Cognitive bias is implicit in decision making since individuals develop predictable thinking patterns (Tversky and Kahneman, 1974; Dvorsky, 2013). While many patterns are positive and reflect rational decision making, other patterns lead to poor choices. Although an individual can learn to overcome some of their biases, there is an individual component to the way that people process and use information. Decision aids and decision support systems (DSS) can reinforce biases or improve the way that a user thinks about a situation (Silver, 1990). Since people have limitations as information processors, biases can and often do reduce the amount of thinking and processing that a person does to make a choice, especially in stressful or time-limited situations. The way that information is presented and the way analyses are conducted also impacts the amount of cognitive resources and information gathering that a person requires in a situation (Power, 2004, 2016). These considerations are important to the design of DSS in overcoming cognitive bias (Arnott, 2006).
As an example of why this matters in the case of big data, consider ‘recency bias’, a concept that describes the human tendency to elevate the importance of recent experience in estimating future events (Chatfield, 2016). Recency bias is a version of the availability heuristic, i.e. the tendency to base thinking disproportionately on whatever comes most easily to your mind. Big data gives us an overwhelming amount of recent data, so both in terms of size and recency it tends to overwhelm smaller and more distant data that could be important in decision making. The conditions appear ripe for cognitive bias to creep into decision making as big data becomes more mainstream.
Additionally, two conditions that influence DSS acceptance is the consideration of decision-making styles which are part of the cognitive styles of decision makers (Chakraborty et al., 2008) and the decision makers’ attitudes toward taking or avoiding risks (Schiebener and Brand, 2015). Cognitive styles are “the way in which people process and organize information and arrive at judgments or conclusions based on their observations” (Leonard et al., 1999; p. 407). Cognitive styles research has been a core topic in management science (Armstrong et al., 2012), and it has been identified as a relevant construct for DSS design research (Engin and Vetschera, 2017). Decisions involving risk imply that the “outcome of choosing an option cannot be guaranteed. Thus, an individual is confronted with the risk that the outcome will be worse than optimal” (Schiebener and Brand, 2015; p. 171). Decision makers may be risk-averse, risk-neutral or risk-taking, and their risk tolerance impacts DSS acceptance (Schiebener and Brand, 2015; Holt and Laury, 2002).
In this special issue, we are interested in collecting studies on cognitive biases, decision styles, and risk attitudes in decision makers and how negative ones can be reduced with adequate DSS designs or implementations. These topics can be drawn from theoretical or case studies that address decision making or the design, development and use of DSS. Fruitful areas for investigation include DSS, visualization, information processing, healthcare IT, big data, analytics, organizational decision making, intelligent DSS, personalization, cognitive computing, and methodologies for information processing, design science, and research design.
Thus, we call for papers addressing the following topics (but not limited to):
- Cognitive biases in decision making
- Impact of cognitive biases on decision making
- Decision styles in decision making
- Impact of decision styles on decision making
- Risk attitudes in decision making
- Impact of risk attitudes on decision making
- Methodologies to create DSS to guide decision making process
- Big data and analytics to foster data-driven decision making
- Short term vs long term data perspectives and influence on decision making
- Decisional guidance in DSS
- Innovative development methodologies for DSS
- Intelligent DSS (IDSS) to personalize user experience and reduce bias
- Case studies and applications such as Clinical DSS (CDSS)
- Influence of DSS on strategic and organizational decision making and bias
- Impact of social media on bias in decision making
- Design science approaches to improve DSS development
- Theories of cognitive bias to assist DSS development
- Executive information systems to improve data-driven decision making
Dates for your diary
- Submission date: December 30, 2018
- First editorial decision: January 30, 2019
- Submission deadline for conditioned papers: March 15, 2019
- Final editorial decision: April 1, 2019
- Camera-ready paper submission: April 30, 2019
- Add “Special Issue on Cognitive Bias” to the paper title and submit using the ‘Submit an article’ link on this Call for Papers, or via the JDS homepage. Before submitting, you will be asked to create an account on the Taylor & Francis submission portal.
- During submission, you will be asked for article type (Special Issue) and the special issue name (Special Issue on Cognitive Bias).
- If you are uploading a PDF, please also upload editable source files (e.g. .doc, .tex)
Arnott, D. (2006) Cognitive biases and decision support systems development: a design science approach. Information Systems Journal, 16(1), 55-78.
Chakraborty, I., Hu, P. J. H., and Cui, D. (2008). Examining the effects of cognitive style in individuals' technology use decision making. Decision Support Systems, 45(2), 228-241.
Chatfield, T. (2016). The Trouble with Big Data: The ‘Recency Bias’. http://www.bbc.com/future/story/20160605-the-trouble-with-big-data-its-called-the-recency-bias.
Dvorsky, G. (2013) The 12 cognitive biases that prevent you from being rational, io9, 1/09/13, at URL http://io9.com/5974468/the-most-common-cognitive-biases-that-prevent-you-from-being-rational.
Engin, A., and Vetschera, R. (2017). Information representation in decision making: The impact of cognitive style and depletion effects. Decision Support Systems, 103, 94-103.
Holt, C. A., and Laury, S. K. (2002). Risk Aversion and Incentive Effects. The American Economic Review, 92(5), 1644-1655.
Leonard, N. H., Scholl, R. W., and Kowalski, K. B. (1999). Information processing style and decision making. Journal of Organizational Behavior, 20(3), 407-420.
Power, D. (2004) Do DSS builders assume their targeted users are rational thinkers? DSS News, 5(21), October 10.
Power, D. J. (2016) "Can computerized decision support systems impact, eliminate, exploit, or reduce cognitive biases in decision making?" DSS News, Vol. 6, No. 20, September 11, 2005; updated September 13, 2014 for Decision Support News Vol. 15, No. 19; updated December 7, 2016 for Decision Support News 12-11-2016 Vol. 17 No. 25. On December 7, 2016 the title of this column was shortened to "Can computerized decision support reduce cognitive biases in decision making?"
Schiebener, J., and Brand, M. (2015). Decision making under objective risk conditions–a review of cognitive and emotional correlates, strategies, feedback processing, and external influences. Neuropsychology Review, 25(2), 171-198.
Silver, M.J. (1991). Decisional guidance for computer-based support. MIS Quarterly, 15(1), 105-133.
Silver, M.J. (1990). Decision support systems: Directed and non-directed change. Information Systems Research, 1(1), 47-70.
Tversky, A. and Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124-1131.
- Guest Editor: Gloria Phillips-Wren, Loyola University Maryland, USA
- Guest Editor: Daniel Power, University of Northern Iowa, USA
- Guest Editor: Manuel Mora, Autonomous University of Aguascalientes, Mexico