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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
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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?
EUMAS2013 – Toulouse 12/14 December 2013
CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK)
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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
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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK)
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
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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK)
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
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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK)
REVIEW:
Seven models
• Civil violence
• Worker protest
• Riots
• Urban crime
• Revolution
• Guerrilla warfare
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EUMAS2013 – Toulouse 12/14 December 2013
CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK)
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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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK)
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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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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK)
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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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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK)
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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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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK)
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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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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK)
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(VERY) PRELIMINARY RESULTS: grievance factors, from questionnaires
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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK)
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 ?
EUMAS2013 – Toulouse 12/14 December 2013
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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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK)
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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
22
EUMAS2013 – Toulouse 12/14 December 2013
REPRESENTATIVE RESULTS (source: Makowsky & Rubin, 2011):
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EUMAS2013 – Toulouse 12/14 December 2013
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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EUMAS2013 – Toulouse 12/14 December 2013
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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EUMAS2013 – Toulouse 12/14 December 2013
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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EUMAS2013 – Toulouse 12/14 December 2013
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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EUMAS2013 – Toulouse 12/14 December 2013
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)