Governmental combat of migration between competing terrorist organisations

• Autorzy: Hausken Kjell

Identyfikatory

DOI

10.37190/ord200302

• Języki publikacji: EN

Abstrakty

EN

Each terrorist organisation is modelled with four coupled differential time equations for the evolution of ideologues, criminal mercenaries, captive participants, and capital sponsoring. Emigration of ideologues may cause unbounded growth of the organisation receiving ideologues. The organisation losing ideologues may reach a stationary state where ideologues are supported by capital sponsors and mercenaries. Emigration of mercenaries may cause the organisation losing mercenaries to experience growth. The organisation receiving mercenaries may lose capital sponsors permanently, allowing for the presence of mercenaries, or capital sponsors may rebound deterring mercenaries. Emigration of ideologues from one organisation to another requires more government intervention into the latter to ensure termination. Emigration of mercenaries from one organisation to another may require more government intervention into the latter, since mercenaries support ideologues. Competing terrorist organisations may facilitate their mutual extinction. Various intervention strategies are considered: the most threatening organisation is eliminated first, aided by competition from the least threatening, after which the remaining organisation is eliminated. The government’s instantaneous and accumulated utilities are analysed through time and compared, depending on emigration, competition, and government intervention strategies.

Słowa kluczowe

EN

terrorism; terrorist organization; ideologist; mercenary; captive participant; sponsor; evolution; dynamics; simulation; migration; government; intervention; differential equations

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Operations Research and Decisions
• Rocznik: 2020
• Tom: Vol. 30, No. 3
• Strony: 21--46
• Opis fizyczny: Bibliogr. 35 poz., rys.

Twórcy

autor

Hausken Kjell

• Faculty of Science and Technology, University of Stavanger, 4036 Stavanger, Norway

Bibliografia

• [1] ABBAS A.E., TAMBE M., VON WINTERFELDT D., Improving Homeland Security Decisions, Cambridge University Press, Cambridge 2017.
• [2] BERMAN O., GAVIOUS A., Location of Terror Response Facilities. A Game Between State and Terrorist, Eur. J. Oper. Res., 2007, 177 (2), 1113–1133.
• [3] BIER V.M., HAUSKEN K., Endogenizing the Sticks and Carrots. Modeling Possible Perverse Effects of Counterterrorism Measures, Ann. Oper. Res., 2011, 186 (1), 39–59.
• [4] BUNN M., A Mathematical Model of the Risk of Nuclear Terrorism, Ann. Am. Acad. Pol. Soc. Sci.,2006, 607 (1), 103–120.
• [5] CAULKINS J.P., GRASS D., FEICHTINGER G., TRAGLER G., Optimizing Counter-Terror Operations.Should one Fight Fire With “Fire” or “Water”?, Comp. Oper. Res., 2008, 35 (6), 1874–1885.
• [6] CHAMBERLAIN T., Systems Dynamics Model of Al-Qa’ida and United States, J. Home. Sec. Emerg. Manage., 2007, 4 (3), article No. 14, http://www.bepress.com/jhsem /vol4/iss3/14/
• [7] FEICHTINGER G., NOVAK A., Terror and Counterterror Operations. Differential Game with CyclicalNash Solution, J. Opt. Theor. Appl., 2008, 139 (3), 541–556.
• [8] FEINSTEIN J., KAPLAN E., Analysis of a Strategic Terror Organization, J. Conf. Res., 2010, 54 (2), 281–302.
• [9] GOLANY B., KAPLAN E.H., MARMUR A., ROTHBLUM U.G., Nature Plays with Dice. Terrorists Do Not: Allocating Resources to Counter Strategic Versus Probabilistic Risks, Eur. J. Oper. Res., 2009, 192 (1), 198–208.
• [10] GUAN P., HE M.,ZHUANG J., HORA S.C., Modeling a Multitarget Attacker–Defender Game with Budget Constraints, Dec. Anal., 2017, 14 (2), 87–107.
• [11] GUPTA D.K., Understanding Terrorism. The Life Cycle of Birth, Growth, Transformation, and Demise. Routledge, New York 2008.
• [12] HAUSKEN K., The Dynamics of Terrorist Organizations, Oper. Res. Persp., 2019, 6, https://doi.org/10.1016/j.orp.2019.100120
• [13] HAUSKEN K., Government Intervention to Combat the Dynamics of Terrorist Organizations, J. Oper.Res. Soc., 2020, https://doi.org/10.1080/01605682.2019.1656561
• [14] HAUSKEN K., Government Protection Against Terrorists Funded by Benefactors and Crime: an Economic Model, Int. J. Conf. Viol., 2017, 11 (5), 1–37.
• [15] HAUSKEN K., Governmental Combat of the Dynamics of Multiple Competing Terrorist Organizations, Math. Comp. Simul., 2019, 166, 33–55.
• [16] HAUSKEN K., Governments Playing Games and Combating the Dynamics of a Terrorist Organization, Int. Game Theory Rev., 2020, https://doi.org/10.1142/S0219198920500139
• [17] HAUSKEN K., Whether to Attack a Terrorist’s Resource Stock Today or Tomorrow, Games Econ. Beh., 2008, 64 (2), 548–564.
• [18] HAUSKEN K., BANURI S., GUPTA D.K., ABBINK K., Al Qaeda at the Bar. Coordinating Ideologues and Mercenaries in Terrorist Organizations, Publ. Choice, 2015, 164 (1), 57–73.
• [19] HAUSKEN K., BIER V.M., Defending against Multiple Different Attackers, Eur. J. Oper. Res., 2011, 211 (2), 370–384.
• [20] HAUSKEN K., GUPTA D.K., Determining the Ideological Orientation of Terrorist Organizations: the Effects of Government Repression and Organized Crime, Int. J. Publ. Pol., 2016, 12 (1–2), 71–97.
• [21] HAUSKEN K., GUPTA D.K., Government Protection Against Terrorism and Crime, Global Crime, 2015, 16 (2), 59–80.
• [22] HAUSKEN K., GUPTA D.K., Terrorism and Organized Crime: the Logic of an Unholy Alliance, Int. J. Cont. Soc., 2015, 52 (2), 141–166.
• [23] HAUSKEN K., MOXNES J.F., Stochastic Conditional and Unconditional Warfare, Eur. J. Oper. Res., 2002, 140 (1), 61–87.
• [24] HAUSKEN K., ZHUANG J., Governments’ and Terrorists’ Defense and Attack in a T-Period Game, Dec. Anal., 2011, 8 (1), 46–70.
• [25] HIRSHLEIFER J., Anarchy and Its Breakdown, J. Pol. Econ., 1995, 103 (1), 26–52.
• [26] INSUA D.R., CANO J., PELLOT M., ORTEGA R., Multithreat Multisite Protection. A Security Case Study, Eur. J. Oper. Res., 2016, 252 (3), 888–899.
• [27] KAMINSKIY M.P., AYYUB B.M., Terrorist Population Dynamics Model, Risk Anal., 2006, 26 (3), 747–752.
• [28] SALOP S.C., SCHEFFMAN D.T., Raising Rivals’ Costs, Am. Econ. Rev., 1983, 73 (2), 267–271.
• [29] SAPERSTEIN A.M., Mathematical Modeling of the Interaction Between Terrorism and Counter-Terrorism and Its Policy Implications, Complexity, 2008, 14 (1), 45–49.
• [30] SHAN X., ZHUANG J., Modeling Cumulative Defensive Resource Allocation Against a Strategic Attacker in a Multi-Period Multi-Target Sequential Game, Rel. Eng. Syst. Saf., 2018, 179, 12–26.
• [31] TULLOCK G., The Welfare Costs of Tariffs, Monopolies, and Theft, West. Econ. J., 1967, 5, 224–232 [Online], available: http://search.ebscohost.com/login.aspx?direct=true&db=eoh&AN=1222544&scope=site
• [32] UDWADIA F., LEITMANN G., LAMBERTINI L., A Dynamical Model of Terrorism, Disc. Dynam. Nature Soc., 2006, DOI: 10.1155/DDNS/2006/85653.
• [33] XU Z., ZHUANG J., A Study on a Sequential One‐Defender‐N‐Attacker Game, Risk Anal., 2019, 39 (6), 1414–1432.
• [34] ZHANG J., ZHUANG J., Modeling a Multi-Target Attacker-Defender Game with Multiple Attack Types, Rel. Eng. Syst. Saf., 2019, 185, 465–475.
• [35] ZHANG J., ZHUANG J.,JOSE V.R.R., The Role of Risk Preferences in a Multi-Target Defender-Attacker Resource Allocation Game, Rel. Eng. Syst. Saf., 2018, 169, 95–104.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).