Experts and Decision Making: First Steps Towards a Unifying Theory of Decision Making in Novices, Intermediates and Experts
Expertise research shows quite ambiguous results on the abilities of experts in judgment and decision making (JDM) classic models cannot account for. This problem becomes even more accentuated if different levels of expertise are considered. We argue that parallel constraint satisfaction models (PCS) might be a useful base to understand the processes underlying expert JDM and the hitherto existing, differentiated results from expertise research. It is outlined how expertise might influence model parameters and mental representations according to PCS. It is discussed how this differential impact of expertise on model parameters relates to empirical results showing quite different courses in the development of expertise; allowing, for example, to predict under which conditions intermediates might outperform experts. Methodological requirements for testing the proposed unifying theory under complex real-world conditions are discussed.
|Date of creation:||Jan 2009|
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- Andreas Glöckner & Tilmann Betsch, 2008. "Modeling Option and Strategy Choices with Connectionist Networks: Towards an Integrative Model of Automatic and Deliberate Decision Making," Working Paper Series of the Max Planck Institute for Research on Collective Goods 2008_02, Max Planck Institute for Research on Collective Goods.
- Shanteau, James, 1992. "Competence in experts: The role of task characteristics," Organizational Behavior and Human Decision Processes, Elsevier, vol. 53(2), pages 252-266, November.
- Andreas Glöckner & Tilmann Betsch, 2008. "Multiple-Reason Decision Making Based on Automatic Processing," Working Paper Series of the Max Planck Institute for Research on Collective Goods 2008_12, Max Planck Institute for Research on Collective Goods.
- Hoffman, Robert R. & Shadbolt, Nigel R. & Burton, A. Mike & Klein, Gary, 1995. "Eliciting Knowledge from Experts: A Methodological Analysis," Organizational Behavior and Human Decision Processes, Elsevier, vol. 62(2), pages 129-158, May.
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