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EUMAS2013 – Toulouse 12/14 December 2013

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1 EUMAS2013 – Toulouse 12/14 December 2013
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, Portugal 3 Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal 4 Instituto de Telecomunicações IT-IUL, Lisbon, Portugal

2 EUMAS2013 – Toulouse 12/14 December 2013
CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK)

3 EUMAS2013 – Toulouse 12/14 December 2013
CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK) 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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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK) SOCIAL CONFLICT PHENOMENA: tentative classification framework Peaceful protest demonstrations, flash mobs Protest demonstrations, with violence Riots Insurgence, terrorism Civil War, International War EMERGENCE, CAS behavior TRANSITIONS Intensity Psychology, Sociology, History Security Studies, Police Studies Military Sciences (Military History, Military Strategy, “Operational Art”) Hierarchical Thinking & Approaches Syria Afghanistan Egypt Brazil Greece Portugal

5 EUMAS2013 – Toulouse 12/14 December 2013
CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK) WORLD media, SN … COUNTRY SOCIAL CONTEXT Political, Economic, Social: #protests, violence … … … … INTENSITY Time Protest Protest Protest Protest COMPLEX PATH DEPENDENT

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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK) ABM OF SOCIAL CONFLICT, CIVIL VIOLENCE AND REVOLUTION: Framework – simplified ODD (Grimm et al., 2010) 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

7 EUMAS2013 – Toulouse 12/14 December 2013
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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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 population quiet rebellious jailed move at random “perceived grievance” G =H×(1-L) “net risk” N=R×P×J α police move at random arrest rebellious agents within “vision radius” safe havens in peacekeeping outbursts of violence gradual reduction of police Source: Epstein (2002)

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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK) (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) (VERY) PRELIMINARY RESULTS: … and now a large rebellious uprise !

11 FINDINGS: agent behavior frameworks in S-O-A models
EUMAS2013 – Toulouse 12/14 December 2013 CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK) FINDINGS: agent behavior frameworks in S-O-A models collective behavior memory/memoryless reactive/deliberative events environmental features “grievance” “net risk perception” “threshold” RATIONAL BEHAVIOR MODEL RULE-BASED BEHAVIOR MODEL CHANGE STATE, SELECT/PERFORM ACTION

12 EUMAS2013 – Toulouse 12/14 December 2013
CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK) 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) 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) FUTURE TRENDS & ONGOING WORK Aim for a framework with two-step cycles (CONTAGION  PROTEST)  (CONTAGION  PROTEST)  … Assembling/contagion model with multiple contexts Complex contagion + layered NW Protest model Start with Epstein’s model, refine agent types/attibutes/behavior, add new types of agents Parametrization/validation Collect & process data in real events (images, videos, questionnaires) Obtain data on news sites, activist group sites, etc.

15 EUMAS2013 – Toulouse 12/14 December 2013
CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK) FUTURE TRENDS & ONGOING WORK: the layered network concept nodes may not be connected in individual context (source: Hamill, 2006) concept (source: Hamill, 2006) . . . As redes em camada permitem descrever interacções e propagação de influências em contextos múltiplos (família, redes sociais, sindicato, etc), com base na teoria das redes simples. A cada contexto corresponde uma rede distinta. A ponderação das influências e a(s) acção(ões) resultante(s) é feita nos agentes, que funcionam como uma espécie de “pilha de nós” A intensidade e até a natureza das ligações pode ser diferente de contexto para contexto. Nós que não estão ligados em nenhum contexto individual (rede simples), podem estar ligados quando se consideram contextos múltiplos. (exemplo: nós 1 e 13 na figura) Neste trabalho, as redes em camada são importantes para avaliar o efeito dos diversos contextos no ajuntamento e caracterizar a diferença entre manifestações orgânicas e inorgânicas. … but are linked in multiple influence contexts (source: Hamill, 2006) criteria for tie strength (source: Hamill, 2006)

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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK) (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) (VERY) PRELIMINARY RESULTS: grievance factors, from questionnaires

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QUESTIONS ?

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CONTEXT & MOTIVATION CONFLICT & PROTEST DYNAMICS SOA REVIEW DISCUSSION FUTURE PROSPECTS (ONGOING WORK) FUTURE TRENDS & ONGOING WORK: contagion models 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) Individual decision: keep A or adopt B * Berry et al. (2004) Group recruitment model

20 EUMAS2013 – Toulouse 12/14 December 2013
Berry et al. (2004): Computational Social Dynamic Modeling of Group Recruitment, Sandia National Laboratory Report SAND Purpose: describe recruitment of urban street gangs (surrogate of terrorist groups) Agents: simple agents (2 attributes+School Attendance Tendency – SAT, connected by social networks) + abstract agents (“School” and “Gang”) Assumptions: simple agents (teenagers) decide to attend school or joint gang depending on where Gindex is cumulative (inflence of past association with gang) and T is a threshold Source: Berry et al. (2004)

21 EUMAS2013 – Toulouse 12/14 December 2013
Makowsky & Rubin (2011): An Agent-Based Model of Centralized Institutions, Social Network Technology, and Revolutions, Working Paper , Towson University Purpose: 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:

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

23 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

24 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 actions Formulation: agents select action (move/combat) by minimizing a penalty function: Source: Ilachinsky (2004) “personality” vector

25 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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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, Neuroticism Emotion 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)


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