  Partisan gerrymandering poses a threat to democracy. Moreover, the complexity
of the districting task may exceed human capacities. One potential solution is
using computational models to automate the districting process by optimizing
objective and open criteria, such as how spatially compact districts are. We
formulated one such model that minimized pairwise distance between voters
within a district. Using US Census Bureau data, we confirmed our prediction
that the difference in compactness between the computed and actual districts
would be greatest for states that are large and therefore difficult for humans
to properly district given their limited capacities. The computed solutions
highlighted differences in how humans and machines solve this task with machine
solutions more fully optimized and displaying emergent properties not evident
in human solutions. These results suggest a division of labour in which humans
debate and formulate districting criteria whereas machines optimize the
criteria to draw the district boundaries. We discuss how criteria can be
expanded beyond notions of compactness to include other factors, such as
respecting municipal boundaries, historic communities, relevant legislation,
etc.
