proportionality. Doing so requires efficiently incorporating predicted partisan outcomes -- under various multi-winner social choice functions -- into an algorithm that optimizes over an ensemble of maps. We find that with three-member districts using Single Transferable Vote, fairness-minded independent commissions would be able to achieve proportional outcomes in every state up to rounding, and advantage-seeking partisans would have their power to gerrymander significantly curtailed. Simultaneously, such districts would preserve geographic cohesion, an arguably important aspect of representative democracies. In the process, we open up a rich research agenda at the intersection of social choice and computational redistricting.

Bio: Nikhil Garg is an Assistant Professor of Operations Research and Information Engineering at Cornell Tech as part of the Jacobs Technion-Cornell Institute. His research interest is the application of algorithms, data science, and mechanism design to the study of democracy, markets, and societal systems at large. He received his PhD from Stanford University, where he was part of the Society and Algorithms Lab and Stanford Crowdsourced Democracy Team. He has spent time at Uber, NASA, and Microsoft, and most recently was the Principal Data Scientist at PredictWise, which provides election analytics for political campaigns. Nikhil has received the INFORMS George Dantzig Dissertation Award, an honorable mention for the ACM SIGecom dissertation award, and 2nd place in the MSOM student paper competition.