OPEN ACCESS
Cognitive biases are unconscious deviations in judgement - rooted in the very nature of the human mind – that represent a common source of failure in intelligence analysis. This article investigates the relationship between cognitive biases and big data analytics in the intelligence domain. Big data analytics tools can assist analysts with the discovery of hidden patterns within large datasets, thus eliminating some of the factors responsible for the rise of cognitive biases. Such technologies, however, do not make analysts immune to cognitive biases and, if in the hands of inexperienced users, may even facili- tate their occurrence. To illustrate this dynamics, the article provides a series of examples showing how different types of IT technologies commonly used by intelligence analysts may cause or facilitate the emergence of specific cognitive biases. Building on the work carried out during the RECOBIA and LEILA projects, the article proposes serious games as a solution for mitigating the effects of both IT- and non-IT- induced cognitive biases.
intelligence analysis, IT-induced cognitive biases, RECOBIA, LEILA, serious games.
[1] Frini, A., An intelligence process model based on a collaborative approach. Proceeding of the 16th International Command and Control Research and Technology Symposium (ICCRTS 2011), 2011.
[2] Penn State College of Earth and Mineral Sciences. The Intelligence Process, available at https://courseware.e-education.psu.edu/courses/bootcamp/lo07/09.html
[3] CIA. The Intelligence Cycle, available at https://www.cia.gov/kids-page/6-12th-grade/who-we-are-what-we-do/the-intelligence-cycle.html
[4] George, R.Z. & Bruce, J.B., Analyzing Intelligence Origins, Obstacles, and Innovations, George Town University Press: Washington DC, 2008.
[5] Couch, N. & Robins, B., Big Data for Defence and Security, available at http://www.rusi.org/downloads/assets/RUSI_BIGDATA_Report_2013.pdf
[6] Cukier, K. & Mayer-Schoenberger, V., The rise of big data: how it’s changing the way we think about the world. Foreign Affairs, 92(May/June), pp. 28–40, 2013. http://doi.org/10.1515/9781400865307-003
[7] Laney, D., 3-D Data Management: Controlling Data Volume, Velocity and Variety, available at http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
[8] Cabena, P., Hadjinian, P., Stadler, R., Verhees, J. & Zanasi, A., Discovering Data Mining. From Concept to Implementation, Prentice Hall: Upper Saddle River, NJ, 1998.
[9] de Bruin, J.S., Cocx, T.K., Kosters, W.A., Laros, J.F.J. & Kok, J.N., Data mining approaches to criminal career analysis. Proceedings of ICDM 2006, the IEEE International Conference on Data Mining, pp. 171–177, 2006.
[10] Mena, J., Investigative Data Mining for Security and Criminal Detection, Elsevier: Amsterdam, 2003.
[11] Zanasi, A., (ed). Text Mining and its Applications to Intelligence, CRM and Knowledge Management, WIT Press: Southampton, UK, 2007.
[12] Herz, J. & Bellaachia, A., The Authorship of Audacity: Data Mining and Stylometric Analysis of Barack Obama Speeches. Proceedings of the International Conference on Data Mining (DMIN), 2014.
[13] Zinner, C., Intelligence Must Plan to Develop Tomorrow’s Analyst, available at http://www.afcea.org/content/?q=intelligence-must-plan-develop-tomorrows-analyst
[14] Smith, S.W., Security and cognitive bias: exploring the role of the mind. IEEE Security & Privacy, 10, pp. 75–78, 2012. http://doi.org/10.1109/msp.2012.126
[15] Haselton, M.G., Nettle, D. & Andrews, P.W., The evolution of cognitive bias. In Buss, D.M. (Ed.), The Handbook of Evolutionary Psychology, John Wiley & Sons Inc: Hoboken, NJ, US, pp. 724–746, 2005.
[16] Heuer, R.J., Psychology of Intelligence Analysis, Books Express Publishing: Saffron Walden, UK, 2010.
[17] Wheaton, K.J. & Richey, M.K., You Can’t Beat Biases with Big Numbers, available at http://www.growthconsulting.frost.com/web/images.nsf/0/3D6C419B4830EF0286257C55005D54CD/$File/SCIP14V6I1_IndustryInsight_Kristan.htm
[18] RECOBIA project, available at http://www.recobia.eu
[19] Pirolli, P. & Card, S., The Sensemaking Process and Leverage Points for Analyst Technology as Identified through Cognitive Task Analysis. Proceeding of the International Conference on Intelligence Analysis, 2005.
[20] Bar-Ilan, J., Google bombing from a time perspective. Journal of Computer-Mediated Communication, 12(3), pp. 910–938, 2007. https://doi.org/10.1111/j.1083-6101.2007-00356.x
[21] Zanasi, A., Cyber Defense, Cyber Intelligence e relative armi. Casi di collaborazione tra pubblica amministrazione, industria e ricerca finanziata dalla Commissione Europea. Cyber Warfare 2014. Armi cibernetiche, sicurezza nazionale e difesa del business, eds. S. Gori & S. Lisi, Franco Angeli: Milano, 2015.
[22] Winter, L.-C., Bedek, M. & Albert, D., Mitigating cognitive biases in intelligence analysis. Journal for Intelligence, Propaganda and Security Studies, 7(2), pp. 140–151, 2013.
[23] Hillemann, E.-C., Nussbaumer, A. & Albert, D., The Role of Cognitive Biases in Criminal Intelligence Analysis and Approaches for their Mitigation. Proceedings of the European Intelligence and Security Informatics Conference (EISIC), pp. 125–128, 2015.
[24] Reese, E.J., Techniques for mitigating cognitive biases in fingerprint identification. UCLA Law Review, 59, pp. 1252–1290, 2012.
[25] US Government. A Tradecraft Primer: Structured Analytic Techniques for Improving Intelligence Analysis, available at https://www.cia.gov/library/center-for-the-study-ofintelligence/csi-publications/books-and-onographs/Tradecraft%20Primer-apr09.pdf
[26] Dreisbach, G. & Fischer, R., Conflicts as aversive signals. Brain and Cognition, 72, pp. 94–98, 2012.
https://doi.org/10.1016/j.bandc.2011.12.003
[27] Libes, D. & O’Connell, T., Applying Serious Games to Intelligence Analysis. Proceedings of SEA ‘07, the 11th IASTED International Conference on Software Engineering and Applications, ACTA Press: Anaheim, CA, pp. 311–317, 2007.
[28] IARPA’s Sirius program, available at https://www.iarpa.gov/index.php/research-programs/Sirius
[29] Dunbar, N.E., et al., MACBETH: development of a training game for the mitigation of cognitive bias. International Journal of Game-Based Learning, 3(4), pp. 7–26, 2013. https://doi.org/10.4018/ijgbl.2013100102
[30] Symborski, C., Barton, M., Quinn, M., Morewedge, C.K., Kassam, K.S., & Korris, J.H., Missing: A Serious Game for the Mitigation of Cognitive Biases. Proceedings of the Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), 2014.
[31] LEILA project, available at http://www.leila-project.eu
[32] Zanasi, A., Ruini, F. & Bonzio, A., Intelligence analysts’ training through serious dames: the LEILA project. International Journal of Safety and Security Engineering, 7(3), pp. 380–389, 2017. https://doi.org/10.2495/safe-v7-n3-380-389
[33] Kohavi, R. & Provost, F., On applied research in machine learning. Machine Learning, 30, pp. 271–274, 1998.
https://doi.org/10.1023/a:1017181826899