Abstract
This chapter discusses three clusters of approaches to measure and detect cognitive biases in the context of a Visual Analytics Environment (VAE). The first approach is called theory-driven since it refers to purely expert-driven methods. One of these methods is based on the definition of design recommendations for interactive data visualizations to reduce the potential effects of cognitive biases. Another expert-driven method is the in-depth analysis of a VAE and the cognitive biases it may induce. The second, empirical approach, encompasses behavioral observations, as well as experimental methods to operationalize cognitive biases. We describe methods to measure the Confirmation Bias and the Clustering Illusion. Finally, the third approach refers to data-driven methods that enable a non-invasive measurement of cognitive biases. This method is based on data-mining techniques to analyze and interpret user interaction patterns in terms of cognitive biases.
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References
APA (2009) Publication Manual of the American Psychological Association, 6th edn. American Psychological Association, Washington DC, USA
Barron G, Leider S (2010) The role of experience in the Gambler’s Fallacy. J Behav Decis Making 23(1):117–129
BMI (2017) Sicherheit 2016 - Kriminalitaetsentwicklung fuer Oesterreich. Bundesministerium fuer Inneres. http://bundeskriminalamt.at/501/files/BroschuereSicherheit_2016.pdf
Cook M, Smallman H (2008) Human factors of the confirmation bias in intelligence analysis: decision support from graphical evidence landscapes. Hum Factors: J Hum Factors Ergon Soc 50(5):745–754
Dearborn DC, Simon HA (1958) Selective perception: a note on the departmental identification of executives. Sociometry 21(2):140–144
Everitt BS, Landau S, Leese M, Stahl D (2011) Cluster analysis. Wiley, New York
Festinger L (1957) A theory of cognitive dissonance. Stanford University Press, Stanford
Fischer P, Fischer J, Weisweiler S, Frey D (2010) Selective exposure to information: how different modes of decision making affect subsequent confirmatory information processing. Br J Soc Psychol 49(4):871–881
Fischer P, Greitemeyer T, Frey D (2008) Self-regulation and selective exposure: the impact of depleted self-regulation resources on confirmatory information processing. J Pers Soc Psychol 94(3):382–395
Fischer P, Schulz-Hardt S, Frey D (2008) Selective exposure and information quantity: how different information decision makers’ preference for consistent and inconsistent information. J Pers Soc Psychol 94(2):231–244
Gigerenzer G (1994) Why the distinction between single-event probabilities and frequencies is important for psychology (and vice versa). Wiley, Chichester, pp 129–161
Gilovich TD (1991) How we know what isnt so: the fallibility of human reason in everyday life. The Free Press, New York
Hillemann EC, Nussbaumer A, Albert D (2015) The role of cognitive biases in criminal intelligence analysis and approaches for their mitigation. In: Brynielsson J, Yap MH (eds) Proceedings of the European intelligence and security informatics conference (EISIC 2015). IEEE, New York, USA, pp 125–128
Jones P, Roelofsma P (2000) The potential for social contextual and group bias in team decision-making: biases, conditions and psychological mechanisms. Ergonomics 43(8):1129–1152
Kahneman D (2011) Thinking, fast and slow. Farrar, Straus and Giroux, New York
Mussweiler T (2002) The malleability of anchoring effects. Exp Psychol 49(1):67–72
Nickerson RS (1998) Confirmation bias: a ubiquitous phenomenon in many guises. Rev Gen Psychol 2(2):175–220
Nussbaumer A, Verbert K, Hillemann EC, Bedek MA, Albert D (2016) A framework for cognitive bias detection and feedback in a visual analytics environment. In: Brynielsson J, Johansson F (eds) Proceedings of European intelligence and security informatics conference (EISIC 2016). IEEE, New York, pp 148–151
Okoli C, Pawlowski SD (2004) The Delphi method as a research tool: an example, design considerations and applications. Inf Manage 42(1):15–29
Steinwart I, Christmann A (2008) Support vector machines. Springer, New York. https://doi.org/10.1007/978-0-387-77242-4
Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185(4157):1124–1131
Tversky A, Kahneman D (1981) The framing of decisions and the psychology of choice. Science 211(4481):453–458
Wong BLW (2014) How analysts think (?): early observations. In: Proceedings of the IEEE joint intelligence and security informatics conference. IEEE, New York, pp 296–299
Wong BLW, Kodagoda N (2015) How analysts think: inference making strategies. In: Proceedings of the human factors and ergonomics society annual meeting, pp 269–273
Acknowledgements
The research leading to the results reported here has received funding from the European Union Seventh Framework Programme through Project VALCRI, European Commission Grant Agreement N\(^{\circ }\) FP7-IP-608142, awarded to B.L. William Wong, Middlesex University London and Partners.
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Bedek, M.A., Nussbaumer, A., Huszar, L., Albert, D. (2018). Methods for Discovering Cognitive Biases in a Visual Analytics Environment. In: Ellis, G. (eds) Cognitive Biases in Visualizations. Springer, Cham. https://doi.org/10.1007/978-3-319-95831-6_5
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DOI: https://doi.org/10.1007/978-3-319-95831-6_5
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