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On improving dynamic decision‐making: implications from multiple‐process cognitive theory
Author(s) -
Bakken Bent Erik
Publication year - 2008
Publication title -
systems research and behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 1092-7026
DOI - 10.1002/sres.906
Subject(s) - dynamic decision making , intuition , cognition , decision field theory , computer science , process (computing) , decision theory , decision making , decision process , optimal decision , decision rule , decision engineering , management science , business decision mapping , artificial intelligence , cognitive science , psychology , decision support system , mathematics , decision tree , engineering , operations management , statistics , operating system , neuroscience , purchasing
Decision environments that afford unambiguous and transparent feedback allow humans to build up corresponding mental models that give them good decision guidance. But in many complex decision situations, such feedback is not available. In such environments people not only show initial misperceptions, but also fail to learn. Education and training have therefore been proposed, but with limited success. This paper applies multiple‐process cognitive theory to explain findings of poor learning and sub‐optimal decision‐making. The applied theory suggests that intuitive processes are default in decision‐making and natural learning. Analytic processes are more seldom applied. Implications for education and training in dynamic decision‐making are suggested, and in light of the primacy of intuitive processes, include massive exposure to complex dynamic decision‐making in order to improve upon intuition in such environments. Copyright © 2008 John Wiley & Sons, Ltd.