[AISWorld] Call for Papers: Special issue on Cognitive Bias, Decision Styles, and Risk Attitudes - Journal of Decision Systems
mmora at securenym.net
mmora at securenym.net
Thu Feb 1 14:45:11 EST 2018
MOTIVATION:
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 are: 1) the
consideration of decision-making styles which are part of the cognitive
styles of decision makers (Chakraborty et al., 2008), and 2) 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.
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
DEADLINES:
Submission deadline: August 30, 2018
First editorial decision: October 15, 2018
Submission deadline for conditionally accepted papers: November 15, 2018
Final editorial decision: November 30, 2018
Camera-ready material submission: December 30, 2018
GUEST CO-EDITORS:
Prof. Gloria Phillips-Wren, Loyola University, USA
Prof. Daniel Power, University of Northern Iowa, USA
Prof. Manuel Mora, Autonomous University of Aguascalientes, Mexico
THE JOURNAL OF DECISION SYSTEMS WEBSITE:
http://www.tandfonline.com/toc/tjds20/current
References
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, for
Decision Support News Vol. 15, No. 19; updated December 7, for Decision
Support News 12-11-2016 Vol. 17 No. 25. On December 7, 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
conditionsa 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.
--------------------------------------------------
Manuel Mora, EngD.
Full-time Professor and Researcher Level C
ACM Senior Member / SNI Level I
Coordinator of the CONACYT-PNPC MSc in Informatics
Department of Information Systems
Autonomous University of Aguascalientes
Ave. Universidad 940
Aguascalientes, AGS
Mexico, 20131
Linkedin Weblink: https://www.linkedin.com/in/manuel-mora-engd-37b03a1/
ResearchGate Weblink:
https://www.researchgate.net/profile/Manuel_Mora
Scholar Google
Wehttps://scholar.google.com.mx/citations?hl=en&user=97rTgbkAAAAJ&view_op=list_works&sortby=pubdateblink:
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