Adversarial risk analysis in national security decision problems: a hypothetical case of Eastern Mediterranean conflict

Yıl 2023, Cilt: 5 Sayı: 1, 62 - 73, 08.06.2023

Gülşah Bozcu , Esma Nur Çinicioğlu

https://doi.org/10.58588/aru-jfeas.1247679

Öz

Adversarial risk analysis (ARA), with its capabilities to analyze the opponent’s decision-making process, predict the possible steps that the opponent may follow and choose the decision choice to maximize the analyst’s expected utility, is an effective model to be used in strategic decision problems involving an intelligent opponent. In contrast to classical game theory adversarial risk analysis does not employ the common knowledge assumption and instead uses a subjective probability distribution over the opponent’s decisions and the utilities. Consequently, problems modelled using ARA can be solved realistically. In strategic decision problems concerning national security, using analytical models which can offer a systematic view and evaluation process becomes important. In this work, a hypothetical problem regarding Turkey’s Eastern Mediterranean conflict is modeled and solved using ARA. The ARA model exemplified in this work has the capability both to analyze the opponent’s decision-making process and also to make scenario analyses based on the evidence observed on the model. This research is the first to theme the Eastern Mediterranean conflict, a strategically important decision-making problem concerning national security and to solve its hypothetical model created using the Adversarial Risk Analysis.

Anahtar Kelimeler

Adversarial Risk Analysis, Influence Diagrams, Eastern Mediterranean Conflict, Defense-Attack Problem

Ulusal güvenlik karar problemlerinde karşıtsal risk analizi: Doğu Akdeniz problemi örnek modelleme

Öz

Karşıtsal risk analizi (KRA) akıllı rakipleri barındıran problemlerde karar vericiye sağladığı, rakibin karar verme sürecini ve düşünme sistematiğini analiz etme, rakibin atması muhtemel adımları öngörme ve bu doğrultuda beklenen faydasını maksimize edecek karar seçeneğini belirleme gibi özellikleri ile stratejik karar problemlerine başarılı bir şekilde uygulanabilecek etkin bir modelleme yöntemidir. KRA’nın, klasik oyun teorisinin aksine ortak bilgi varsayımını benimsememesi ve rakip tarafın karar ve faydaları için öznel bir olasılık dağılımı kullanması, KRA ile modellenen problemlerin gerçekçi bir şekilde çözümüne imkân tanımaktadır. Milli güvenliği ilgilendiren stratejik karar problemlerinde sistematik bir bakış açısı ve değerlendirme imkânı sağlayan analitik modellerin kullanılması önem taşımaktadır. Bu çalışmada Türkiye’nin Doğu Akdeniz problemi ele alınmış ve konu çerçevesinde oluşturulan hipotetik bir örneğin KRA ile modellemesi ve çözümü yapılmıştır. Oluşturulan model, hem rakip tarafın düşünce sistematiğinin analiz edilmesine imkân vermekte hem de model çerçevesinde gözlemler dâhilinde senaryo analizlerini mümkün kılmaktadır. Stratejik bakımdan önemi büyük, milli güvenliğe dair problemlerin değerlendirilmesi için, farklı, etkin analitik modellerin uygulanabilirliğinin gösterilmesi önemlidir. Bu çalışma, bir ulusal güvenlik stratejik karar problemi olan Doğu Akdeniz meselesini konu alması ve çalışmada oluşturulan alan hipotetik modeli KRA ile çözmesi bakımından bir ilk niteliğindedir.

Anahtar Kelimeler

Karşıtsal Risk Analizi, Etki Diyagramları, Doğu Akdeniz Problemi, Savunma-Saldırı Modeli

Kaynakça

• Alpar, G. (2019). Rum-Yunan İkilisi Mısır, İsrail ve Lübnan’ın Akdeniz’deki Haklarını Gasp Ediyor. https://www.sde.org.tr/guray-alpar/genel/rum-yunan-ikilisi-misir-israil-ve-lubnanin-akdenizdeki-haklarini-gasp-ediyor-kose-yazisi-14308
• Banks, D., Gallego, V., Naveiro, R. ve Ríos Insua, D. (2022). Adversarial risk analysis: An overview. Wiley Interdisciplinary Reviews: Computational Statistics, 14(1), 1–16. https://doi.org/10.1002/wics.1530
• Banks, D. L., Rios, J., & Ríos Insua, D. (2016). Adversarial risk analysis.
• Banks, D., Petralia, F. ve Wang, S. (2011). Adversarial risk analysis: Borel games. Applied Stochastic Models in Business and Industry, 27(2), 72–86. https://doi.org/10.1002/asmb.890
• BayesFusion. (2022). GeNIe modeler software package. https://www.bayesfusion.com/genie
• Cano, J., Insua, D. R., Tedeschi, A. ve Turhan, U. (2014). Security economics: an adversarial risk analysis approach to airport protection. Annals of Operations Research, 245(1–2), 359–378. https://doi.org/10.1007/s10479-014-1690-7
• Cano, J., Pollini, A., Falciani, L. ve Turhan, U. (2016). Modeling current and emerging threats in the airport domain through adversarial risk analysis. Journal of Risk Research, 19(7), 894–912. https://doi.org/10.1080/13669877.2015.1057201
• Deng, L. ve Ma, B. (2015). Application of adversarial risk analysis model in pricing strategies with remanufacturing. Journal of Industrial Engineering and Management, 8(1), 1–20. https://doi.org/10.3926/jiem.1223
• Ejaz, M., Joshi, C. ve Joe, S. (2021). Adversarial risk analysis for first-price sealed-bid auctions. Australian and New Zealand Journal of Statistics, 63(2), 357–376. https://doi.org/10.1111/anzs.12315
• Esteban, P. G., Razuri, J. G. ve Insua, D. R. (2012). An Adversarial Risk Analysis Model for a Decision Agent facing Multiple Users. IEEE.
• Gil, C. ve Parra-Arnau, J. (2019). An adversarial-risk-analysis approach to counterterrorist online surveillance. Sensors (Switzerland), 19(3), 1–26. https://doi.org/10.3390/s19030480
• Gil, C., Rios Insua, D. ve Rios, J. (2016). Adversarial Risk Analysis for Urban Security Resource Allocation. Risk Analysis, 36(4), 727–741. https://doi.org/10.1111/risa.12580
• González-Ortega, J., Ríos Insua, D. ve Cano, J. (2019). Adversarial risk analysis for bi-agent influence diagrams: An algorithmic approach. European Journal of Operational Research, 273(3), 1085–1096. https://doi.org/10.1016/j.ejor.2018.09.015
• Howard, R. A. ve Matheson, J. E. (1984). Decision analysis volume II: professional collection. Strategic Decisions Group.
• Insua, D. R., Banks, D., Ríos, J. ve González-Ortega, J. (2021). Adversarial risk analysis as a decomposition method for structured expert judgement modelling. International Series in Operations Research and Management Science, 293(1991), 179–196. https://doi.org/10.1007/978-3-030-46474-5_7
• Insua, D. R., Couce-Vieira, A., Rubio, J. A., Pieters, W., Labunets, K. ve G. Rasines, D. (2021). An adversarial risk analysis framework for cybersecurity. Risk Analysis, 41(1), 16–36. https://doi.org/10.1111/risa.13331
• Insua, D. R., Rios, J. ve Banks, D. (2009). Adversarial risk analysis. Journal of the American Statistical Association, 104(486), 841–854. https://doi.org/10.1198/jasa.2009.0155
• Insua, D. R., Ruggeri, F., Alfaro, C. ve Gomez, J. (2016). Robustness for adversarial risk analysis, M. Doumpos, C. Zopounidis ve E. Grigoroudis (Eds.), Vol. 241. Springer International Publishing. https://doi.org/10.1007/978-3-319-33121-8
• Joshi, C., Insua, D. R. ve Rios, J. (2019). Insider threat modeling: An adversarial risk analysis approach. http://arxiv.org/abs/1911.09945
• Kjaerulff, U. B. ve Madsen, A. L. (2008). Bayesian networks and influence diagrams: a guide to construction and analysis (information science and statistics). Springer.
• Koller, D. ve Milch, B. (2003). Multi-agent influence diagrams for representing and solving games. Games and Economic Behavior, 45(1), 181–221. https://doi.org/10.1016/S0899-8256(02)00544-4
• McLay, L., Rothschild, C. ve Guikema, S. (2012). Robust adversarial risk analysis: A level-k approach. Decision Analysis, 9(1), 41–54. https://doi.org/10.1287/deca.1110.0221
• Miller, A., Merkhofer, M. ve Howard, R. (1976). Development of automated aids for decision analysis. Stanford Research Institute.
• National Research Council. (2008). Department of homeland security bioterrorism risk assessment: a call for change. National Academies Press, 2, 2–5.
• Niyaz, Q., Sun, W. ve Alam, M. (2015). Impact on SDN powered network services under adversarial attacks. Procedia Computer Science, 62, 228–235. https://doi.org/10.1016/j.procs.2015.08.444
• Onder, M. ve Akıncı, N. (2020). Akdeniz’de petrol ve doğalgaz aramalarının bölge enerji ve güvenlik politikalarına etkisi. https://www.researchgate.net/publication/338801871
• Perry, M. ve El-Amine, H. (2019). Computational efficiency in multivariate adversarial risk analysis models. Decision Analysis, 16(4), 314–332. https://doi.org/10.1287/deca.2019.0394
• Ren, K., Zheng, T., Qin, Z. ve Liu, X. (2020). Adversarial attacks and defenses in deep learning. Engineering, 6(3), 346–360. https://doi.org/10.1016/j.eng.2019.12.012
• Rios, J. ve Insua, D. R. (2012). Adversarial risk analysis for counterterrorism modeling. Risk Analysis, 32(5), 894–915. https://doi.org/10.1111/j.1539-6924.2011.01713.x
• Roponen, J. ve Salo, A. (2015). Adversarial Risk Analysis for Enhancing Combat Simulation Models. Journal of Military Studies, 6(2), 82–103. https://doi.org/10.1515/jms-2016-0200
• Rothschild, C., Mclay, L. ve Guikema, S. (2012). Adversarial risk analysis with incomplete information: A level-k approach. Risk Analysis, 32(7), 1219–1231. https://doi.org/10.1111/j.1539-6924.2011.01701.x
• Sevillano, J. C., Insua, D. R. ve Rios, J. (2012). Adversarial risk analysis: The Somali pirates case. Decision Analysis, 9(2), 86–95. https://doi.org/10.1287/deca.1110.0225
• Velu, C. ve Iyer, S. (2008). The rationality of irrationality for managers: returns-based beliefs and the traveler’s dilemma. http://ssrn.com/abstract=1334909
• Vieira, A. C., Houmb, S. H. ve Insua, D. R. (2014). A graphical adversarial risk analysis model for oil and gas drilling cybersecurity. Electronic Proceedings in Theoretical Computer Science, EPTCS, 148, 78–93. https://doi.org/10.4204/EPTCS.148.6
• Wang, S. ve Banks, D. (2011). Network routing for insurgency: an adversarial risk analysis framework. Naval Research Logistics, 58(6), 595–607. https://doi.org/10.1002/nav.20469
• Wang, W., Di Maio, F. ve Zio, E. (2019). Adversarial risk analysis to allocate optimal defense resources for protecting cyber–physical systems from cyber attacks. Risk Analysis, 39(12), 2766–2785. https://doi.org/10.1111/risa.13382
• Wortman, P. A. ve Chandy, J. A. (2020). SMART: Security model adversarial risk-based tool for systems security design evaluation. Journal of Cybersecurity, 6(1), 1–8. https://doi.org/10.1093/cybsec/tyaa003
• Wortman, P. A., Tehranipoor, F. ve Chandy, J. A. (2018, December 4). An adversarial risk-based approach for network architecture security modeling and design. 2018 International Conference on Cyber Security and Protection of Digital Services, Cyber Security 2018. https://doi.org/10.1109/CyberSecPODS.2018.8560685
• Yaycı, C. (2012). Doğu Akdeniz’de deniz yetki alanlarının paylaşılması sorunu ve Türkiye, Bilgi Strateji, 4(6), 1–70.
• Zhang, L. ve Reniers, G. (2018). Applying game theory for adversarial risk analysis in chemical plants. In TU Delft University. https://doi.org/10.4233/uuid:eec6ef3b-3d9d-4b7d-8d9b-02fa5a4d9245