Agent-based modeling of social conflict, civil violence and revolution: state-of-the-art review and further prospects Carlos Lemos 1,2,3, Helder Coelho

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Agent-based modeling of social conflict, civil violence and revolution: state-of-the-art review and further prospects

Carlos Lemos1,2,3, Helder Coelho2, Rui J. Lopes3,4

1 Instituto de Estudos Superiores Militares (IESM), Lisbon, Portugal

2 Faculty of Sciences of the University of Lisbon, Portugal3 Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal

4 Instituto de Telecomunicações IT-IUL, Lisbon, Portugal

EUMAS2013 – Toulouse 12/14 December 2013

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http://www.bing.com/images/search?q=manifesta%c3%a7%c3%a3o+15+setembro+parlamento+mulher+nua&view=detail&id=5D88C21BFC553882E91EC717DDA46BD213F08FBA

http://1.bp.blogspot.com/-v0yD5CrO8Fw/UFb4b8BmT3I/AAAAAAAAV6s/Djq5mzkqM4M/s1600/222222.jpghttp://db2.stb.s-msn.com/i/7B/F35CB26D513744D8A788DD7E24A8B.jpg

http://www.meiosepublicidade.pt/wp-content/uploads/2012/11/carga-policial-300x222.jpg

CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK)


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QUESTIONS:

How do large protest demonstrations form and how can these turn to violent confrontation?

How do protest demonstrations change the social and political context?

Can these links be understood? Predicted? Controlled?



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SOCIAL CONFLICT PHENOMENA: tentative classification framework

Peaceful protestdemonstrations, flash mobs

Protestdemonstrations, with violence

RiotsInsurgence, terrorism

Civil War, International War

EMERGENCE,CAS behavior

TRANSITIONSInte

nsity

Psychology, Sociology, History

Security Studies, Police Studies Military Sciences (Military History, Military Strategy, “Operational Art”)

Hierarchical Thinking & Approaches



PortugalGreece

BrazilEgypt Afghanistan

Syria

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Protest Protest Protest Protest

COUNTRY SOCIALCONTEXT

Political, Economic, Social:#protests, violence

… … … …

WORLD media, SN …

COMPLEX PATH DEPENDENT



INTE

NSITY

Time

ABM OF SOCIAL CONFLICT, CIVIL VIOLENCE AND REVOLUTION:Framework – simplified ODD (Grimm et al., 2010)

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DESCRIPTION

Purpose Scope of the model (type of phenomena to be simulated)

Entities Agent types (attributes, rules, environment)

Basic time cycle Time cycle, sequence, synch./asynch. activation

Model results Scales, phenomena explained

Observation Use of empirical parametrization/validation

Model strengths & limitations

Explanatory power, gaps between model results and reality



REVIEW:

Seven models

• Civil violence

• Worker protest

• Riots

• Urban crime

• Revolution

• Guerrilla warfare

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EPSTEIN (2002): Modeling civil violence: An Agent-Based computational approach Purpose: simulation of rebellion against a central authority or violence between 2 groups

populationquietrebelliousjailed

move at random

policemove at random

arrest rebellious agents within “vision radius”

“perceived grievance” G =H×(1-L)

“net risk” N=R×P×J α

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Source: Epstein (2002)

safe havens in peacekeeping outbursts of violence gradual reduction of police

)/exp(1 vACkP



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(VERY) PRELIMINARY RESULTS: all quiet before a burst of rebellion…



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(VERY) PRELIMINARY RESULTS: … and now a large rebellious uprise !



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collective behavior

memory/memorylessreactive/deliberative

eventsenvironmental

features

TNG “grievance” “net risk

perception”“threshold”

)tenvironmenns,interactioagent,statenternal(:maximize , iUU ta

RATIONAL BEHAVIOR MODEL

RULE-BASED BEHAVIOR MODELCHANGE STATE, SELECT/PERFORM ACTION

FINDINGS: agent behavior frameworks in S-O-A models



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FINDINGS: strengths & explanatory power of ABM

Intermittent bursts of rebellion/violence (punctuated equilibrium)[Epstein’s model and derived ABM]

Deceptive behavior in protester/police interaction[Idem]

Instability of authoritarian regimes if access to ICT is sufficiently widespread(cascade of reference revelation leading to revolution)

[Makowsky & Rubin model]

Multi-step concept + empirical validation soundness + robustness + realism[Davies et al. model; Fonoberova et al. model]



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FINDINGS: gaps between ABM and reality

Need to relate grievance G, hardship H, etc. to Relative Deprivation (RD)

[Social psychology, empirical data]

Assembling stage not treated as a contagion process with multiple contexts

[Network theory, empirical data]

Effect of formal/informal media coverage not considered

[New types of agentes (e.g. media, agitators)]

Modeling of police tactics (mostly …) missing

[Refining police agent models]

Path dependence due to successive events not considered

[Multiple 2-step cycles]



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FUTURE TRENDS & ONGOING WORK

Aim for a framework with two-step cycles(CONTAGION PROTEST) (CONTAGION PROTEST) …

Assembling/contagion model with multiple contextsComplex contagion + layered NW

Protest modelStart with Epstein’s model, refine agent types/attibutes/behavior, addnew types of agents

Parametrization/validationCollect & process data in real events (images, videos, questionnaires)Obtain data on news sites, activist group sites, etc.



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nodes may not be connected in individual context

(source: Hamill, 2006)

FUTURE TRENDS & ONGOING WORK: the layered network concept

concept (source: Hamill, 2006)

criteria for tie strength (source: Hamill, 2006)

. . .

… but are linked in multiple influence contexts (source: Hamill, 2006)



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(VERY) PRELIMINARY RESULTS: analysis of Facebook network of “Que se Lixe a Troika – Queremos as nossas vidas” political activist group

friendship network: giant component, community structures, filtering by node degree

group interactions network: hubs of activity



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(VERY) PRELIMINARY RESULTS: grievance factors, from questionnaires



Low sa

laries

Lack o

f acq

uisition power

Unemploym

ent

Lack o

f pers

pective

s of b

etter

future

Unfair w

ealth

distrib

ution

Discrim

ination of a

ccess

to basic s

ocial se

rvices

Corruption

Bad go

vern

ance

Loss

of indep

enden

ce/su

bordinati

on to fo

reign

inter

ests

Lack o

f legiti

macy of g

overn

ment to

impose

auste

rity

0

20

40

60

80

100

120

140

160

4 1 8 0 0 0 0 0 2 0

2414 7

5 2 4 2 112 9

2638

1711 12 12 11 7

16 22

31 45

3044 44

5637

31

49 36

6553

88 90 9379

98 110

71 83

Low(small effect)

Moderate(significant effect)

High(strong effect)

Exceptional(very strong effect)

Critical(afects survival)

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QUESTIONS ?


FUTURE TRENDS & ONGOING WORK: contagion models

*

1,, i

t

Tttitit ddD

Dodds and Watts (2005) SIR network contagion model(Complex contagion, memory effects)

* Watts and Dodds (2007) 2-step model of influence(Complex contagion, memoryless, rule-based)

ii

ii

b

bB

if0

if1adoptPr

Individual decision: keep A or adopt B

TNwNwNwG schoolschoolgangnongangnonganggangindex

* Berry et al. (2004) Group recruitment model

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Purpose: describe recruitment of urbanstreet gangs (surrogate of terrorist groups)

Agents: simple agents (2 attributes+SchoolAttendance Tendency – SAT, connected by social networks) + abstract agents (“School” and “Gang”)

Assumptions: simple agents (teenagers)decide to attend school or joint gangdepending on

where Gindex is cumulative (inflence of pastassociation with gang) and T is a threshold

Source: Berry et al. (2004)

Source: Berry et al. (2004)

TNwNwNwG schoolschoolgangnongangnonganggangindex

Berry et al. (2004): Computational Social Dynamic Modeling of Group Recruitment, Sandia National Laboratory Report SAND2003-8754

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Makowsky & Rubin (2011): An Agent-Based Model of Centralized Institutions, Social Network Technology, and Revolutions, Working Paper 2011-05, Towson UniversityPurpose: study large scale social change in authoritarian regimes and influence of ICT (e.g. “Arab Spring Revolution”)Agents: citizens, central authority (government), non-central authority (e.g. police)Assumptions: citizens hide/show preference against authority by maximizing an utility function:

central authority may change preference (institutional change) and non-central authority may support central authority or citizens, by maximizing their utility functions:

2,4

2

,3

2

,,22

,1,Cttj

Nttjtcitizenstjjtjtj aawaawaawbawU

212

2

1, tCt

CCCt

CCtj aawbawU

22

12

2

1,Ct

Ntt

Nt

NNNt

NNtj aaaawbawU

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REPRESENTATIVE RESULTS (source: Makowsky & Rubin, 2011):

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Assessment of Makowsly & Rubin (2011) model:

ADVANTAGES:•Explains revolution as a contagion process of “cascade preference revelation”

•Can represent sublevation of non-central authority

•Can represent institutional revolution (social context) changes due to revolution (“closes loop”)

LIMITATIONS:•Agents (citizens, n.c. authority) cannot move

•Agent actions in protests not represented (essentially a contagion model)

•Unrealistic modeling of SN/ICT (oversimplification of SN topology)

•Modeling of agents’ behavior not as effective as Epstein’s

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A. Ilachinsky (2004): EINStein combat model (in “Artificial War. Multi-Agent-Based Simulation of Combat”, World Scientific)Purpose: AB model of land combat Agents: Agent hierarchy (fireman, squad commander, force commander, supreme commander), multiple squads, realistic terrain features, and personality and goal-driven combat/movement actionsFormulation: agents select action (move/combat) by minimizing a penalty function:

cA

yxZyxyxAcAcwyxZ

A

AcAA

ˆ

),(min(:ˆ,ˆ,;),(

Source: Ilachinsky (2004)

“personality” vector 654321 ,,,,,

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Assessment of Ilachinsky (2004) model:

ADVANTAGES:•Useful framework for modeling police forces (actions, movement and hierarchical structure)•More realistic agent behavior•Rich collective/emergent behavior patterns•Realistic scenarios (not considered in simpler models)•Can still deal with a significant number of agents

LIMITATIONS:•Substantially more complicated than e.g. Epstein’s model and related variants

•More demanding in terms of computer resources

•Maximization/minimization less efficient than threshold comparison

•Requires substantial reworking for agents other than police forces (?)

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Assessment of Ilachinsky (2004) model:

ADVANTAGES:•Useful framework for modeling police forces (actions, movement and hierarchical structure)•More realistic agent behavior•Rich collective/emergent behavior patterns•Realistic scenarios (not considered in simpler models)•Can still deal with a significant number of agents

LIMITATIONS:•Substantially more complicated than e.g. Epstein’s model and related variants

•More demanding in terms of computer resources

•Maximization/minimization less efficient than threshold comparison

•Requires substantial reworking for agents other than police forces (?)

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F. Durupɩnar(2010): From Audiences to Mobs: Crowd Simulation with Psychological Factors, PhD Thesis (continuation)

5 factor model of personality:Openness, Consciousness,Extroversion,Aggreableness,NeuroticismEmotion model:Ortony, Clore and Collins (OCC) 22 emotion-model

*temperament; average emotional state; less permanent than personality but more persistent than emotions

Source: Durupɩnar (2004)

Documents

Agent-based modeling of social conflict, civil violence and revolution: state-of-the-art review and further prospects Carlos Lemos 1,2,3, Helder Coelho