The American winner-take-all congressional district system empowers
politicians to engineer electoral outcomes by manipulating district
boundaries. To date, computational solutions mostly focus on drawing
unbiased maps by ignoring political and demographic
input, and instead simply optimize for compactness and other related
metrics. However, we maintain that this is a flawed approach because
compactness and fairness are orthogonal qualities; to achieve a
meaningful notion of fairness, one needs model political
and demographic considerations, using historical data to do this.
We
will
discuss two recent papers
that explore and develop this perspective. In the first (joint with
David Shmoys), we present a scalable approach to explicitly optimize for
arbitrary piecewise-linear definitions of fairness; this employs a
hierarchical stochastic decomposition approach to
produce an exponential number of distinct district plans that can be
optimized via a standard set partitioning integer programming
formulation. This enables the largest-ever ensemble study of
congressional districts, providing insights into the range of possible
expected outcomes and the implications of this range on potential
definitions of fairness. In the second paper (joint with Nikhil Garg,
David Rothschild, and David Shmoys), we use the above decomposition
technique to study the design of multi-member districts
(MMDs) in which each district elects multiple representatives,
potentially through a non-winner-takes-all voting rule (as currently
proposed in H.R. 4000). 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. We find that with
three-member districts using Single Transferable Vote, fairness-minded
independent commissions can achieve proportional outcomes
in every state up to rounding, and this would significantly curtail the
power of advantage-seeking partisans to gerrymander. We believe that
this work opens up a rich research agenda at the intersection of social
choice and computational redistricting.
Bio:
Wes Gurnee
is a Ph.D. student
in the Operations Research Center at the Massachusetts Institute of
Technology advised by Dimitris Bertsimas. He is broadly interested in
the use of mathematical modeling and optimization to improve
sociotechnical systems, with an emphasis on fairness and
sustainability. Before MIT, he was a software engineer at Google and
graduated with a degree in computer science from Cornell University.