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GerryManderNL.nlogo
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globals[state-maj change-mind-prob polarization pop-vote rep-house-seats leader signal election house-win-list reversal strong-com swing-com]
extensions [ nw ]
breed [ dots dot ]
breed [ users user ]
breed [ citizens citizen ]
citizens-own[distr vote community-type pol-community stg-community ]
to setup
clear-all
; Create lattice with dots at each patch
set house-win-list list 0 0 ;initialize list recording majority in the number of representatives
ask patches [ ;initialize patches
set pcolor 1
sprout-dots 1 [
set color 0
set size 0.2
set heading 0
set shape "circle"
]
]
ifelse city-country[ ;initialize districts according to city-country setup or random setup
draw-starting-districts-city] [draw-starting-districts-random]
election-result ;initali election at tick 0
count-strong-swing ;initial count of strong and competitive (swing) districts
reset-ticks
end
to draw-starting-districts-random ;procedure to implement the random setup
create-citizens 63 [
ifelse any? patches with [not any? citizens-here][ move-to one-of patches with [not any? citizens-here]][]
set shape "person"
set size 0.5
ifelse random-float 1 > dem-probability [set color 15
set vote 1
] [set color 95
set vote -1
]
]
ask citizens[
create-links-with citizens-on neighbors4 with [pycor = [ycor] of myself]
]
nw:set-context citizens links
let communities nw:louvain-communities
let colors sublist base-colors 0 (length communities) (foreach communities colors [ [community col] ->
ask community [ set distr col ]
])
end
to draw-starting-districts-city ;procedure to implement the city-country setup
create-citizens 63 [
ifelse any? patches with [not any? citizens-here][ move-to one-of patches with [not any? citizens-here]][]
set shape "person"
set size 0.5
]
ask citizens[
ifelse random-float 1 > (1 - (ycor / 12)) ^ 2 ;<= change here if you want to model differently city-country
[set color 15
set vote 1
] [set color 95
set vote -1
]
create-links-with citizens-on neighbors4 with [pycor = [ycor] of myself]
]
nw:set-context citizens links
let communities nw:louvain-communities
let colors sublist base-colors 0 (length communities) (foreach communities colors [ [community col] ->
ask community [ set distr col ]
])
end
to go ;GO procedure
ask citizens [ set community-type "none" ]
changing-idea ;let citizens change their opinion
ifelse (ticks + 2 + 1) / 2 = int ((ticks + 2 + 1) / 2) [ ;; hold elections every two years
election-result
set election 1
][set election 0]
if gerrymendering-on [
ifelse (ticks + 10 + 1) / 10 = int ((ticks + 10 + 1) / 10) [ ;; gerrymandaring every ten years
set signal 1
gerry-mandaring][set signal 0]
]
count-strong-swing ;count the number of strong and competitive districts
tick
if ticks = 99 [
reversal-count] ; count the number of reversals in the time series % house seats
if ticks >= 100 [ ;stop the model in the 100th year
stop]
end
to election-result ;; compute election results
let college-vote 0
let communities nw:louvain-communities
foreach communities[
[community] ->
let vote-community 0
let monitor [vote] of citizens-on community
ifelse sum [vote] of citizens-on community >= 0 [set vote-community 1] [set vote-community -1]
set college-vote sentence college-vote vote-community
]
polarizing-count ; compute the standard deviation across districts
set rep-house-seats (length filter [ i -> i = 1 ] college-vote)
set state-maj rep-house-seats / 9 ;
let house-win 0
ifelse state-maj > 0.5 [set house-win 1][set house-win -1]
set house-win-list lput house-win house-win-list
set pop-vote ( sum [vote] of citizens with [vote = 1]) / 63 ;; count popular vote
ifelse pop-vote > 0.5 [set leader "Republican candidate" ][set leader "Democrat candidate"]
end
to gerry-mandaring ;gerrymandering procedure
ifelse pop-vote > 0.5 [ ;if incumbent Republican -> GerryM in favor of Rep and viceversa
if random-float 1 >= 1 - success-rate[ ;possibility of failing to gerrymander ( 0% by default)
ifelse packing-or-craking[
repeat n-times[
republican-count-2 ;packing classification of citizens
republican-tweak-2 ;packing swap of citizens
ask citizens [set community-type 0 ]]
][
repeat n-times[
republican-count-1 ;cracking classification of citizens
republican-tweak-1 ;cacking swap of citizens
ask citizens [set community-type 0 ]]
]
]]
[if random-float 1 >= 1 - success-rate[ ;if incumbent Dem -> GerryM in favor of Dem
ifelse packing-or-craking[
repeat n-times[
democrat-count-2 ;packing classification of citizens
democrat-tweak-2 ;packing swap of citizens
ask citizens [set community-type 0 ]]
][
repeat n-times[
democrat-count-1 ;cracking classification of citizens
democrat-tweak-1 ;cacking swap of citizens
ask citizens [set community-type 0 ]]
]
]]
end
to republican-count-1 ;Cracking Classificationin favor of Rep
let communities nw:louvain-communities
foreach communities[
[community] ->
let strength-community sum [vote] of citizens-on community
ifelse strength-community >= 3 [ask community[set community-type "strong_adv"]] [] ;<= Change here the classification procedure
ifelse (strength-community = -1) or (strength-community = -3) [ask community[set community-type "interesting"]] [] ;<= Change here the classification procedure
]
end
to republican-tweak-1 ; Cracking Swap favor of Rep
if (any? citizens with [(community-type = "strong_adv")]) and (any? citizens with [(community-type = "interesting")])[
ask one-of citizens with [(community-type = "strong_adv") and (vote = 1)] [set vote -1
set color 95 ]
ask one-of citizens with [(community-type = "interesting") and (vote = -1)] [set vote 1
set color 15 ]
]
end
to republican-count-2 ;Packing Classificationin favor of Rep
let communities nw:louvain-communities
foreach communities[
[community] ->
let strength-community sum [vote] of citizens-on community
ifelse (strength-community = -3) or (strength-community = -5) [ask community[set community-type "strong_dis"]] [] ;<= Change here the classification procedure
ifelse (strength-community = -1) or (strength-community = 1) [ask community[set community-type "interesting"]] [] ;<= Change here the classification procedure
]
end
to republican-tweak-2 ;Packing Swap in favor of Rep
if (any? citizens with [(community-type = "strong_dis")]) and (any? citizens with [(community-type = "interesting")])[
ask one-of citizens with [(community-type = "strong_dis") and (vote = 1)] [set vote -1
set color 95 ]
ask one-of citizens with [(community-type = "interesting") and (vote = -1)] [set vote 1
set color 15 ]
]
end
to democrat-count-1 ;Cracking Classification in favor of Dem
let communities nw:louvain-communities
foreach communities[
[community] ->
let strength-community sum [vote] of citizens-on community
ifelse strength-community <= -3 [ask community[set community-type "strong_adv"]] [] ;<= Change here the classification procedure
ifelse (strength-community = 1) or (strength-community = 3) [ask community[set community-type "interesting"]] [] ;<= Change here the classification procedure
]
end
to democrat-tweak-1 ;Cracking Swap in favor of Dem
if (any? citizens with [(community-type = "strong_adv")]) and (any? citizens with [(community-type = "interesting")])[
ask one-of citizens with [(community-type = "strong_adv") and (vote = -1)] [set vote 1
set color 15 ]
ask one-of citizens with [(community-type = "interesting") and (vote = 1)] [set vote -1
set color 95 ]
]
end
to democrat-count-2 ;Packing Classification in favor of Dem
let communities nw:louvain-communities
foreach communities[
[community] ->
let strength-community sum [vote] of citizens-on community
ifelse (strength-community = 3) or (strength-community = 5) [ask community[set community-type "strong_dis"]] []
ifelse (strength-community = 1) or (strength-community = -1) [ask community[set community-type "interesting"]] []
]
end
to democrat-tweak-2 ;Packing Swap in favor of Dem
if (any? citizens with [(community-type = "strong_dis")]) and (any? citizens with [(community-type = "interesting")])[
ask one-of citizens with [(community-type = "strong_dis") and (vote = -1)] [set vote 1
set color 15 ]
ask one-of citizens with [(community-type = "interesting") and (vote = 1)] [set vote -1
set color 95 ]
]
end
to changing-idea ;; Changing idea procedure
ask citizens [
if random-float 1 <= probability-mind-change [
set vote (-1) * vote
ifelse vote = 1 [set color 15] [set color 95]
]
]
end
to polarizing-count ;STD computation across districts
let communities nw:louvain-communities
foreach communities[
[community] ->
ask community [
set pol-community ( sum [vote] of citizens-on community with [ vote = 1]) / 7 ]]
set polarization standard-deviation [pol-community] of citizens
end
to reversal-count ; Procedure to count the number of reversals in the representtives majority sime series
set house-win-list but-first house-win-list
set house-win-list but-first house-win-list ;; remove the two initial zeros
let house-win-list-lag-one but-last house-win-list ; cretae the lag one list
set house-win-list but-first house-win-list ;uniform the number of elements in house-win-list
let difference (map + (house-win-list) (house-win-list-lag-one ))
set reversal length filter [i -> i = 0] difference
end
to count-strong-swing ;procedure to count the number of stron and competitive districts
let communities nw:louvain-communities
foreach communities[
[community] ->
ask community [
set stg-community sum [vote] of citizens-on community]
]
set strong-com (count citizens with [(stg-community = -7) or (stg-community = 7) or (stg-community = -5) or (stg-community = 5) or (stg-community = -3) or (stg-community = 3) ]) / 7
set swing-com (count citizens with [(stg-community = -1) or (stg-community = 1) ]) / 7
end
@#$#@#$#@
GRAPHICS-WINDOW
256
10
653
520
-1
-1
55.7
1
14
1
1
1
0
0
0
1
0
6
0
8
0
0
1
ticks
30.0
BUTTON
10
110
73
143
NIL
setup
NIL
1
T
OBSERVER
NIL
NIL
NIL
NIL
1
BUTTON
115
155
178
188
NIL
go
T
1
T
OBSERVER
NIL
NIL
NIL
NIL
1
BUTTON
25
155
102
188
go once
go
NIL
1
T
OBSERVER
NIL
NIL
NIL
NIL
1
SLIDER
20
200
185
233
probability-mind-change
probability-mind-change
0
0.1
0.1
0.001
1
NIL
HORIZONTAL
SLIDER
20
245
185
278
success-rate
success-rate
0
1
1.0
0.05
1
NIL
HORIZONTAL
PLOT
685
195
990
340
% republican vote (popular vote)
NIL
NIL
0.0
10.0
0.0
1.0
true
false
"" ""
PENS
"pen-1" 1.0 0 -2674135 true "" "plotxy ticks 0.5 "
"pen-2" 1.0 0 -16777216 true "" "plot pop-vote"
MONITOR
15
455
197
500
11 (9 + 2) electoral votes to
leader
17
1
11
PLOT
690
15
990
170
% republican house seats
NIL
NIL
0.0
10.0
0.0
1.0
true
false
"" ""
PENS
"default" 1.0 0 -16777216 true "" "plot state-maj"
"pen-2" 1.0 0 -3844592 true "" "plotxy ticks 0.5"
SLIDER
20
290
185
323
dem-probability
dem-probability
0
1
0.5
0.05
1
NIL
HORIZONTAL
PLOT
685
360
990
515
Standard Deviation of District %
NIL
NIL
0.0
10.0
0.0
0.5
true
false
"" ""
PENS
"default" 1.0 0 -16777216 true "" "plot polarization"
"pen-1" 1.0 0 -1184463 true "" "plot signal / 2"
PLOT
1025
370
1225
520
events
NIL
NIL
0.0
10.0
0.0
1.0
true
false
"" ""
PENS
"default" 1.0 0 -5298144 true "" "plot election"
"pen-1" 1.0 0 -1184463 true "" "plot signal"
SWITCH
25
60
162
93
City-Country
City-Country
1
1
-1000
SWITCH
25
15
202
48
gerrymendering-on
gerrymendering-on
1
1
-1000
MONITOR
15
395
87
440
Reversals
reversal
17
1
11
SWITCH
80
110
235
143
packing-or-craking
packing-or-craking
1
1
-1000
SLIDER
15
340
187
373
n-times
n-times
0
5
2.0
1
1
NIL
HORIZONTAL
PLOT
1025
205
1225
355
Number of strong districts
NIL
NIL
0.0
10.0
0.0
7.0
true
false
"" ""
PENS
"default" 1.0 0 -16777216 true "" "plot strong-com"
"pen-1" 1.0 0 -1184463 true "" "plot signal"
PLOT
1025
50
1225
200
Number of Competitive Districts
NIL
NIL
0.0
10.0
0.0
10.0
true
false
"" ""
PENS
"default" 1.0 0 -16777216 true "" "plot swing-com"
"pen-1" 1.0 0 -1184463 true "" "plot signal"
@#$#@#$#@
## WHAT IS IT?
GerrManderNL aims at assessing the effect of different re-districting strategies in an ideal US state over a 100-year time horizon. The state is composed of 63 citizens grouped into 9 districts. The frame of the model is coherent with what we can observe in the US: two major parties (Republicans and Democrats), representatives elections held every even year and re-districting every ten years. Citizens are allowed to be either Republicans (in red, vote: +1) or Democrats (in blue, vote: -1) and change opinion with a given probability on a yearly basis (MindChange Probability).
Up to now, two famous strategies can be tested: cracking and packing. With cracking, the incumbent party tries to move supporting citizens from strong districts (where it has strong majority) to weak challenged districts where it has no majority but in which there is still the presence of some supporters. In the model, this involves swapping supporters from strong districts with adverse citizens from challenged districts. With packing, the incumbent party tries to pack adverse citizens in districts where the other party is already strong. This, in practice, involves swapping supporters in very weak districts with adversary citizens in challenged districts.
Of course, this is a simplification of how redistricting works. In fact, districts are re-drawn in practice and citizens are not required to move at all. The feasible actions for the gerrymander are in practice more limited than in the model.
NOTE: Be sure that the Nw extension is updated.
## HOW IT WORKS
### Entities, state variables and scales
The model moves along two main dimensions: space and time. Dealing with the space dimension, an ideal federal state is represented by a 9x7 lattice-world in which every node represents a citizen and every row of the lattice represents a district.
Citizens have one state variable "vote" which assumes values 1 if the citizen is inclined to vote for Republicans in a given year or -1 if the citizen is inclined to vote for Democrats in that year.
Moreover, citizen may assume other auxiliary state variables. "Community-type" is one of these and reports the characteristics of the citizen's district to the eyes of the incumbent, (whether a district is composed of strong supporters, strong antagonists or if it is potentially interesting).
For what concerns the time dimension, the horizon of the model is 100 years. In this time lapse, citizens vote for the state governor (incumbent) and for their representatives, change their opinion, and incumbents are allowed to exploit gerrymandering strategies.
### Process Overview and Scheduling
Every year, citizens are allowed to change idea about their voting intention with a given probability, which can be setup though the \emph{MindChange} global variable.
Every even year, representatives elections are held. The representative of a given district is Republican if the majority within the district is Republican and vice versa for Democrats. Additionally the model keeps trace of the popular majority and of the standard deviation of votes composition between districts. The latter can be used as an index of polarization of the state.
Every 10 years, the incumbent (state governor) exploits gerrymandering after that representatives elections are held. For sake of simplicity, the incumbent is the party winning the popular vote in that year. Using the switch "packing-or-cracking", the observer can decide whether to implement one of two strategies, which will be described more in detail in the next subsection.
At the end of the 100-year time horizon, the global variable \emph{reversal} reports the number of reversals happened to the majority of representatives. The lowest the number of reversals the most effective the gerrymandering strategy exploited.
### DESIGN CONCEPTS
The main design concepts I wish to discuss here are: the two gerrymandering strategies implemented and the two possible initial dispositions of citizens.
For what concerns cracking, the strategy is implemented as follows. Every time the incumbent has the possibility to gerrymander, it repeats the following actions n times: 1) it counts how many districts can be classified as strongly supportive (with 5 or more supporters) and how many districts can be classified as interesting (3 supporters and 4 opponents, or 2 supporters and 5 opponents); 2) the gerrymander than chooses one supporter randomly from the strong district and swaps it with one random opponent belonging to the interesting district.
In a similar fashion, packing is implemented as follows. Every time the incumbent has the possibility to gerrymander, it repeats the following actions n times: 1) it counts how many districts can be classified as strongly adverse (with 5 or 6 opponents) and how many districts can be classified as interesting (3 supporters and 4 opponents, or 4 supporters and 3 opponents); 2) the gerrymander than chooses one supporter randomly from the strongly adverse district and swaps it with one random opponent belonging to the interesting district.
For what concerns the initial vote-state of citizens, I allow for two different options. The first and simplest is the random one. Every citizen has a probability "q" of being Republican and a probability "1-q" of being Democrat. By means of a slider ("Dem-probability"), the observe can control the probability of being Democrat. The second initial setup tries to mimic the stylized fact according to which democrats self-segregate in cities while republicans are predominant in rural area. This is done by linking the probability of being republican to the y coordinate by means of a normalizing constant and a convex function. This way, democrats are more concentrated on the lower half of the lattice, hence simulating an ideal city. Of course, different solutions may be preferred.
## HOW TO USE IT
The initialization procedure is rather simple.
1) As a first step, the observer decides whether he or she wants to allow for gerrymandering in the model.
2) Then the observer chooses whether to adopt a random setup of citizens or a city-country setup.
3) After that the decision is about which gerrymandering strategy to use: whether packing or cracking.
4) SETUP & GO
Optionally, the observe can tweak the probability-of-mind-change, the gerrymandering success rate (equal to 1oo% by default), the probability of being democrat (just in the random setup and 50% by default), and the number of rogerrymandering swaps implemented at each census (2 by default).
## THINGS TO NOTICE
Notice that the number of strong districts and standard deviation of districts composition increase on average with the packing strategy, while both decrease with the cracking strategy. Nonetheless, both strategies seems to lead to less reversals than no gerrymandering. This may suggest that both strategies are effective in favoring. incumbents
## THINGS TO TRY
Try to tweak the probability of mind change and see how the previously mentioned results vary. Try also to see the effect of natural seggregation by turning on the city-country switch.
## EXTENDING THE MODEL
The model could be extended and improved in several aspects. On the one hand, it may be interesting to codify different other strategies and study their effect. On the other hand, it may be interesting to add a continuous scale of voting beliefs for citizens ranging from convinced democrat to convinced republican. This way the probability that a citizen changes his/her opinion may vary according to the position along this scale.
## RELATED MODELS
* redistrictingPackNCrack
* NetDistrict by Collin Lysford
* Congressional Redistricting by Luke Elissiry
## HOW TO CITE
If you mention this model or the NetLogo software in a publication, we ask that you include the citations below.
For the model itself:
* Antonio Peruzzi. (2020). "GerryManderNL" "https://github.com/antonioperuzzi94/GerryManderNL"
Please cite the NetLogo software as:
* Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
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