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Forecasting the Use of Violence by Ethno–Political Organizations: Middle Eastern Minorities and the Choice of Violence

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Handbook of Computational Approaches to Counterterrorism

Abstract

Can analytic models, informed by social scientific theories using computational engineering approaches, offer effective forecasting of violent behavior? This chapter discusses a new data set which codes the structure and behavior of ethno-political organizations and the use of a new approach for forecasting political behavior drawn from computer engineering. In the chapter, we build a forecasting model and then test the model against existing data as well as a predictive analysis for the year 2009 (the analysis was done in 2008 and data for 2005–2009 has not yet been collected for this data set). The data used was drawn from the Minorities at Risk Organizational Behavior (MAROB) data set. MAROB was created through collaboration between the National Consortium for the Study of Terrorism and Responses to Terrorism and the Minorities at Risk (MAR) Project. This data focuses on ethno-political organizations in the Middle East to test factors that make it more or less likely that an organization will choose to use violence. While the variables on which data was collected were informed by theories of contentious politics, this chapter focuses primarily on the data itself and the forecasting approach that we used and less on the social science theoretical models as such. Analytically we use multiple approaches for data massaging, classification and forecasting to achieve high classification accuracies (measured in terms of overall accuracy, recall, precision, false positives, and F-measure). We also strive for parsimony in the number of variables we use to make our forecasting predictions.

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Notes

  1. 1.

    Whether experts are listened to is an important but different issue that we do not address in this chapter (see [2, 20]).

  2. 2.

    The data can be downloaded at http://www.cidcm.umd.edu/mar/data.asp (see [52]).

  3. 3.

    See [914, 18, 2527, 34, 42, 44, 45, 49, 55, 65, 67, 72].

References

  1. Alpaydin E (2004) Introduction to machine learning. MIT, Cambridge

    Google Scholar 

  2. Andriole SJ, Hopple GW (1984) The rise and fall of event data: from basic research to applied use in the US Department of Defense. Int Interact 10(3):293–309

    Google Scholar 

  3. Bankes S, Lempert R, Popper S (2002) Making computational social science effective: epistemology, methodology, and technology. Soc Sci Comput Rev 20(4):377–388

    Google Scholar 

  4. Beck N, King G, Zeng L (2004) Theory and evidence in international conflict: a response to de Marchi, Gelpi, and Grynaviski. Am Pol Sci Rev 98(2):379–389

    Google Scholar 

  5. Bennett SD, Stam AC (2006) Predicting the length of the 2003 US-Iraq war. Foreign Policy Anal 2(2):101–116

    Google Scholar 

  6. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York

    Google Scholar 

  7. Bond J, Petrov V, Bond D, O’Brien S (2004) Forecasting turmoil in Indonesia: an application of hidden Markov models. A paper prepared for presentation at the International Studies Association Convention, Montreal, Quebec

    Google Scholar 

  8. Bro R (1996) Multiway calibration multilinear PLS. J Chemom 10(1):47–61

    Google Scholar 

  9. Bueno de Mesquita E (2005a) Conciliation, counterterrorism, and patterns of terrorist violence. Int Org 59(1):145–176

    Google Scholar 

  10. Bueno de Mesquita E (2005b) The quality of terror. Am J Pol Sci 49(3):515–530

    Google Scholar 

  11. Bueno de Mesquita E (2005c) The terrorist endgame: a model with moral hazard and learning. J Confl Resolut 49(2):237–258

    Google Scholar 

  12. Callaway R, Harrelson-Stephens J (2006) Toward a theory of terrorism: human security as a determinant of terrorism. Stud Confl Terror 29(7):679–702

    Google Scholar 

  13. Caprioli M (2000) Gendered conflict. J Peace Res 37(1):51–68

    Google Scholar 

  14. Caprioli M (2005) Primed for violence: the role of gender inequality in predicting internal conflict. Int Stud Q 49(2):161–178

    Google Scholar 

  15. Chiang LH, Braatz RD (2001) Fault detection and diagnosis in industrial systems. Springer, London

    Google Scholar 

  16. Choi K (2011) Intelligent data-driven classification and forecasting processes for complex engineering and social systems. PhD Dissertation, University of Connecticut

    Google Scholar 

  17. Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York

    Google Scholar 

  18. Fearon JD, Laitin DD (2003) Ethnicity, insurgency, and civil war. Am Pol Sci Rev 97(1):75–90

    Google Scholar 

  19. Ge M, Du R, Zhang G, Xu Y (2004) Fault diagnosis using support vector machine with an application in sheet metal stamping operations. Mech Syst Signal Process 18(1):143–159

    Google Scholar 

  20. Gilpin R (2005) War is too important to be left to ideological amateurs. Int Relat 19(1):5–18

    Google Scholar 

  21. Gleditsch KS (2007) Transnational dimensions of civil war. J Peace Res, 44(3):293–309

    Google Scholar 

  22. Goldsmith BE, Chalup SK, Quinlan MJ (2008) Regime type and international conflict: towards a general model. J Peace Res 45(6):743–763

    Google Scholar 

  23. Goldstone JA, Bates RH, Gurr TR, Lustik M, Marshall MG, Ulfelder J, Woodward M (2010) A global forecasting model for political instability. Am J Pol Sci 54(1):190–208

    Google Scholar 

  24. Green KC, Armstrong JS (2007) The ombudsman: value of expertise for forecasting decisions in conflicts. Interfaces 37(3):287–299

    Google Scholar 

  25. Gressang DS IV (2001) Audience and message: assessing terrorist WMD potential. Terror Pol Violence 13(3):83–106

    Google Scholar 

  26. Gurr TR (1988) Empirical research on political terrorism: the state of the art and how it might be improved. In Slater R, Stohl M (eds) Current perspectives on international terrorism. Palgrave Macmillan, London

    Google Scholar 

  27. Gurr TR (2000) People vs. states. United States Institute of Peace, Washington, DC

    Google Scholar 

  28. Gurr TR (2004) The minorities at risk (MAR) project, 8 July 2004. Available via DIALOG, http://www.cidcm.umd.edu/mar/. Cited 28 Jan 2012

  29. Hollis M, Smith S (1990) Explaining and understanding international relations. Oxford University Press, New York

    Google Scholar 

  30. Hopkins DJ, King G (2010) A method of automated nonparametric content analysis for social science. Am J Pol Sci 54(1):229–247

    Google Scholar 

  31. Hsu C-W, Lin C-J (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425

    Google Scholar 

  32. Jackson JE (1991) A user’s guide to principal components. Wiley, New York

    Google Scholar 

  33. Jensen NM, Young DJ (2008) A violent future? Political risk insurance markets and violence forecasts. J Confl Resolut 52(4):527–547

    Google Scholar 

  34. Juergensmeyer M (2003) Terror in the mind of God: the global rise of religious violence, 3rd edn. University of California Press, Berkley

    Google Scholar 

  35. Karush W (1939) Minima of functions of several variables with inequalities as side constraints. Masters Thesis, University of Chicago

    Google Scholar 

  36. Khuller S, Martinez V, Nau D, Simari G, Sliva A, Subrahmanian VS (2007) Computing most probable worlds of action probabilistic logic programs: scalable estimation for 1030, 000 worlds. Ann Math Artif Intell 51(2–4):295–331

    Google Scholar 

  37. King G, Zeng L (2001) Improving forecasts of state failure. World Polit 53(4):623–658

    Google Scholar 

  38. King G, Zeng L (2007) When can history be our guide? The pitfalls of counterfactual inference. Int Stud Q 51(1):183–201

    Google Scholar 

  39. King G, Li Y (2008) Verbal autopsy methods with multiple causes of death. Stat Sci 23(1): 78–91

    Google Scholar 

  40. Kuhn HW, Tucker AW (1951) Nonlinear programming. In Neyman J (eds) Proc Second Berkeley Symp on Math Statist and Prob. University of California Press, Berkeley, pp 481-492

    Google Scholar 

  41. Kullback S (1987) The Kullback-Leibler distance. Am Stat 41(4):340–341

    Google Scholar 

  42. Laitin DD (2002) Comparative politics: the state of the subdiscipline. In Katznelson I, Milner HV (eds) Political science: state of the discipline. Norton, New York

    Google Scholar 

  43. Lichbach MI (2003) Is rational choice theory all of social science? The University of Michigan Press, Ann Arbor

    Google Scholar 

  44. Lichbach MI, Zuckerman AS (1997) Comparative politics: rationality, culture, and structure. Cambridge University Press, New York

    Google Scholar 

  45. Ljung L (1999) System identification: theory for the user. Prentice-Hall, New Jersey

    Google Scholar 

  46. Luo J, Pattipati KR, Qiao L, Chigusa S (2008) Model-based prognostic techniques applied to a suspension system. IEEE Trans Syst Man Cybern A 38(5):1156–1168

    Google Scholar 

  47. Martinez V, Simari G, Sliva A, Subrahmanian VS (2008) CONVEX: similarity-based algorithms for forecasting group behavior. IEEE Intell Syst 23(4):51–57

    Google Scholar 

  48. Mazarr MJ (2007) The Iraq war and agenda setting. Foreign Policy Anal 3(1):1–23

    Google Scholar 

  49. McAdam D, Tarrow S, Tilly C (2001) Dynamics of contention. Cambridge University Press, Cambridge

    Google Scholar 

  50. McCammon HJ, Granberg EM, Campbell KE, Mowery C (2001) How movements win: gendered opportunity structures and U.S. women’s suffrage movements, 1866 to 1919. Am Sociol Rev 66(1):49–70

    Google Scholar 

  51. Minorities at Risk Project (2008) Minorities at risk organizational behavior dataset. Center for International Development and Conflict Management, College Park. Available via DIALOG http://www.cidcm.umd.edu/mar/data.asp. Cited 14 Feb 2012

  52. O’Brien SP (2002) Anticipating the good, the bad, and the ugly: an early warning approach to conflict and instability analysis. J Confl Resolut 46(6):791–811

    Google Scholar 

  53. Page EB (1963) Ordered hypotheses for multiple treatments: a significance test for linear ranks. J Am Stat Assoc 58(301):216–230

    Google Scholar 

  54. Peterson WW, Birdsall TG, Fox WC (1954) The theory of signal detectability. Trans IRE Prof Group Inf Theory 4(4):171–212

    Google Scholar 

  55. Regan PM, Norton D (2005) Greed, grievance, and mobilization in civil wars. J Confl Resolut 49(3):319–336

    Google Scholar 

  56. Rost N, Schneider G, Kleibl J (2009) A global risk assessment model for civil wars. Soc Sci Res 38(4):921–933

    Google Scholar 

  57. Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT, Cambridge

    Google Scholar 

  58. Schrodt PA (2000) Pattern recognition of international crises using hidden Markov models. In Richards D (eds) Political complexity: nonlinear models of politics. University of Michigan Press, Ann Arbor, pp 296–328

    Google Scholar 

  59. Schrodt PA (2006) Forecasting conflict in the Balkans using hidden Markov models. In: Trappl R (ed) Programming for peace: computer-aided methods for international conflict resolution and prevention. Springer, Dordrecht, pp 161–184

    Google Scholar 

  60. Sliva A, Subrahmanian VS, Martinez V, Simari G (2009) CAPE: automatically predicting 693 changes in terror group behavior. In: Memon N et al (eds) Mathematical methods in 694 counterterrorism. Springer, New York, pp 253–269

    Google Scholar 

  61. Smola AJ, Bartlett PL, Schlkopf B, Schuurmans D (2000) Advances in large margin classifiers. MIT, Cambridge

    Google Scholar 

  62. Steel RGD, Torrie JH (1960) Principles and procedures of statistics. McGraw-Hill, New York

    Google Scholar 

  63. Subrahmanian VS, Albanese M, Martinez V, Nau D, Reforgiato D, Simari G, Sliva A Wilkenfeld J (2007) CARA: a cultural adversarial reasoning architecture. IEEE Intell Syst 22(2):12–16

    Google Scholar 

  64. Taleb NN (2008) The black swan: the impact of the highly improbable. Random House, Inc, New York

    Google Scholar 

  65. Tarrow S (1993) Power in movements. Cambridge University Press, Cambridge

    Google Scholar 

  66. Tetlock PE (1999) Theory-driven reasoning about plausible pasts and probable futures in world politics: are we prisoners of our preconceptions? Am J Pol Sci 43(2):335–366

    Google Scholar 

  67. Tickner JA (2001) Gendering world politics: issues and approaches in the post-cold war era. Columbia University Press, New York

    Google Scholar 

  68. Vapnik VN (2000) The nature of statistical learning theory. Springer, New York

    Google Scholar 

  69. Ward MD, Greenhill BD, Bakke KM (2010) The perils of policy by p-value: predicting civil 711 conflicts. J Peace Res 47(4):1–13

    Google Scholar 

  70. Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85

    Google Scholar 

  71. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1–3):37–52

    Google Scholar 

  72. Zimmermann E (1987) Political violence and other strategies of opposition movements: a look at some recent evidence. J Int Aff 40(2):325–351

    Google Scholar 

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Choi, K., Asal, V., Wilkenfeld, J., Pattipati, K.R. (2013). Forecasting the Use of Violence by Ethno–Political Organizations: Middle Eastern Minorities and the Choice of Violence. In: Subrahmanian, V. (eds) Handbook of Computational Approaches to Counterterrorism. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5311-6_10

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