The Political Decision-Making Research Cluster combines the expertise of mathematicians, political scientists, and philosophers to investigate quantitative models of political decision-making. During the 2021-22 year we have three focus areas: the mathematics of redistricting, social choice theory, and mathematical modeling of polarization. Our immediate focus is applying Markov Chain Monte Carlo methods to provide relevant, timely analysis during the TX legislative redistricting cycle (MathForUnbiasedMapsTX, or MUM_TX). We will invite speakers pertaining to all three focus areas and talks are expected to be available to remote participants on Zoom: please check back for details.
MathForUnbiasedMapsTX develops and implements Markov Chain Monte Carlo sampling methods to study the practice of redistricting; i.e. drawing single-member districts for the purpose of holding elections. We are applying these methods to the current TX redistricting cycle. By generating a large pool of legal plans, we can provide an unbiased baseline for districting plans. As candidate maps are released, we will compare them to their baseline on measures of partisan and racial gerrymandering.