TY - JOUR
T1 - Understanding Significance Tests From a Non-Mixing Markov Chain for Partisan Gerrymandering Claims
AU - Tam Cho, Wendy K.
AU - Rubinstein-salzedo, Simon
N1 - Funding Information:
This research has been funded in part by the National Science Foundation (grant no. SES-1725418/1728902) and the Guggenheim Foundation, as well as multiple computing allocation grants on the Blue Waters sustained-petascale computing resources, which is supported by the NSF (grants OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and the National Center for Supercomputing Applications.
Publisher Copyright:
© 2019, © 2019 The Author(s). Published with license by Taylor & Francis Group, LLC.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Markov chain Monte Carlo
KW - Redistricting
KW - Simulation
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U2 - 10.1080/2330443X.2019.1574687
DO - 10.1080/2330443X.2019.1574687
M3 - Article
SN - 2330-443X
VL - 6
SP - 44
EP - 49
JO - Statistics and Public Policy
JF - Statistics and Public Policy
IS - 1
ER -