BİLİŞSEL BARİYERLERDEN KURUMSAL YÖNETİME: İSTİHBARAT TOPLULUKLARINDA ÖNYARGI YÖNETİMİ

Yıl 2024, Cilt: 3 Sayı: 5, 263 - 288, 30.01.2025

Nedim Havle , Sudiye Aksoy

Öz

Bu makale, istihbarat analizi sürecinde bilişsel önyargıların nasıl ortaya çıktığını ve bu önyargıları azaltmak için kullanılan yapılandırılmış analitik tekniklerin etkinliğini incelemektedir. Çalışmada ayna imgeleme, doğrulama, hedef saplantısı, yanlış analoji, seçicilik, güvenilirlik ve sabitleme önyargıları ile planlama yanılgısı ayrıntılı şekilde ele alınmıştır. Bu hatalar, analizlerin nesnelliğini zayıflatıp stratejik kararlarda önemli riskler doğurabilir. Araştırma, Rakip Hipotezlerin Analizi (ACH), Delphi Yöntemi, SWOT Analizi, Kırmızı Takım Analizi ve Önceden Belirlenmiş Kritik Olayların Analizi gibi tekniklerin önyargıları hafifletme potansiyelini değerlendirmektedir. Elde edilen bulgular, söz konusu yöntemlerin analistler tarafından doğru ve sistematik biçimde uygulandığında hatalı çıkarımların azalabileceğini göstermektedir. Ancak bu tekniklerin zaman, kaynak ve eğitim gerektirmesi gibi sınırlılıklarının yanı sıra, yapay zekâ ve algoritmik sistemlerde görülebilecek ek önyargı riskleri de ele alınmıştır. Çalışma sonuçlarına göre, bilişsel önyargıların yönetiminde kültürel, örgütsel ve teknolojik faktörlerin birbirleriyle etkileşimini anlamak kritik önem taşımaktadır. Ayrıca analistlerin kültürel zekâ, metabilişsel beceri ve etik farkındalık gibi nitelikleri geliştirmeleri önyargıların azaltılmasını kolaylaştırabilir. Gelecekteki araştırmaların, özellikle yapay zekâ uygulamalarının ve insan-makine iş birliğinin bilişsel önyargılar üzerindeki etkisini derinlemesine incelemesi önerilmektedir.

Anahtar Kelimeler

Bilişsel Önyargılar, İstihbarat Analizi, Yapılandırılmış Analitik Teknikler, Karar verme, Nesnellik

FROM COGNITIVE BARRIERS TO CORPORATE GOVERNANCE: BIAS MANAGEMENT IN THE INTELLIGENCE COMMUNITY

Öz

This paper explores the emergence of cognitive biases within the context of intelligence analysis and the efficacy of structured analytic techniques employed to mitigate these biases.The discussion encompasses a comprehensive examination of various cognitive biases, including mirror imagery, confirmation, target bias, false analogy, selectivity, reliability, fixation biases, and planning fallacy. These errors have the potential to compromise the objectivity of analyses and pose substantial risks to strategic decision-making processes. The study evaluates the potential of techniques such as Analysis of Competing Hypotheses (ACH), Delphi Method, SWOT Analysis, Red Team Analysis, and Analysis of Predetermined Critical Events to mitigate biases.The findings suggest that these methods can reduce erroneous inferences when applied correctly and systematically by analysts. However, the study also discusses the limitations of these techniques, including the time, resources, and training required for implementation, as well as the additional risks of bias that can be seen in artificial intelligence and algorithmic systems.The study's findings emphasize the critical importance of understanding the interaction of cultural, organizational, and technological factors in managing cognitive biases. Additionally, it suggests that developing qualities such as cultural intelligence, metacognitive skills, and ethical awareness in analysts can facilitate the reduction of biases. It is recommended that subsequent research investigate the impact of artificial intelligence applications and human-machine collaboration on cognitive biases in greater depth.

Anahtar Kelimeler

Cognitive Biases, Intelligence Analysis, Structured Analytical Techniques Bias Management, Decision Making, Objectivity

Kaynakça

• Ang, S., Van Dyne, L., & Koh, C. (2007). Cultural intelligence: Its measurement and effects on cultural judgment and decision making, cultural adaptation and task performance. Management and Organization Review, 3(3), 335-371.
• Aven T (2010) On the need for restricting the probabilistic analysis in risk assessment to variability. Risk Anal 30(3):354–360
• Buehler, R., Griffin, D., & Ross, M. (1994). Exploring the "Planning Fallacy": Why people underestimate their task completion times. Journal of Personality and Social Psychology, 67(3), 366-381.
• Choi, I., Nisbett, R. E., & Norenzayan, A. (1999). Causal attribution across cultures: Variation and universality. Psychological Bulletin, 125(1), 47-63.
• Dalton, J. (2019). SWOT Analysis (Strengths, Weaknesses, Opportunities, Threats). In: Great Big Agile. Apress.
• Dillmann, R., Nakamura, Y., Schaal, S., & Vernon, D. (Eds.). (2021). Cognitive Systems Monographs. Springer. Retrieved from [https://www.springer.com/series/8354]
• DoshiVelez, F. ve Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint.
• Echterhoff, J., Yarmand, M., & McAuley, J. (2022). AI-Moderated Decision-Making: Capturing and Balancing Anchoring Bias in Sequential Decision Tasks. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems.
• Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350-383.
• Edmondson, Amy ve Lei, Zhike. (2014). Psychological Safety: The History, Renaissance, and Future of an Interpersonal Construct. Annual Review of Organizational Psychology and Organizational Behavior. 1. 23-43.
• Fischhoff, B. (1996). The real world: What good is it? Organizational Behavior and Human Decision Processes, 65(3), 232-248.
• Flanagan J (1954) The critical incident technique. Psychological Bulletin 51(4):327–358
• Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707.
• Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspectives, 19, 25–42.
• George, J., Duffy, K., & Ahuja, M. (2000). Countering the anchoring and adjustment bias with decision support systems. Decis. Support Syst., 29, 195-206.
• Graafland, M., Schraagen, J. M., & Schijven, M. P. (2012). Systematic review of serious games for medical education and surgical skills training. British Journal of Surgery, 99 (10), 1322-1330.
• Hahn, U. ve Harris, A. J. L. (2014). What does it mean to be biased. Motivated reasoning and rationality. In Psychology of learning and motivation—Advances in research and theory (Vol. 61, pp. 41–102). Academic Press Inc.
• Hanafin S (2004) Review of literature on the Delphi technique.
• Hasson F, Keeney S (2011) Enhancing rigour in the Delphi technique research. Technol Forecast Soc Chang 78(9):1695–1704
• Heuer, R. J. (1999). Psychology of intelligence analysis. Central Intelligence Agency.
• Heuer, R. J. ve Pherson, R. H. (2020). Structured analytic techniques for intelligence analysis (3rd ed.). CQ Press.
• History.com (n.d.). 6 Daring Double Agents. Retrieved October 7, 2024, from https://www.history.com/news/6-daring-double-agents.
• Hoffman, B. G. (n.d.). What is Red Teaming? Retrieved from [https://www.redteamthinking.com/what-is-red-teaming]
• Holyoak, K. J. ve Thagard, P. (1995). Mental leaps: Analogy in creative thought. MIT Press.
• House, J. (2015). Translation quality assessment: Past and present. Routledge.
• Hung, W. (2013). Team-based complex problem solving: a collective cognition perspective. Educational Technology Research and Development, 61, 365-384.
• Ismail, G. ve Taliep, N. (2023). The Delphi Method. In: Liamputtong, P. (eds) Handbook of Social Sciences and Global Public Health. Springer, Cham.
• Janis, I. L. (1982). Groupthink: Psychological studies of policy decisions and fiascoes. Houghton Mifflin.
• Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
• Jussupow, E., Spohrer, K., Heinzl, A., & Gawlitza, J. (2021). Augmenting medical diagnosis decisions? An investigation into physicians' decisionmaking process with artificial intelligence. Information Systems Research, 31(3), 1160-1178.
• Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
• Kahneman, D. ve Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
• Kamar, E. (2016). Directions in hybrid intelligence: Complementing AI systems with human intelligence. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16). Retrieved from https://www.microsoft.com/en us/research/publication/directions-hybrid-intelligence-complementing-ai-systems-human-intelligence/.
• Karvetski, C.W., Olson, K.C., Gantz, D.T. et al (2013) Structuring and analyzing competing hypotheses with Bayesian networks for intelligence analysis. Euro J Decis Process 1, 205–231.
• Klein, G. (2007). Performing a project premortem. Harvard Business Review, 85(9), 18-19.
• Lantz-Deaton, C. ve Golubeva, I. (2020). What Are Critical Incidents and How Can We Get the Most Out of Them?. In: Intercultural Competence for College and University Students. Springer, Cham
• Lehner, P., Adelman, L., Cheikes, B., & Brown, M. (2008). Confirmation Bias in Complex Analyses. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 38, 584-592.
• Linstone, H. A. ve Turoff, M. (Eds.). (2002). The Delphi method: Techniques and applications. AddisonWesley.
• Kubicek, P. (1999). Russian Foreign Policy and the West. Political Science Quarterly, 114, 547-568.
• Mack, John E. (1976). A Prince of Our Disorder: The Life of T. E. Lawrence. Harvard University Press,
• Mandel DR, Collins RN, Walker AC, Fugelsang JA, Risko EF. (2022) Hypothesized drivers of the bias blind spot—cognitive sophistication, introspection bias, and conversational processes. Judgment and Decision Making.17(6):1392-1421.
• Mani, V. (2022). Strengthening Cybersecurity With Red Team Engagements. ISACA Journal. Retrieved from [https://www.isaca.org].
• Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6), 135.
• Mercier, H. ve Sperber, D. (2017). The enigma of reason. Harvard University Press
• Mersha, M., Lam, K., Wood, J., AlShami, A., & Kalita, J. (2024). Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction. Neurocomputing, 599, 128111.
• Modestus, Q. (2024). Diplomatic Discourse Analysis: Translating and Reconstructing “Zhengqueyiliguan” in China-Africa Relations. Fudan J. Hum. Soc. Sci. 17, 361–381.
• National Research Council. (2011). Intelligence analysis: Behavioral and social scientific foundations. The National Academies Press.
• Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175220.
• Nisbett, R. E., Peng, K., Choi, I., & Norenzayan, A. (2001). Culture and systems of thought: Holistic versus analytic cognition. Psychological Review, 108(2), 291-310.
• O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
• Pherson, K. H. ve Pherson, R. H. (2016). Critical thinking for strategic intelligence. CQ Press.
• Phillips LD (2007) Decision conference. In Edwards W, Miles RF, von Winterfeldt D (eds) Advances in decision analysis. Cambridge University Press.
• Rahwan, I., Cebrian, M., Obradovich, N. et al. (2019). Machine behaviour. Nature 568, 477–486.
• Rezaei, J. (2020). Anchoring bias in eliciting attribute weights and values in multi-attribute decision-making. Journal of Decision Systems, 30, 72 - 96.
• Rowe, G. ve Wright, G. (1999). The Delphi technique as a forecasting tool: Issues and analysis. International Journal of Forecasting, 15(4), 353-375.
• Salas, E., Tannenbaum, S. I., Kraiger, K., & SmithJentsch, K. A. (2012). The science of training and development in organizations: What matters in practice. Psychological Science in the Public Interest, 13(2), 74-101.
• Serrat, O. (2017). The Critical Incident Technique. In: Knowledge Solutions. Springer.
• Shapiro, S. ve Lawrence, J. A. (2014). The design and use of simulation computer games in education. Simulation & Gaming, 45(2), 131-133.
• Shelley, C. (2004). Analogy Counterarguments: A Taxonomy for Critical Thinking. Argumentation, 18, 223-238.
• Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99-118.
• Sticha P, Buede D, Rees RL (2005) APOLLO: an analytical tool for predicting a subject’s decision making. Presented at the international conference on intelligence analysis methods and tools, McLean
• Sunstein, C. R. ve Hastie, R. (2015). Wiser: Getting Beyond Groupthink to Make Groups Smarter. Harvard Business Review Press.
• Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House.
• Tavana, M., Soltanifar, M. & Santos-Arteaga, F.J. (2023). Analytical hierarchy process: revolution and evolution. Ann Oper Res 326, 879–907.
• Tecuci, G., Schum, D. A., Marcu, D., & Boicu, M. (2016). Intelligence analysis as discovery of evidence, hypotheses, and arguments. Cambridge University Press.
• Teece, D.J. (2018). SWOT Analysis. In: Augier, M., Teece, D.J. (eds) The Palgrave Encyclopedia of Strategic Management. Palgrave Macmillan.
• Thomas, D. C. ve Inkson, K. (2017). Cultural intelligence : surviving and thriving in the global village (Third edition). Berrett-Koehler Publishers, Inc. http://www.books24x7.com/marc.asp?bookid=117513
• Toh, R.Q.E., Koh, K.K., Lua, J.K. et al. (2022). The role of mentoring, supervision, coaching, teaching and instruction on professional identity formation: a systematic scoping review. BMC Med Educ 22, 531.
• Toplak, M. E., West, R. F., & Stanovich, K. E. (2011). The Cognitive Reflection Test as a predictor of performance on heuristicsandbiases tasks. Memory & Cognition, 39(7), 1275-1289.
• University of Foreign Military and Cultural Studies. (2012). Red Team Handbook. UFMCS.
• UK Ministry of Defence. (2021). Red Teaming Handbook (3rd ed.). Head of Futures and Strategic Analysis, Ministry of Defence.
• U.S. Army. (2015). The applied critical thinking handbook. TRADOC G2.
• Wallrich, L., Opara, V., Wesołowska, M. et al. (2024). The Relationship Between Team Diversity and Team Performance: Reconciling Promise and Reality Through a Comprehensive Meta-Analysis Registered Report. J Bus Psychol
• Whitesmith, M. (2020). Cognitive bias in intelligence analysis: Testing the analysis of competing hypotheses method. Edinburgh University Press.
• Wilde, T., Velden, F., & Dreu, C. (2018). The anchoring-bias in groups. Journal of Experimental Social Psychology, 76, 116-126.
• Yang X ve Krajbich I. (2021) Webcam-based online eye-tracking for behavioralresearch. Judgment and Decision Making. 16(6):1485-1505.
• Zenko, M. (2016). Red team: How to succeed by thinking like the enemy. Basic Books.