Agent-Based Modeling of Conflict - ETH Z · § Agent-based models • Cognition based on behavioral...

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Prof. Dr. Karsten DonnayUniversity of Konstanz

[email protected]

Agent-Based Modeling of Conflict

125.11.2019Karsten Donnay

mailto:[email protected]

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Outline

§ Introduc-on

§ ABMs in Conflict Modeling

§ Examples

§ Taking a closer look: Jerusalem


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Why do we fight?

§ Conflict as the result of irrationality• Human nature• Evolutionary perspective• Psychology

§ Conflict as the outcome of calculated, rational decisions• Bargaining failures• Arms races• Power shifts• Violence as a last resort of political struggle

25.11.2019 3Karsten Donnay

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§ Classical: two opposing views• Hobbes: Endemic `warre’

life is `poore, nasty, brutish, and short’(Leviathan, 1651, 13)

• Rousseau: Harmonious livingAgriculture, demographic growth and privateproperty are what brought war

§ Human nature• Competition for food and water• Sex and reproduction, competition over partners• Rank, status and honor• Revenge and (blood) feuds


Conflict as the result of irrationality

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§ Evolutionary Perspective

• (Darwinian) struggle for survival against environment and each other• “Selfish gene”: evolutionary reward to protecting own kind• No perpetual conflict because others are strong, i.e., risks and costs

of (permanent) conflict are evolutionarily counterproductive

§ Psychology

• Frustration-Aggression: aggressiveness is produced by frustration andfrustrations high when discrepancy between expectations and reality

• Redirected aggression: towards minorities, foreign nationals, anyonethat is different than the in-group

• Authoritarian personality: poor self-image, identification with the aggressor


Conflict as the result of irraKonality

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§ Bargaining failures• Conflict is costly, should therefore always be able to strike a deal,

especially because it is inefficient, i.e., losing party always regrets

• Possible causes for bargaining failure

§ Indivisibility (e.g. holy sites in Jerusalem)§ Private information and misrepresentation§ Shifts in power through economic/social change§ Systemic uncertainties

§ Violence as a last resort of political struggle• Organized political struggles may not always achieve their goals,

especially against much stronger opponents (asymmetric conflict)

• Terrorism as a means of continuing the fight with other meansin order to achieve political goals


Conflict as the outcome of calculated, rational decisions

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§ Game Theory• Actors are strategic: forward and backward-looking

• Actors draw the best inferences given available knowledgeand constraints

• Maximize their payoffs given a set of preferences

§ Advantages: • Clarity, tractability

• Formal proofs

§ Disadvantages• Cumbersome to solve

• Strong and oFen simplisGc assumpGons aboutcogniGon and interacGons


Modeling rational actors

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§ Agent-based models• Cognition based on behavioral heuristics not necessarily

strategic considerations

• Large number of agents and complex systemic setupsthat can be very realistic depictions of conflicts

§ Advantages• Ease of use

• Ability to model large number of agents and includemany parameters

§ Disadvantages• Lack of transparency and potential for parameter overload

• Difficult to infer the core mechanism


Modeling rational actors

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Outline

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§ ABMs in Conflict Modeling

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ABMs in Conflict Modeling

§ Why we need quantitative models of conflict:• Need better quantitative understanding of past and contemporary conflicts

(e.g. Afghanistan, Iraq) for informed policy decisions

• Patterns in empirical data ≠ understanding!

• Challenge: bridge gap between observed empirical conflict patterns andestablished theories of conflict

• In particular: connect theories of (individual or group) interaction withobserved macro patterns


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§ Empirical patterns - Afghanistan

Bohorquez et al. Nature 462 (2009) Johnson et al. Science 333 (2011)

Casualty sizes Inter-event times


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§ What we require of quan8ta8ve conflict models:

• Model parameters: observable or related to observable variablesand quan8fiable

• Models: applicable to a par8cular case but general enough to betransferable to a broader context

• Models should account for specifics of the country(popula8on, size, ethnicity etc.)

• BUT over-fiJng must be avoided!


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§ Challenges of interdisciplinary approaches:• Natural science experience in modeling complex systems provides

unique insights for conflict research

• No impact on sociological or political science research if a modelthat “fits” empirical observations is NOT consistent with establishedtheories of conflict dynamics

• Quality of a model as a function of fit to empirical observations andplausibility of its mechanisms


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§ Why use ABMs in conflict research• Political science: mainly regression analysis in quantitative studies

• Recently: agent-based models (ABMs) for detailed studies of specificconflict scenarios

• ABMs: simplified representation of empirical scenario

• Very useful where formal models fail

• Specifying and validating ABMs: use detailed empirical data to increaserealism and validity


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§ Specifications of conflict ABMs

• Computational agents = relevant actor groups

• Microscopic interaction dynamics based on established theories

• Dynamics: who interacts with whom and when

• Simulation outputs: results of agent interactions(attack, migration etc.)


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§ Empirical Data and Validation of ABMs• Empirical population data: realistic agent population and topography of

model landscape

• Simulation outputs: quantitative comparison to empirically observeddistributions of conflict indicators

• (Microscopic) Interaction mechanisms: qualitatively, ideally quantitatively,consistent with the empirical observations

• Test predictive power:§ In-sample or out-of-sample predictions§ Comparison to “null models”


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Outline

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§ Applications and Examples

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Applications and Examples

§ Epstein’s model of Civil War

• Epstein, PNAS 99 (2002)

• Agent-based model of civil violence:

§ Central authority vs. decentralized rebellion

§ Central authority suppressing violence between two ethnic groups

• Main mechanisms:

§ Grievance leads to violence

§ Private grievances ≠ public actions

§ Police intercepts attacks with certain probability


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Applica'ons and Examples


Spatial outbursts

Epstein PNAS 99 (2002):7243-7250

Temporal outbursts


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http://ccl.northwestern.edu/netlogo/models/Rebellion2025.11.2019Karsten Donnay

http://ccl.northwestern.edu/netlogo/models/Rebellion

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§ Epstein’s model of Civil War§ Reproduces some stylized facts of revolutions/communal violence

§ BUT all model parameters unobservable

§ Interaction mechanisms somewhat arbitrary

§ Some results due to particular implementation (for details see http://ccl.northwestern.edu/netlogo/models/Rebellion )

§ No formal validation of results or mechanisms


http://ccl.northwestern.edu/netlogo/models/Rebellion

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§ Severity Size Distributions – Bohorquez et al. (2009)• Bohorquez et al. Nature 462 (2009)

• Agent-based model of insurgent violence:§ Security forces vs. insurgent groups§ Casualties in attacks scale with a group’s (military) strength

• Main mechanisms:§ Interaction probability scales with group strength§ Coalescence and fragmentation of groups as a consequence of

interaction§ Relative strengths of groups decide casualties in attacks


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§ Severity Size Distributions – Bohorquez et al. (2009)

Bohorquez et al. Nature 462 (2009)

Size of events


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§ Severity Size Distributions – Bohorquez et al. (2009)

• Excellent fit for a wide range of cases (4 fit parameters)

• Model parameters have empirical interpretation

• Problem of the modeling approach:assumed insurgent/military group dynamics NOT empirically plausible

§ Not realistic that military units fragment after insurgent attack§ Unsubstantiated that group fragmentation and coalescence is main driver of

insurgent/military group strength§ Empirically insurgent groups very often attack when own strength much smaller than

that of target: model predicts quick eradication of insurgency contrary to empirical situation


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Outline

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§

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§ Taking a closer look: Jerusalem


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Taking a closer look: Jerusalem

§ Violence in Jerusalem• Highly contested urban space

• High symbolic importance for theArab world

• Limited understanding of conflict mechanisms

• Only few quantitative studies

• Inform policy decisions?


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§ Agent-based model• Interactions of main population groups

§ Secular Jews§ Ultra-Orthodox Jews§ Palestinians§ Security Forces

• Realistic topology of Jerusalem seeded withempirical population data

• Model mechanisms based on Jerusalem specific literature

• Detailed violence and census data for 2001-09period available


Uninhabited/Industrial AreaPredominantly Secular/Moderate-Orthodox JewsPredominantly Ultra-Orthodox JewsPredominantly PalestiniansMunicipal Boundary; post-June 1967

Bhavnani, Donnay, Miodownik, Mor, Helbing. (2014). “Group Segregation and Urban Violence.” American Journal of Political Science 58(1): 226-245

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§ Key Ideas• empirical population data and geography: increase external validity

• violence data: optimize model to best match empirical situation

• validation:§ test predictive power in-sample§ compare predictions to simple statistical model§ consistency of model mechanisms with empirical reality


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§ Why this approach?• empirical grounding & formal validation:

§ external validity§ plausible model mechanisms

• building on literature: relevance for political science research

• goals:§ move beyond agent-based “toy models”

§ use framework for counterfactual studies (with policy implications)


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§ Evidence-driven computational modeling (EDM)


Bhavnani, Donnay, Miodownik, Mor, Helbing. (2014). “Group SegregaLon and Urban Violence.” American Journal of Poli0cal Science 58(1): 226-245

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§ Counterfactual analysis


Bhavnani, Donnay, Miodownik, Mor, Helbing. (2014). “Group Segregation and Urban Violence.” American Journal of Political Science 58(1): 226-245

Levels of Violenceno violencevery lowlowintermediatehighMunicipal Boundary; post-June 1967

Levels of Violenceno violencevery lowlowintermediatehigh

International Border; pre-June 1967Municipal Boundary; post-June 1967

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§ Implementation details:

• robust implementation as Java simulation platform

§ unit-testing for algorithmic consistency

§ counter measures against computingartifacts

§ validation against test scenarios

• full enumeration using supercomputing cluster

§ Euler cluster of ETH Zurich with >3000compute nodes

§ fine-grained enumeration of full model parameter space

§ sensitivity analysis for other key model parameters


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Software tools


§ Open source and free:

• MESA for Python:https://github.com/projectmesa/mesa/

• Repast for Java:https://repast.github.io/

• NetLogo (not recommended for full-on modeling):https://ccl.northwestern.edu/netlogo/

https://github.com/projectmesa/mesa/

https://repast.github.io/

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Literature suggestions

• Bhavnani, Ravi, Karsten Donnay, Dan Miodownik, Maayan Mor, and Dirk Helbing. (2014). Group Segregation and Urban Violence. American Journal of Political Science 58(1): 226–245.

• de Marchi, Scott and Scott E. Page. (2014). Agent-Based Models. Annual Review of Political Science 17(1): 1–20.

• Epstein, Joshua M. (1999). Agent-Based Computational Models and Generative Social Science.Complexity 4(5): 41–60.

• Epstein, Joshua M. (2008). Why Model? Journal of Artificial Societies and Social Simulation 11(4): 12.

• Miller, John H. and Scott E. Page. (2004). The Standing Ovation Problem. Complexity 9(5): 8–16.

• Schelling, Thomas C. (1971). Dynamic Models of Segregation. Journal of Mathematical Sociology 1: 143–186.

• Weidmann, Nils and Idean Salehyan. (2013). Violence and Ethnic Segregation: A Computational Model Applied to Baghdad. International Studies Quarterly 57(1): 52–64.

• Axelrod, Robert. (1986). An Evolutionary Approach to Norms. American Political Science Review 80(4): 1095–1111.

• Axelrod, Robert. (1997). The Dissemination of Culture: A Model with Local Convergence and Global Polarization. Journal of Conflict Resolution 41(2): 203–226.

• Axtell, Robert L., Joshua M. Epstein, Jeffrey S. Dean, et al. (2002). Population Growth and Collapse in a Multiagent Model of the Kayenta Anasazi in Long House Valley. Proceedings of the National Academy of Sciences 99: 7275–7279.


http://onlinelibrary.wiley.com/doi/10.1111/ajps.12045/abstract

http://doi.org/10.1146/annurev-polisci-080812-191558

http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1099-0526(199905/06)4:5%253C41::AID-CPLX9%253E3.0.CO;2-F/abstract

http://jasss.soc.surrey.ac.uk/11/4/12.html

http://onlinelibrary.wiley.com/doi/10.1002/cplx.20033/abstract

http://www.tandfonline.com/doi/abs/10.1080/0022250X.1971.9989794

http://onlinelibrary.wiley.com/doi/10.1111/isqu.12059/abstract

https://www.cambridge.org/core/journals/american-political-science-review/article/an-evolutionary-approach-to-norms/2B829FB347BBDD0F1A8C0F325EFB6F7B

http://journals.sagepub.com/doi/abs/10.1177/0022002797041002001

http://www.pnas.org/content/99/suppl_3/7275.full

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Literature suggestions (continued)

• Bhavnani, Ravi and Dan Miodownik. (2008). Ethnic Polarization, Ethnic Salience, and Civil War. Journal of Conflict Resolution 53(1): 30–49.

• Bhavnani, Ravi, Michael G. Findley and James H. Kuklinski. (2009). Rumor Dynamics in Ethnic Violence.The Journal of Politics 71(3): 1–20.

• Cederman, Lars-Erik. (2003). Modeling the Size of Wars: From Billiard Balls to Sandpiles. American Journal of Political Science 97(1): 135–150.

• Centola, Damon, Robb Willer and Michael Macy. (2005). The Emperor's Dilemma: A Computational Model of Self-Enforcing Norms. American Journal of Sociology 110(4): 1009–1040.

• Epstein, Joshua M. (2002). Modeling Civil Violence: An Agent-Based Computational Approach.Proceedings of the National Academy of Sciences 99: 7243–7250.

• Helbing, Dirk. (2010). Pluralistic Modeling of Complex Systems. Science and Culture 76(9-10): 315– 329.

• Macy, Michael and Robert Willer. (2002). From Factors to Actors: Computational Sociology and Agent-Based Modelling. Annual Review of Sociology 28: 143–166.

• Schutte, Sebastian. (2010). Optimization and Falsification in Empirical Agent-Based Models. Journal of Artificial Societies and Social Simulation 13(1): 2.


http://doi.org/10.1177/0022002708325945

http://doi.org/10.1017/S002238160909077X

https://www.cambridge.org/core/journals/american-political-science-review/article/modeling-the-size-of-wars-from-billiard-balls-to-sandpiles/84FFC9F035902B95419977608C5A628A

http://www.journals.uchicago.edu/doi/full/10.1086/427321

http://doi.org/10.1073/pnas.092080199

https://arxiv.org/abs/1007.2818

http://doi.org/10.1146/annurev.soc.28.110601.141117

http://jasss.soc.surrey.ac.uk/13/1/2.html

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§ If you have any questions, please get in touch!

Prof. Dr. Karsten DonnayAssistant Professor for Computational Social ScienceDepartment of Politics andPublic AdministrationUniversity of Konstanz

[email protected]/cdm


mailto:[email protected]

http://www.polver.uni-konstanz.de/cdm

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