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Multiple-Reason Decision Making Based on Automatic Processing


Glöckner,  Andreas
Max Planck Institute for Research on Collective Goods, Max Planck Society;

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Glöckner, A., & Betsch, T. (2008). Multiple-Reason Decision Making Based on Automatic Processing.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0028-6E1A-6
It has been repeatedly shown that in decisions under time constraints, individuals predominantly use noncompensatory strategies rather than complex compensatory ones. We argue that these findings might be due not to limitations of cognitive capacity but instead to limitations of information search imposed by the commonly used experimental tool Mouselab (Payne et al., 1988). We tested this assumption in three experiments. In the first experiment, information was openly presented, whereas in the second experiment the standard Mouselab program was used under different time limits. The results indicate that individuals are able to compute weighted additive decision strategies extremely quickly if information search is not restricted by the experimental procedure. In a third experiment, these results were replicated using more complex decision tasks, and the major alternative explanations that individuals use more complex heuristics or merely encode the constellation of cues were ruled out. In sum, the findings challenge the fundaments of bounded rationality and highlight the importance of automatic processes in decision making.