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City election prediction.nlogo
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City election prediction.nlogo
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extensions[nw]
breed [candidates candidate] ;; people who are in some electoral list; obviously, they never change their preference.
breed[people person] ;; the simple citizen who must chose a preference.
globals [
A_other ;; this variable is a consequence of the other ones' value.
B_other ;; this variable is a consequence of the other ones' value.
C_other ;; this variable is a consequence of the other ones' value.
]
people-own [
impartial? ;; person who has no preference
A_side? ;; person who will vote for Mayor A in the election
B_side? ;; person who will vote for Mayor B in the election
C_side? ;; person who will vote for Mayor C in the election
charisma
cogency
weakness ;; how susceptible each person is to being convinced.
social ;; il peso che ogni persona da alle iterazioni sociali.
young? ;; if a person is younger than 35.
senior? ;; if a person is older than 65.
satisfied? ;; person who is satisfied of the current Mayor.
dissatisfied? ;; person who is not satisfied of the current Mayor.
;; The utility function of each person is a linear combination of the variables of each mayor and the coefficients
;; (weights) representing the importance (in percentage) that each person gives to each variable.
A_utility ;; utility function calculated on the mayor's choices of variable values A.
B_utility ;; utility function calculated on the mayor's choices of variable values B.
C_utility ;; utility function calculated on the mayor's choices of variable values C.
p1 ;; the weight that each person gives to job varible.
p2 ;; the weight that each person gives to tax varible.
p3 ;; the weight that each person gives to reg (urban regeneration) varible.
p4 ;; the weight that each person gives to vis (visibility of town) varible.
p5 ;; the weight that each person gives to others varibles.
]
candidates-own[
impartial? ;; person who has no preference
A_side? ;; person who will vote for Mayor A in the election
B_side? ;; person who will vote for Mayor B in the election
C_side? ;; person who will vote for Mayor C in the election
charisma
cogency
weakness ;; how susceptible each person is to being convinced.
social ;; il peso che ogni persona da alle iterazioni sociali
A_utility ;; utility function calculated on the mayor's choices of variable values A.
B_utility ;; utility function calculated on the mayor's choices of variable values B
C_utility ;; utility function calculated on the mayor's choices of variable values C.
]
to setup
clear-all
set A_other 0
set B_other 0
set C_other 0
if (perc_youth + perc_seniority > 100)
[
user-message (word
"The sum of perc_youth and perc_seniority "
"should not be greater than 100.")
stop
]
if (perc_satisfied + perc_dissatisfied > 100)
[
user-message (word
"The sum of perc_satisfied and perc_dissatisfied "
"should not be greater than 100.")
stop
]
if A_job + A_tax + A_reg + A_vis > 100
[
user-message (word
"The sum of A_job, A_tax, A_reg, A_vis and A_other "
"should not be greater than 100.")
stop
]
if B_job + B_tax + B_reg + B_vis > 100
[
user-message (word
"The sum of B_job, B_tax, B_reg, B_vis and B_other "
"should not be greater than 100.")
stop
]
if C_job + C_tax + C_reg + C_vis > 100 [
user-message (word
"The sum of C_job, C_tax, C_reg, C_vis and C_other "
"should not be greater than 100.")
stop
]
if A_job + A_tax + A_reg + A_vis < 100
[
set A_other 100 - (A_job + A_tax + A_reg + A_vis)
]
if B_job + B_tax + B_reg + B_vis < 100
[
set B_other 100 - (B_job + B_tax + B_reg + B_vis)
]
if C_job + C_tax + C_reg + C_vis < 100
[
set C_other 100 - (C_job + C_tax + C_reg + C_vis)
]
setup-people
setup-spatially-clustered-network
ask links
[
set color white
]
if (show-initial-preferences = TRUE)
[
choose-preference
]
reset-ticks
end
to random-setup
clear-all
if (perc_youth + perc_seniority > 100)
[
user-message (word
"The sum of perc_youth and perc_seniority "
"should not be greater than 100.")
stop
]
if (perc_satisfied + perc_dissatisfied > 100)
[
user-message (word
"The sum of perc_satisfied and perc_dissatisfied "
"should not be greater than 100.")
stop
]
set-random-percentage
setup-people
setup-spatially-clustered-network
ask links
[
set color white
]
if (show-initial-preferences = TRUE)
[
choose-preference
]
reset-ticks
end
;; the 'random setup' procedure randomly sets the percentage values assigned to the variables of each mayor.
to go
choose-preference
influence
tick-advance 0.3
update-plots
end
to set-random-percentage
set A_job 50
set A_tax 50
set A_reg 50
set A_vis 50
while [A_job + A_tax + A_reg + A_vis >= 100]
[
set A_job random 100
set A_tax random 100
set A_reg random 100
set A_vis random 100
set A_other (100 - A_job - A_tax - A_reg - A_vis)
]
set B_job 50
set B_tax 50
set B_reg 50
set B_vis 50
while [B_job + B_tax + B_reg + B_vis >= 100]
[
set B_job random 100
set B_tax random 100
set B_reg random 100
set B_vis random 100
set B_other (100 - B_job - B_tax - B_reg - B_vis)
]
set C_job 50
set C_tax 50
set C_reg 50
set C_vis 50
while [C_job + C_tax + C_reg + C_vis >= 100]
[
set C_job random 100
set C_tax random 100
set C_reg random 100
set C_vis random 100
set C_other (100 - C_job - C_tax - C_reg - C_vis)
]
end
;; The 'set-random-percentage' procedure focuses on setting the 'other' variables in such a way that the sum of
;; the percentages of all variables for each mayor equals 100.
to setup-people
set-default-shape turtles "person"
;; with the following procedure we create the people who will choose a preference.
create-people (number-of-citizens - initial_A_side - initial_B_side - initial_C_side)
[
setxy (random-xcor * 0.95) (random-ycor * 0.95) ;; multipling by 0.95 for not to be too close to the edge
set size 1.5
become-impartial ;; at the time it is generated, each person is set, for simplicity's sake, impartial.
set young? false ;; at the time it is generated, each person is set, for simplicity's sake, not young.
set senior? false ;; at the time it is generated, each person is set, for simplicity's sake, not senior.
]
;; We now set the desired number of young and old people.
ask n-of (perc_youth / 100 * count people) people
[
become-young
]
ask n-of (perc_seniority / 100 * count people) people with [young? = FALSE]
[
become-senior
]
;; with the following procedure, we create the people who belong to the electoral list of Mayor A.
create-candidates initial_A_side
[
setxy (random-xcor * 0.95) (random-ycor * 0.95) ;; multipling by 0.95 for not to be too close to the edge
set size 1.5
become-A_side
set A_utility 100
set B_utility 0
set C_utility 0
]
;; with the following procedure, we create the people who belong to the electoral list of Mayor B.
create-candidates initial_B_side
[
setxy (random-xcor * 0.95) (random-ycor * 0.95) ;; multipling by 0.95 for not to be too close to the edge
set size 1.5
become-B_side
set A_utility 0
set B_utility 100
set C_utility 0
]
;; with the following procedure, we create the people who belong to the electoral list of Mayor C.
create-candidates initial_C_side
[
setxy (random-xcor * 0.95) (random-ycor * 0.95) ;; multipling by 0.95 for not to be too close to the edge
set size 1.5
become-C_side
set A_utility 0
set B_utility 0
set C_utility 100
]
;; with the following procedure we ask people to choose an initial preference based on their age and the weights
;; they each give to the variables.
ask people
[
if (young? = TRUE)
[
setup-preference-youth
]
if (senior? = TRUE)
[
setup-preference-seniority
]
if (senior? = FALSE and young? = FALSE)
[
setup-preference
]
set A_utility (p1 * A_job / 10 + p2 * A_tax / 10 + p3 * A_reg / 10 + p4 * A_vis / 10 + p5 * A_other / (number-other-variables * 10))
set B_utility (p1 * B_job / 10 + p2 * B_tax / 10 + p3 * B_reg / 10 + p4 * B_vis / 10 + p5 * B_other / (number-other-variables * 10))
set C_utility (p1 * C_job / 10 + p2 * C_tax / 10 + p3 * C_reg / 10 + p4 * C_vis / 10 + p5 * C_other / (number-other-variables * 10))
]
ask turtles
[
set charisma random-float 1
set cogency ( nw:eigenvector-centrality + charisma ) ;; congency feature for each person is made up of his notoriety
;; (eigenvector centrality) and his cherisma.
set weakness (random-float 1)
set social (random-float 1) ;;
]
;; in the event that one of the three candidate mayors is the current mayor, the satisfied persons will have an increase in the
;; corresponding utility function of an addend that can at most reach a value of 10 (as well as all other addends of the utility
;; function).
if (current_mayor? = TRUE)
[
ask people [
set satisfied? false
set dissatisfied? false
]
ask n-of (perc_satisfied / 100 * count people) people
[
become-satisfied
]
ask n-of (perc_dissatisfied / 100 * count people) people with [satisfied? = FALSE]
[
become-dissatisfied
]
if (current_mayor = "A")
[
ask people with [satisfied?]
[
let satisfaction_value random-float 10
set A_utility (A_utility + satisfaction_value)
]
ask people with [dissatisfied?]
[
let dissatisfaction_value random-float 10
set A_utility (A_utility - dissatisfaction_value)
]
]
if (current_mayor = "B")
[
ask people with [satisfied?]
[
let satisfaction_value random-float 10
set B_utility (B_utility + satisfaction_value)
]
ask people with [dissatisfied?]
[
let dissatisfaction_value random-float 10
set B_utility (B_utility - dissatisfaction_value)
]
]
if (current_mayor = "C")
[
ask people with [satisfied?]
[
let satisfaction_value random-float 10
set C_utility (C_utility + satisfaction_value)
]
ask people with [dissatisfied?]
[
let dissatisfaction_value random-float 10
set C_utility (C_utility - dissatisfaction_value)
]
]
]
end
;; the following procedures generate the coefficients (weights) that each person gives to each variable, they differ from each other based on
;; the age of the persons. The procedures are such that the sum of the percentages equals 100.
to setup-preference
set p1 0.5
set p2 0.5
set p3 0.5
set p4 0.5
while [p1 + p2 + p3 + p4 >= 1] ;; older people give more weight to urban regeneration variable than job and visibility ones.
[
set p1 random-float 1 ;; weight associated to job variable
set p2 random-float 1 ;; weight associated to tax variable
set p3 random-float 1 ;; weight associated to urban regeneration variable
set p4 random-float 1 ;; weight associated to visibility variable
set p5 (1 - p1 - p2 - p3 - p4) ;; weight associated to other variable
]
end
to setup-preference-seniority
set p1 0.5
set p2 0.5
set p3 0.5
set p4 0.5
while [p1 + p2 + p3 + p4 >= 1 or p3 <= p1 or p3 <= p4] ;; older people give more weight to urban regeneration variable than job
[ ;; and visibility ones.
set p1 random-float 1 ;; weight associated to job variable
set p2 random-float 1 ;; weight associated to tax variable
set p3 random-float 1 ;; weight associated to urban regeneration variable
set p4 random-float 1 ;; weight associated to visibility variable
set p5 (1 - p1 - p2 - p3 - p4) ;; weight associated to other variable
]
end
to setup-preference-youth
set p1 0.5
set p2 0.5
set p3 0.5
set p4 0.5
while [p1 + p2 + p3 + p4 >= 1 or p1 <= p3 or p4 <= p3] ;; the youth give more weight to job and visibility variables than urban regeneration one.
[
set p1 random-float 1 ;; weight associated to job variable
set p2 random-float 1 ;; weight associated to tax variable
set p3 random-float 1 ;; weight associated to urban regeneration variable
set p4 random-float 1 ;; weight associated to visibility variable
set p5 (1 - p1 - p2 - p3 - p4) ;; weight associated to other variable
]
end
to setup-spatially-clustered-network
let num-links (average-node-degree * number-of-citizens) / 2
while [count links < num-links ]
[
ask one-of turtles
[
let choice (min-one-of (other turtles with [not link-neighbor? myself])
[distance myself])
if choice != nobody [ create-link-with choice ]
]
]
end
to choose-preference
ask people
[
ifelse (A_utility > B_utility)
[
ifelse (A_utility > C_utility)
[
become-A_side
]
[
become-C_side
]
]
[
ifelse (B_utility > C_utility)
[
become-B_side
]
[
become-C_side
]
]
if (
abs (A_utility - B_utility) <= (p1 + p2 + p3 + p4 + p5) / 5 or
abs (C_utility - B_utility) <= (p1 + p2 + p3 + p4 + p5) / 5 or
abs (A_utility - B_utility) <= (p1 + p2 + p3 + p4 + p5) / 5 )
[
become-impartial
]
]
end
;; the 'choise-preference' procedure makes people rational. In fact, they satisfy the completeness property, i.e. they are able to choose their preference for each pair of values
;; assumed by the utility functions, and the transitive property.
to influence
ask people with [A_side?]
[
ask n-of ceiling (count link-neighbors * influence-neighbors / 100) link-neighbors
[
if ([weakness] of self < [cogency] of myself)
[
set A_utility (A_utility + (social) * ([cogency] of myself - [weakness] of self)); each person update his utility functions based on the influence of his neighbors
]
]
]
ask people with [B_side?]
[
ask n-of ceiling (count link-neighbors * influence-neighbors / 100) link-neighbors
[
if ([weakness] of self < [cogency] of myself)
[
set B_utility (B_utility + (social) * ([cogency] of myself - [weakness] of self)); each person update his utility functions based on the influence of his neighbors
]
]
]
ask people with [C_side?]
[
ask n-of ceiling (count link-neighbors * influence-neighbors / 100) link-neighbors
[
if ([weakness] of self < [cogency] of myself)
[
set C_utility (C_utility + (social) * ([cogency] of myself - [weakness] of self)); each person update his utility functions based on the influence of his neighbors
]
]
]
end
;; the 'influence' procedure initiates influence between people. Influence between two individuals is successful if the 'cogency' value of the person to be convinced
;; exceeds the 'weakness' value of the target person.
to watch-most-influential
ask max-one-of turtles [cogency]
[
set size 2 watch-me
]
end
;; the following procedure emulates a situation in which news of a scandal concerning one of the mayors running for office (whether it is real news or fake-news) comes into the town.
to scandal
if (mayor_scandal = "A")
[
if (scandal-intesity = "low")
[
ask n-of (scandal-popularity / 100 * count people) people
[
let scandal-value (25 / 100 * A_utility)
set A_utility (A_utility - random-float scandal-value) ;; a low scandal could decrease a voter's interest in a mayor A by at most 25 percent of the initial interest.
]
]
if (scandal-intesity = "medium")
[
ask n-of (scandal-popularity / 100 * count people) people
[
let scandal-value (50 / 100 * A_utility)
set A_utility (A_utility - random-float scandal-value) ;; a medium scandal could decrease a voter's interest in a mayor A by at most 50 percent of the initial interest.
]
]
if (scandal-intesity = "high")
[
ask n-of (scandal-popularity / 100 * count people) people
[
let scandal-value (80 / 100 * A_utility)
set A_utility (A_utility - random-float scandal-value) ;; a high scandal could decrease a voter's interest in a mayor A by at most 80 percent of the initial interest.
]
]
]
if (mayor_scandal = "B")
[
if (scandal-intesity = "low")
[
ask n-of (scandal-popularity / 100 * count people) people
[
let scandal-value (25 / 100 * B_utility)
set B_utility (B_utility - random-float scandal-value) ;; a low scandal could decrease a voter's interest in a mayor B by at most 25 percent of the initial interest.
]
]
if (scandal-intesity = "medium")
[
ask n-of (scandal-popularity / 100 * count people) people
[
let scandal-value (50 / 100 * B_utility)
set B_utility (B_utility - random-float scandal-value) ;; a medium scandal could decrease a voter's interest in a mayor B by at most 50 percent of the initial interest.
]
]
if (scandal-intesity = "high")
[
ask n-of (scandal-popularity / 100 * count people) people
[
let scandal-value (80 / 100 * B_utility)
set B_utility (B_utility - random-float scandal-value) ;; a high scandal could decrease a voter's interest in a mayor B by at most 80 percent of the initial interest.
]
]
]
if (mayor_scandal = "C")
[
if (scandal-intesity = "low")
[
ask n-of (scandal-popularity / 100 * count people) people
[
let scandal-value (25 / 100 * C_utility)
set C_utility (C_utility - random-float scandal-value) ;; a low scandal could decrease a voter's interest in a mayor C by at most 25 percent of the initial interest.
]
]
if (scandal-intesity = "medium")
[
ask n-of (scandal-popularity / 100 * count people) people
[
let scandal-value (50 / 100 * C_utility)
set C_utility (C_utility - random-float scandal-value) ;; a medium scandal could decrease a voter's interest in a mayor C by at most 50 percent of the initial interest.
]
]
if (scandal-intesity = "high")
[
ask n-of (scandal-popularity / 100 * count people) people
[
let scandal-value (80 / 100 * C_utility)
set C_utility (C_utility - random-float scandal-value) ;; a high scandal could decrease a voter's interest in a mayor C by at most 80 percent of the initial interest.
]
]
]
end
to become-satisfied
set satisfied? true
set dissatisfied? false
end
to become-dissatisfied
set satisfied? false
set dissatisfied? true
end
to become-young
set young? true
set senior? false
end
to become-senior
set young? false
set senior? true
end
to become-impartial
set impartial? true
set A_side? false
set B_side? false
set C_side? false
set color blue
end
to become-A_side
set impartial? false
set A_side? true
set B_side? false
set C_side? false
set color red
end
to become-B_side
set impartial? false
set A_side? false
set B_side? true
set C_side? false
set color yellow
end
to become-C_side
set impartial? false
set A_side? false
set B_side? false
set C_side? true
set color green
end
@#$#@#$#@
GRAPHICS-WINDOW
400
10
1878
907
-1
-1
14.56
1
10
1
1
1
0
1
1
1
-50
50
-30
30
0
0
1
ticks
30.0
SLIDER
0
43
198
76
initial_A_side
initial_A_side
4
20
13.0
1
1
NIL
HORIZONTAL
BUTTON
200
755
318
804
NIL
setup
NIL
1
T
OBSERVER
NIL
NIL
NIL
NIL
1
SLIDER
0
10
399
43
number-of-citizens
number-of-citizens
500
1500
785.0
5
1
NIL
HORIZONTAL
SLIDER
199
76
399
109
average-node-degree
average-node-degree
5
25
13.0
1
1
NIL
HORIZONTAL
SLIDER
0
76
199
109
initial_B_side
initial_B_side
4
20
14.0
1
1
NIL
HORIZONTAL
SLIDER
198
43
399
76
initial_C_side
initial_C_side
4
20
12.0
1
1
NIL
HORIZONTAL
SLIDER
0
606
200
639
A_job
A_job
0
100
15.0
1
1
%
HORIZONTAL
SLIDER
0
639
200
672
B_job
B_job
0
100
15.0
1
1
%
HORIZONTAL
SLIDER
0
672
200
705
C_job
C_job
0
100
19.0
1
1
%
HORIZONTAL
SLIDER
200
606
399
639
A_tax
A_tax
0
100
36.0
1
1
%
HORIZONTAL
SLIDER
0
705
200
738
A_reg
A_reg
0
100
5.0
1
1
%
HORIZONTAL
SLIDER
0
804
200
837
A_vis
A_vis
0
100
30.0
1
1
%
HORIZONTAL
SLIDER
200
639
399
672
B_tax
B_tax
0
100
44.0
1
1
%
HORIZONTAL
SLIDER
200
672
399
705
C_tax
C_tax
0
100
17.0
1
1
%
HORIZONTAL
SLIDER
0
738
200
771
B_reg
B_reg
0
100
14.0
1
1
%
HORIZONTAL
SLIDER
0
771
200
804
C_reg
C_reg
0
100
33.0
1
1
%
HORIZONTAL
SLIDER
0
837
200
870
B_vis
B_vis
0
100
11.0
1
1
%
HORIZONTAL
SLIDER
0
870
200
903
C_vis
C_vis
0
100
13.0
1
1
%
HORIZONTAL
BUTTON
318
755
399
804
NIL
go
T
1
T
OBSERVER
NIL
NIL
NIL
NIL
0
PLOT
0
276
399
438
Temporary Scores
time
# scores
0.0
10.0
0.0
100.0
true
true
"" ""
PENS
"A_side" 1.0 0 -2674135 true "" "plot count turtles with [A_side?]"
"B_side" 1.0 0 -1184463 true "" "plot count turtles with [B_side?]"
"C_side" 1.0 0 -10899396 true "" "plot count turtles with [C_side?]"
"Impartial" 1.0 0 -13345367 true "" "plot count turtles with [impartial?]"
MONITOR
0
438
57
483
A_side
count turtles with [A_side?]
17
1
11
MONITOR
57
438
114
483
B_side
count turtles with [B_side?]
17
1
11
MONITOR
114
438
171
483
C_side
count turtles with [C_side?]
17
1
11
MONITOR
171
438
242
483
impartial
count turtles with [impartial?]
17
1
11
BUTTON
199
705
399
755
NIL
random-setup
NIL
1
T