mathematical theory of democracy and its applications 3. applications
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Mathematical theory of democracy and its applications
3. Applications
Andranik TangianHans-Böckler Foundation, Düsseldorf
University of Karlsruheandranik-tangian@boeckler.de
2
Plan of the courseThree blocks :
1. BasicsHistory, Arrow‘s paradox, indicators of representativeness, solution
2. Fundamentals:
Model of Athens governance (president, assembly, magistrates, courts) and German Bundestag (parties and coalitions)
3. Applications
MCDM, traffic control, financies
3
Questions
' '
dichotomous questions (Y/N answers)
total number of questions
{ } -vector of question weights -
probability measure:
non-negativity: 0 for all
additivity:
normality: 1
Equ
q
q
Q qq Q
q
m
µ m
µ q
µ µ
µ
µ
al weights 1/ qµ m
4
Individuals
individuals (Athenian citizens)
total number of individuals
{ } -vector of individual weights - probability
Equal chances 1/
{ } ( )-matrix of 1 opinions of
individuals on questions
i
i
iq
i
n
n
n
a n m
i q
ν
A
a A { } -vector balance of opinions -
predominance of protagonists over antagonists
qa m'ν
5
Candidates
candidates
total number of candidates
{ } -vector of candidate weights-probability
Equal chances 1/
{ } ( )-matrix of 1 opinions of
candidates on questions
{ } -vector balance
c
c
cq
q
c
N
N
N
b N m
c q
b m
ξ
B
b B'ξ of
candidate opinions
6
RepresentativenessThe size of group with the same opinion:
weight of protagonists if 1
weight of antagonists if 1
representativeness of on iq cq
cq
cqcq
ii a b
br
b
c q
Protagonists ai1=1
Example: b11 = 1, b12 = -1; r1q shown by color
Antagonists aiq=-1
ai2=1 ai2=-1ai2=-1 ai2=1
q1
q2
7
Indicator of popularity – „spatial“ representativeness
Average size of the group represented:
P popularity of
P P expected popularity
of a candidate selected by lot
c q cqq
c cc
r c
8
Indicator of universality –„temporal“ representativeness
0 5
Frequency of representing a majority:
U round[ ] universality of
U U expected universality
of a candidate selected by lot
cq
c q q cqq r q
c cc
r c
9
Indicator of goodness – „specific“ representativeness
c
Average ratio "group represented-to-majority":
G goodness of weight of majority for
G expected goodness
of a candidate selected by lot
cqq
q
c cc
rc
q
G
10
MCDM: FU-Hagen outing Candidate
destinations 1. Casino of
Hohensyburg
2. Hagen open air museum
3. Wiblingwerde
4. Schmallenberg
5. Soest
6. Münster
7. Cologne opera
5
6
4
32
1
7
11
Team wishes (individual opinions)
12
Indicators of candidate destinations
13
2-year plans to satisfy a majority once in each respect (cabinets)
14
3- and 5-year plans to satisfy a majority in each respect half
the times (parliaments)
15
Traffic control
10
11
1 13
12
14
15
1917
18
23
5
9
876
4
20
16
Surveillance cameras
1-5 Hagen Ring
6-20 Important inter-sections
16
Prevailing traffic flow at the Hagen ring during the lunch time
+ clockwise traffic flow prevails
- counterclockwise traffic flow prevails
17
Traffic flows all over Hagen+/- main traffic flow outside the Ring is towards/outside the city
18
Intersection representativeness
19
Best collective predictors of the Ring flow and statistical tests
20
Predicting DAX from DJ
NYSE Frankfurter Börse
15:30
21
Week changes of DAX stock prices
22
Week changes of DJ stock prices
23
Anticipative representativeness of DJ stocks
24
Best DAX collective predictors and statistical tests
25
General summaryInstruments: Indicators of representativeness:
popularity, universality, goodness, …
Theoretical implications: Arrow’s paradox, selection of representatives by lot, estimation of size of representative bodies, analogy with forces in physics, inefficiency of democracy in unstable society
Societal applications: finding best candidates, parties, and coalitions
Non-societal applications: MCDM, traffic control, finances (finding compromises, finding predictors, …)
Calculus instead of logic check: finding optimal compromises, whereas ‘yes’–‘no’ logic retains only unobjectionable solutions
26
SourcesTangian A. (2003) MCDM-Applications of the Mathematical Theory of
Democracy: Choosing Travel Destginations, Preventing Traffic Jams, and Predicting Stock Exchange Trends. FernUniversität Hagen, Discussion Paper 333
Tangian A.S. (2007) Selecting predictors for traffic control by methods of the mathematical theory of democracy. European Journal of Operational Research, 181, 986–1003
Tangian A.S. (2008) Predicting DAX trends from Dow Jones data by methods of the mathematical theory of democracy. European Journal of Operational Research, 185, 1632--1662
Tangian A. (2008) A mathematical model of Athenian democracy. Social Choice and Welfare, 31, 537 – 572. First version:
Tangian A. (2005) Decision making in politics and economics: 1. Mathematical model of classical democracy. University of Karlsruhe. Discussion Paper. http://www.wior.uni-karlsruhe.de/LS_Puppe/Personal/Papers-Tangian/modelclassdem.pdf
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