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Opinion Polarization by Learning from Social Feedback Sven Banisch & Eckehard Olbrich MPI for Mathematics in the Sciences (Leipzig) Opinion Dynamics and Collective Decision, Bremen 2017
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Page 1: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Opinion Polarization by Learning from Social Feedback

Sven Banisch & Eckehard OlbrichMPI for Mathematics in the Sciences (Leipzig)

Opinion Dynamics and Collective Decision, Bremen 2017

Page 2: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Opinion Dynamics and Cultural Conflictin European Spaces

WESTERNNATIONALISM

Europeannationalism

White pride

The Golden One

Traditionalist Western Art

The Patriarchy

Smash Cultural Marxism

Unity Rosaries

Freedom of Fear

Be more Swedish

Holland& Barrett Sweden

Norse Art By Micke Johansson

Jack Donovan

Magnanimity

Omniphi

Génération Identitaire

PPDM X Straight Edge X Father Frost Mode X

Beauty

Identity Ireland

Mariusz Pudzianowski

This is Europa

The United Patriots Front

United Patriots Front

Basic Philosophical Re-Education by Blair Cottrell

Maxime Solemn

Right On

Justnationalistgirls

Daniel Friberg

Total War

Nordic Wear

MURDERSCENE

The Aristocrat

Architectural Revival

I'm white

Dennis Michael Lynch

Traditum Orbis Architecture

Imperium

The World Art Nouveau

The Occidental Sentinel

Masterpieces Drawing& Painting

Cultura Classica

Dark and fantastic arts

Counter-Currents PublishingRed Ice

Artaman: The Hyperborean Garden

Golden Century of World Fairs 1851-1951

Art Nouveau& Art Deco

Spells of Art, Gnōsis, and Ritual

The Artists of The Artists Rifles

Europe's Awakening

Anglo American Identity Movement

Women Painters ~

No More Brother Wars

A Handbook of Traditional LivingThe Traditionalist

Nordic Beauty

Real Crusades History

American White History Month 2

Teagan's SmileVoice of One Crying in the Wilderness

Damsel of the Faith

Theotokos Institute for Catholic Studies

A Gentleman's RowFight the New Drug

Institute of Classical Architecture& Art

Scala Regia

Veronica Partridge

The Warden Post

The Soul of the East

Outdoor Research

André Andersson Custom Knives

Smash Cultural Marxism

Refugees Not Welcome In Devon

Soutien au général Piquemal

Smash Freemasonry

Smash Marxismo Cultural

Fascism a misconception

World War II Day by Day

United Patriots Front Productions.

Support the Dover to calais truckers.The Return.

The Populist Party

The Hidden World

Maldon and Heybridge British National Party

Aryan Women in Wheat Fields

World Politics Review

BBC Politics

League of the South

The Beauty of European Girls& Women

Architecture of Ireland

Francis Terry and Associates

Stephen Mitford Goodson

Better Out Than In

The RAMZPAUL Show

Comrade Corbyn: Jeremy Exposed

Nordic Sisterhood

All Nationalist Association& Media

AZL

Labour25

The Conservative Revolution

Pissed Off White Americans

Reporting the anti-white filth on FB

Occidental Offensive

White People World Wide 1

Stop White South African Genocide

White History Month

Eesti Iseseisvuspartei - EIP

Honor USS Liberty Vets

Ukrainian Genocide Famine Foundation - USA

Preserve Our HeritageEuropa Identitaria

Kleinfontein

Orania

Raise awareness of South African farm murders by brutal torture

Smash Cultural Marxism II

Australia First Party Brisbane

American Freedom Party

Smash Usury

The Occidental Observer

I Am Proud To Be British

Remove all EU flags

Praying for South Africa

In Favor Of Traditional Gender Roles

A History of British America

Arktos

Christian Women Against Femen

Nationalism and Religion

The Return of Reason

White Girls who love White Guys II

Українська Естетика

Svoboda - Ukrainian political party

Red October

Traditionalist Youth Hour Podcast

Political Correctness Must Die

Fed Up White Person

BudweiserConservative Voices

Nordic gods The South Africa Project

Open Borders for Israel

Nordic Brotherhood

Censorbugbear Reports

Prosper in Israel

Heterosexual Awareness Month 3

Endecja Podlaskie

Political Correctness Gone Wild

Proud to be White does Not Mean Racist

Support Human Rights for Whites 2

Letters From White South Africa

Enough is Enough: Stop Homosexual Promotion.

This is Italy

European Independent Media Centre

Hellenic Victims of Communism - ĬȪȝĮIJĮ IJȠȣ ȀȠȝȝȠȣȞȚıȝȠȪ

Nationalists of the World Unite

This is Austria

This is Ireland

By The People For The People

Be Active Front USA 2

Traditional Britain Group

Aryan Hyperborean Heritage

Generation Identity London

BNPtv

British National Party

CasaPound Italia

This is Sweden

This is the Netherlands

This is Germany

▶ online and offline data▶ precision language processing▶ dynamic graph theory▶ media geography

▶ computational modelling ▶ game theory▶ conceptual spaces▶ divergent mindsets

Embedding into (latent) political space

argu

men

ts o

n a

set of issues

resulting positions

on the set of issues

argu

men

ts o

n a

set of issues

resulting positions

on the set of issues

argu

men

ts o

n a

set of issues

resulting positions

on the set of issues

Over the course of a discourse not all the issues playing a role are visible from the very beginning. New issues may arise, first-time issues may cease to be central, and sometimes very few controverse points turn out and become the key issues

A controversy, most often, involves a series of issues that are logically and cognitively linked. A proper spatial representation of such complexity should therefore take into account the

multi-dimensionality and interconnectedness of the points at stake.

We propose to model this by a network the nodes of which are key issues in a debate and the links weights for relations in between those.

Over the course of a discourse not all the issues playing a role are visible from the very beginning. New issues may arise, first-time issues may cease to be central, and sometimes very few controverse points turn out and become the key issues

across the participants and this has important consequences for the process of finding compromise. In a prolonged debate the importance of and relations between the dimensions of it are subject to cognitive negotiations as well.

on which final decisions are based. Importantly, the weights assigned to the different issues as well as the understanding about their mutual relatedness differ

multi-dimensionality and interconnectedness of the points at stake.

We propose to model this by a network the nodes of which are key issues in a debate and the links weights for relations in between those.

multi-dimensionality and interconnectedness of the points at stake.

We propose to model this by a network the nodes of which are key issues in a debate and the links weights for relations in between those.

multi-dimensionality and interconnectedness of the points at stake.

We propose to model this by a network the nodes of which are key issues in a debate and the links weights for relations in between those.

multi-dimensionality and interconnectedness of the points at stake.

We propose to model this by a network the nodes of which are key issues in a debate and the links weights for relations in between those.

For a debate to be fruitful the emergence of a common understanding of the structure underlying a controversy is necessary.

A controversy, most often, involves a series of issues that are logically and cognitively linked. A proper spatial representation of such complexity should therefore take into account the

H2020 – FETPROACT-2016 - 732942

odycceus.eu

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Polarization: an important but also controversial issue

Page 4: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

The Puzzle of Polarization

» If people tend to become alike in their beliefs, attitudes, and behavior when they interact, why do not all such differences eventually disappear? « (Axelrod, 1997)

» Since universal ultimate agreement is an ubiquitous outcome of a very broad class of mathematical models, we are naturally led to inquire what on earth one must assume in order to gene-rate the bimodal outcome of community cleavage studies. « (Abelson, 1964)

▶ These questions have inspired a lot of modelling work throug-hout decades: the field is now known as Opinion Dynamics

Page 5: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Terminology

-

)

mainly -

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Mechanisms

Page 7: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Mechanisms → Outcomes

social influence + status homophily → consensus (static networks, traditional models, binary models)

social influence + opinion homophily → plurality with

Positive + negativ social influence → polarization (Bourdieu: cultural/aesthetic differentiation)

influence + opinion homophily → consensus Argument persuasion + opinion homophily → polarizationBiased assimilation + opinion homophily → polarization

Page 8: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Polarization Dynamics by Learning from Social Feedback Social feedback + status homophily → polarization

Ƒ or Ŷ social neighborhood

or non-confirming response depending on the current opinion in the neighborhood

Q(Ƒ) Q(Ŷ)

Page 9: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Rooted in Reinforcement Learning

→ → r = -1 i i

j

i = oi oj Ƒ = 1 Ŷ = -1

4.

Q(Ƒ) Q(Ŷ)

o

Page 10: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Micro- and Macrodynamics▶ Selected individual trajectories (spatial random graph)

Q(Ƒ) Q(Ŷ)

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0 20 40 60 80 100��

0

���

���

���

���

1

0 20 40 60 80 100 0 20 40 60 80 100

Agent 29 Agent 17 Agent 69

time time time

value

Q(1)4��

opinion change

Page 11: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

time (every Nth step)

∆V =

V(Ƒ

)-V(Ŷ

)

Polarization Dynamics▶ Value difference ∆Q = Q(Ƒ)-Q(Ŷ) can be interpreted as

strength of support (interval in continuous models is usually interpreted this way)

Q(Ƒ) Q(Ŷ)

emergence of two opposing opinion groups

Page 12: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Micro- and Macrodynamics▶ Macroscopic polarization measures (DiMaggio et al. 1996)

Q(Ƒ) Q(Ŷ)

0 10 20 30 40 50 60 70 80 90 1000

0.2

0.4

0.6

0.8

1

time (every Nth step)

Mac

rosc

opic

Mea

sure

s

6XSSRUW�6WUHQJWK�ï�Support Strength 1DispersionBimodalityAverage OpinionDissimilarity

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Mathematical Characterizations

»solve«-

Q(Ƒ) Q(Ŷ)

Page 14: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Opinion Games I

→ oN → -oN

ε > 0 *i oN | *

i -oN

i *i

*i oN

→ !

Q(Ƒ) Q(Ŷ)

Page 15: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Opinion Games IIA B

A B A B = 1)→ » «→

Page 16: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Convergence of Q-LearningDynamical evolution of two agents A and B each linked

to a fixed community of opposed sign (kA = 10, kB = 2) ▶ Quick convergence to the payoffs of the associated opinion game

0 100 200 300 400 500 600 700 800 900 1000ï�

��

0

���

1

���

2

time (every 10th step)

6 Q

i

6QB6QA6/A6/B

0 100 200 300 400 500 600 700 800 900 1000ï�

��

��

��

��

0

���

���

���

���

1

time (every 10th step)

Qi(o

i)

QB��

QB(1)QA��

QA(1)/B(1)/B��

/A��

/A(1)

A B

Page 17: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Cohesive Sets and Stable Play

1 and S

1 and S S1 S

Q(Ƒ) Q(Ŷ)

Page 18: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

On Two-Island Graphs

Page 19: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

On Two-Island Graphs▶ qM and qL probability that one agent is less

than 1/2-cohesive▶ There are M respectively L agents in the communities and probability that

none of them is less than 1/2-cohesive is

M = 10 | L = 10

M = 10 | L = 50

M = 50 | L = 100

M = 50 | L = 50

M = 100 | L = 100

M = 500 | L = 500

p

Pr[S

M a

nd S

L ½-c

ohes

ive]

NetworkRealizations

Theory

▶ Comparison of the theoretical results with an average over 100 network realizations

▶ Notice that for large system limit there is a sharp transition at p = 1/2

Page 20: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Stability of Polarization on Two-Island Graphs▶ Polarized initialization of the two-community network such that agents in

M maximally support 1 and agents in L support -1▶ 100 model realizations à

20000 × N steps▶ Combinatorial analysis of

cohesiveness provides reasona-ble approximation

▶ Loss of stability for smaller p in large systems due to finite-size fluctuations (learning rate)

p

Pr[ s

tabl

e po

lari

zatio

n ]

M = L = 10M = L = 50M = L = 100M = L = 500

Cohesiveness:

M = L = 10M = L = 50M = L = 100M = L = 500

Simulations:

Page 21: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 10

0.2

0.4

0.6

0.8

1

modularity

pola

rizat

ion

| con

sens

us p

roba

bilit

y

Consensus Prob. N = 100Consensus Prob. N = 500Consensus Prob. N = 1000Polarization N = 100Polarization N = 500Polarization N = 1000

On Socio-Structural Conditions for Polarization▶ Polarization measure (Flache/Macy) and consensus probability as

a function of molularity in a stochastic block model (C = 10)▶ (Almost) linear relation between modularity and polarization

indicative ofmeaningful

communities

Page 22: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

A Bigger Picture (Discussion)

» «

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The Big Picture (Discussion)

Page 24: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Conclusion V(Ƒ) V(Ŷ)

Page 25: Opinion Polarization by Learning from Social Feedbackodcd2017.user.jacobs-university.de/wp-content/uploads/2017/07/Ban… · Opinion Polarization by Learning from Social Feedback

Online Demonstrations▶ www.universecity.de/demos/OpinionValuesSmall.html

▶ In


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