R EVEALING THE G EOPOLITICAL GEOMETRY THROUGH SAMPLING J ONATHAN M - - PowerPoint PPT Presentation

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R EVEALING THE G EOPOLITICAL GEOMETRY THROUGH SAMPLING J ONATHAN M - - PowerPoint PPT Presentation

R EVEALING THE G EOPOLITICAL GEOMETRY THROUGH SAMPLING J ONATHAN M ATTINGLY (+ THE TEAM ) D UKE M ATH gerrymander manipulate the boundaries of an electoral constituency to favor one party or class. achieve (a result) by manipulating the


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GEOPOLITICAL

GEOMETRY THROUGH SAMPLING

REVEALING THE

JONATHAN MATTINGLY (+ THE TEAM) DUKE MATH

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gerrymander

  • manipulate the boundaries of an

electoral constituency to favor one party

  • r class.
  • achieve (a result) by manipulating the

boundaries of an electoral constituency. "a total freedom to gerrymander the results they want"
 racial vs partisan gerrymander

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North Carolina 13 Congressional Representatives

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NC has around 10.2 million people

Every decade, required to redo the (13) congressional districts

Charlotte Area: Charlotte-Gastonia-Salisbury- population 2,402,623 The Triangle: Raleigh-Durham-Cary-Chapel Hill- population 1,749,525 The Piedmont Triad: Greensboro—Winston-Salem—High Point- population 1,589.200

Population Density Presidential Election 2016

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U.S. House congressional districts for 2012 election

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DEMOCRACY

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The will of the people is expressed

every person can

VOTE

every vote is

COUNTED

(once)

DEMOCRACY

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–Justice Ruth Bader Ginsburg Evenwel v. Abbott, April 2016

“By ensuring that each representative is subject to requests and suggestions from the same number of constituents, total population apportionment promotes equitable and effective representation.”

  • ne person one vote principle
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2012 North Carolina Elections for U.S. House

VOTES PERCENTAGE SEATS Democratic 2,218,357 50.65% 4 Republican 2,137,167 48.80% 9 Libertarian 24,142 0.55%

  • The most Democratic district had 79.63% Democratic votes.
  • The most Republican district had 63.11% Republican votes.
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Are these results due to political gerrymandering ?

  • r

Are these results natural outcomes of NC's geopolitical structure of the spatial distribution of partisan votes ?

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40% Blue 60% Red Red wins 3 Blue wins 2

from Wikipedia after an image by Steven Nass

Red wins 2 Blue wins 3 Red wins 5 Blue wins 0

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2012 2016 Judges’

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How to quantify how gerrymandered

  • r unrepresentative a redistricting is?

How to quantify gerrymandering? How to reveal a state’s geopolitical structure?

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How does one find the true message in an election ?

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What if we drew the districts randomly ?

with no regard for party registration or most demographics

reveal the geopolitical structure encoded in the votes

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Many Groups using algorithmic generated maps to benchmark

  • Jowei Chen (Univ Michigan)
  • Wendy Cho (UIUC)
  • Samuel Wang (Princeton)
  • Kosuke Imai, Benjamin Fifield (Princeton)
  • Alan Frieze, Wesley Pegden, Maria Chikina (CMU)

Not all the same. Not all Random. Some generating alternative maps. Some Sampling a defined distribution. some using actual surrogate districts. Focus on our group at Duke

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Impact of Duke Team work

Common Cause v. Rucho (N.C. Congressional):

  • 3 judge conditional panel. Direct appeal to SCOTUS.
  • Provide expert testimony and report in lawsuit. 


Closing arguments on October 16

Gill v. Whitford (WI State Assembly) :

  • Oral argument held in Supreme Court (SCOTUS) October 2
  • Provide report supporting Amicus Brief by Eric S. Lander

North Carolina v. Covington (N.C. State Assembly):

  • 3 judge conditional panel rule racial gerrymander. Affirmed by

SCOTUS in June.

  • Provide expert testimony on new maps produces at courts order
  • Preparing for partisan gerrymander
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The Recipe

  • 1. Make a good random redistricting of N.C. into

13 U.S. house districts.

  • 2. Count number of Democratic and Republican

votes in each of the new districts using the actual 2012 votes.

  • 3. Determine winner in each district of the random

redistricting.

  • 4. Return to step 1.

Use Markov Chain Monte Carlo to sample a distribution on redistrictings

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Criteria for Sampling

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non-partisan design criteria
 (HB 92)

  • 1. districts have equal population
  • 2. the districts are connected and compact,
  • 3. splitting counties is minimized, and
  • 4. African American voters are sufficiently concentrated in 2

districts to affect the winner.

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Use Markov Chain Monte Carlo to sample from redistricting with good scores.

Sample: (density) ∝ e−β(score of redistricting)

Not just generating a large number of alternatives. Know what distribution we are sampling from.

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N.C. HOUSE BILL 92 REDISTRICTING STANDARDS

  • Districts within 0.1% of equal population
  • Districts shall be reasonably compact
  • Contiguous territory, attempting not to split cities or

counties

  • Comply with the Voting Rights Act of 1965
  • Ignore: Incumbency, party affiliation, demographics
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N.C. HOUSE BILL 92 REDISTRICTING STANDARDS

  • Districts within 0.1% of equal population (we get close)
  • Districts shall be reasonably compact
  • Contiguous territory, attempting not to split cities or

counties

  • Comply with the Voting Rights Act of 1965
  • Ignore: Incumbency, party affiliation, demographics
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Score function

P(ξ) = 1 Z e−βJ(ξ) J(ξ) = wpJpop(ξ) + wIJcompact(ξ) + wcJcounty(ξ) + wmJmino(ξ) ξ : {Precincts} 7! {1, . . . , 13} (a 13 color Potts Model with an unusual energy)

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Population Score

Sum of square deviation from ideal district population

13

X

n=1

h Ideal − (Pop in district n) i2 Ideal = Population of N.C. 13 ≈ 733, 499

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Compactness score

Minimized for a circle (Perimeter)2 Area ≥ 4π ≈ 12.5 Also considered the ratio of district’s area to the smallest circumscribing rectangle

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Also include score terms for Voting Rights Act and Preserving County Boundaries

Soft penalization :

  • for number of split counties of different sizes
  • redistricting plans without two districts meeting

minimal voting age black population.

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Judges NC2016 NC2012 Fraction of result 0.1 0.2 0.3 0.4 Number of Democrats Elected (2012 votes) 3 4 5 6 7 8 9 10 Judges NC2016 NC2012 Fraction of result 0.1 0.2 0.3 0.4 0.5 0.6 Number of Democrats Elected (2016 votes) 2 3 4 5 6 7 8

Election Results for Ensemble of Redistricting Plans

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Overall Fraction of Republican Vote 0.44 0.46 0.48 0.50 0.52 0.54 0.56 Elected Republicans (2012 Votes) 5 10 0.44 0.46 0.48 0.50 0.52 0.54 0.56 Elected Republicans (2016 Votes) 5 10

PRE12 GOV12 USH12 USH16 PRE16 GOV16 NCSS16

Republican Vote Fraction 0.47 0.48 0.49 0.50 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 Elected Republicans 4 6 8 10 12

Historical and Shifted Elections

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2012 2016 Judges

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Other states ?

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Wisconsin General Assembly

WI 50 55 60 65 70 75 WSA12 Fraction of result 0.1 0.2 WI WSA14 Fraction of result 0.1 0.2 WI (int) WI (act) WSA16 Fraction of result 0.1 0.2 Elected Republicans 50 55 60 65 70 75

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PRE12 USS12 SOS14 USH12 WSA12 PRE16 WSA14 USH14 GOV14 GOV12 WSA16

Fraction of Republican vote 0.46 0.48 0.50 0.52 0.54 Number of Republican seats 40 50 60 70

Wisconsin historical elections

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WSA16 Majority Super Majority WSA14 Majority Super Majority

Republicans elected Fraction of Republican vote 0.44 0.46 0.48 0.50 0.52 0.54 0.56 40 60 80 40 60 80

Shift the global percentages

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seats vs global vote

WSA12 Super Majority Majority Expected seats WI (contested) Standard Deviation 90% of ensemble Bound

Number of Republican seats 10 20 30 40 50 60 70 80 90 % Vote to the Republicans 45 50 55 60

WSA14 Super Majority Majority Expected seats WI (contested) Standard Deviation 90% of ensemble Bound

Number of Republican seats 20 30 40 50 60 70 80 90 % Vote to the Republicans 45 50 55 60

WSA16 Super Majority Majority Expected seats WI (contested) Standard Deviation 90% of ensemble Bound

Number of Republican seats 30 40 50 60 70 80 90 % Vote to the Republicans 45 50 55 60

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Structural advantage doesn’t explain

WI Probability 20 40 60 80 100 Republican vote needed for parity in election (2012) 0.46 0.48 0.50 WI Probability 50 100 Republican vote needed for parity in election (2014) 0.44 0.46 0.48 0.50 WI Probability 20 40 60 80 Republican vote needed for parity in election (2016) 0.44 0.46 0.48

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Shift to Median Parity

WI % of maps 5 10 15 20 WSA12 Interpolated Votes (shifted to parity) 40 45 50 55 WI % of maps 5 10 15 20 WSA14 Interpolated Votes (shifted to parity) 40 45 50 55 WI % of maps 5 10 15 20 WSA16 Interpolated Votes (shifted to parity) 40 45 50 55

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What produces these effects ?

What is the signature of gerrymandering ?

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Red wins 2 Blue wins 3 Red wins 2 districts by 8 votes each Blue wins 3 districts by 2 votes each

Percentage of Democrats from lowest to highest

⇥ 10% 10% 60% 60% 60% ⇤

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1 2 3 4 5 6 7 8 9 10 11 12 13

Most Republican to Most Democratic Districts

0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8

Democratic Winning Percentages Democratic Winning Percentages (House 2012)

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1 2 3 4 5 6 7 8 9 10 11 12 13

Most Republican to Most Democratic Districts

0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8

Democratic Winning Percentages Democratic Winning Percentages (House 2012)

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1 2 3 4 5 6 7 8 9 10 11 12 13

Most Republican to Most Democratic Districts

0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8

Democratic Winning Percentages Democratic Winning Percentages (House 2012)

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1 2 3 4 5 6 7 8 9 10 11 12 13

Most Republican to Most Democratic Districts

0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8

Democratic Winning Percentages Democratic Winning Percentages (House 2012)

NC2012

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1 2 3 4 5 6 7 8 9 10 11 12 13

Most Republican to Most Democratic Districts

0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8

Democratic Winning Percentages Democratic Winning Percentages (House 2012)

Judges NC2012 NC2016

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NC2012 NC2016 Judges Democratic vote fraction 0.3 0.4 0.5 0.6 0.7 0.8 Most Republican To Most Democratic Districs (2012 votes) 2 4 6 8 10 12

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NC2012 NC2016 Judges

Most Republican To Most Democratic Districts (2016 votes) (2012 votes) Democratic vote fraction

0.2 0.3 0.4 0.5 0.6 0.7 0.8 2 4 6 8 10 12

.

2 4 6 8 10 12

NC Congressional Delegation

Identify Cracked and Packed districts

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NC Congressional Delegation

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WSA14 Fraction of Democratic vote 0.2 0.4 0.6 0.8 1.0 District from most to least Republican 10 20 30 40 50 60 70 80 90 100

0.40 0.45 0.50 0.55 40 50 60

Wisconsin General Assembly

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Metrics

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Gerrymander Index

Measure deviation for expected district structure

NC2012 NC2016 Judges NC2012 NC2016 Judges

Gerrymandering index (2016 votes) (2012 votes) Fraction w/ worse index

0.2 0.4 0.6 0.8 1.0 0.1 0.2 0.30 0.1 0.2 NC2012 NC2016 Judges NC2012 NC2016 Judges

Gerrymandering index (2016 votes) (2012 votes) Probability density

5 10 15 20 25 30 0.1 0.2 0.30 0.1 0.2

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Frac Republican Vote 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 0.60 0.62 0.64 0.66 Republicans Elected 40 60 80

WI (int) WI (act) WSA16 Fraction of result 0.1 0.2 Elected Republicans 50 55 60 65 70 75

`(map) = − log Prob(outcome map produces)

Average `(map) over shift

Measuring Representativeness

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WI WSA12 Probability 0.5 1.0 1.5 2.0 H 1 2 3 4 5 6 7

WI WSA14 Probability 0.5 1.0 1.5 2.0 H 1 2 3 4 5 6 7

WI WSA16 Probability 0.5 1.0 1.5 2.0 1 2 3 4 5 6 7

The Wisconsin plans are clearly an outlier for the average log likelihood over shifts 45%-55% Measuring Representativeness

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NC2012 NC2016 Judges Fraction w/ worse index 0.2 0.4 0.6 0.8 1.0 Efficiency gap (2012 votes) 0.1 0.2 0.3 0.4 0.5

NC2012 NC2016 Judges Fraction w/ worse index 0.2 0.4 0.6 0.8 1.0 Efficiency gap (2016 votes) 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Efficiency Gap

Situate EG inside the ensemble

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Engineered ?

results should be stable under small changes to districts sample near by districts and observe changes

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NC 2012 NC 2016 Judge’s districts resemble near by districts NC 2012 and NC 2016 do not

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Judges NC2016 NC2012

Gerrymandering index (2012 votes) Fraction w/ worse index

0.2 0.4 0.6 0.8 1.0 0.20 0.22 0.24 0.26 0.28 0.12 0.13 0.14 0.15 0.16 0.04 0.05 0.06 0.07

Gerrymander Index

Local Perturbations

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Stability of Conclusions

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SLIDE 66 24.5×103 samples 119.3×103 samples Fraction of result 0.1 0.2 0.3 0.4 0.5 0.6 Number of Democrats Elected (2012 votes) 3 4 5 6 7 8 9 10 24.5×103 samples 119.3×103 samples Democratic vote fraction 0.3 0.4 0.5 0.6 0.7 0.8 Most Republican To Most Democratic Districs (2012 votes) 1 2 3 4 5 6 7 8 9 10 11 12 13 Main results Dispersion ratio for compactness Fraction of result 0.1 0.2 0.3 0.4 0.5 0.6 Number of Democrats Elected (2012 votes) 3 4 5 6 7 8 9 10 Reported S.A. parameters Doubled S.A. parameters Fraction of result 0.1 0.2 0.3 0.4 0.5 0.6 Number of Democrats Elected (2012 votes) 3 4 5 6 7 8 9 10 Judges (initial) NC2012 (initial) NC2016 (initial) Fraction of result 0.1 0.2 0.3 0.4 0.5 0.6 Number of Democrats Elected (2012 votes) 3 4 5 6 7 8 9 10 No change β=0.8 β=1.2 wI=2 wI=3 wm=700 wm=900 wp=2500 wp=3000 Democratic vote fraction 0.3 0.4 0.5 0.6 0.7 0.8 Most Republican To Most Democratic Districs (2012 votes) 1 2 3 4 5 6 7 8 9 10 11 12 13 Population threshold at 1% Population threshold at 0.75% Population threshold at 0.5% Fraction of result 0.1 0.2 0.3 0.4 0.5 0.6 Number of Democrats Elected (2012 votes) 3 4 5 6 7 8 9 10 Population Threshold 1% Population Threshold 0.5% Democratic vote fraction 0.3 0.4 0.5 0.6 0.7 Most Republican To Most Democratic Districs (2012 votes) 1 2 3 4 5 6 7 8 9 10 11 12 13 Reported S.A. parameters Doubled S.A. parameters Democratic vote fraction 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Most Republican To Most Democratic Districs (2016 votes) 1 2 3 4 5 6 7 8 9 10 11 12 13 Judges (initial) NC2012 (initial) NC2016 (initial) Democratic vote fraction 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Most Republican To Most Democratic Districs (2016 votes) 1 2 3 4 5 6 7 8 9 10 11 12 13
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Math Questions ?

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model a random distribution of political parties Assume the population is uniform Q: Find null distribution of order statistics for district make up

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Q: Give some form of stability of plots

  • ver a class of energy functions

which have certain marginal statistics.

No change β=0.8 β=1.2 wI=2 wI=3 wm=700 wm=900 wp=2500 wp=3000 Democratic vote fraction 0.3 0.4 0.5 0.6 0.7 0.8 Most Republican To Most Democratic Districs (2012 votes) 1 2 3 4 5 6 7 8 9 10 11 12 13

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Q: Characterize the structure of the energy landscape

Even with just population and compactness

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Accelerate the sampling

  • parallel tempering
  • accelerated sampling
  • hierarchical sampling
  • parallel algorithms
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Christy Graves Sachet Bangia Sophie Guo Bridget Dou

The Team

Robert Ravier Justin Luo Hansung Kang Greg Herschlag

MA TH

Michael Bell

arXiv:1709.01596 arXiv:1704.03360

Jonathan 
 Mattingly

arXiv:1410.8796

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Conclusions

Identify the background NULL hypothesis Identify the background geopolitical structure of state. Identify the outliers, the unreasonable maps. Benchmark different proposed metrics Interaction of geopolitical structure and metrics Considered other states and effect of VRA Lots of interesting math questions