Abstract
Recently, Chikina, Frieze, and Pegden proposed a way to assess significance in a Markov chain without requiring that Markov chain to mix. They presented their theorem as a rigorous test for partisan gerrymandering. We clarify that their ε-outlier test is distinct from a traditional global outlier test and does not indicate, as they imply, that a particular electoral map is associated with an extreme level of “partisan unfairness.” In fact, a map could simultaneously be an ε-outlier and have a typical partisan fairness value. That is, their test identifies local outliers but has no power for assessing whether that local outlier is a global outlier. How their specific definition of local outlier is related to a legal gerrymandering claim is unclear given Supreme Court precedent.
Original language | English (US) |
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Pages (from-to) | 44-49 |
Journal | Statistics and Public Policy |
Volume | 6 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2019 |
Keywords
- Markov chain Monte Carlo
- redistricting
- simulation