Title:

Algorithmic Redistricting

District boundaries for the U.S. House of Representatives are often unfair; splitting up communities, disenfranchising minority voters, and failing to reflect the will of the people. This problem is only made worse by partisan gerrymandering, when politicians redraw districts to exacerbate these issues and disproportionately favor their own political party. These gerrymandered districts undermine democracy by delivering elections with a fixed outcome. Even non-partisan redistricting commissions can have hidden partisan agendas. To generate truly fair districts, an unbiased algorithm may be needed. Our project sought to create such an algorithm that would create compact, competitive districts without favor for one side or the other. We researched markov chains and applied them towards a redistricting algorithm that took multiple variables into account, such as compactness of districts and representation of minority voters. While the actual mechanisms behind the algorithm, such as the changing of the map and analysis of results, were effective, our calculations taking our variables into account were less-so, delivering mixed results.

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