ABSTRACT
I n numerous recent publications (Betsch, 2005; Damasio, 1994; Dougherty, Gettys, & Ogden, 1999; Haidt, 2001; Kahneman & Frederick, 2002) as well as in the previous chapters of this volume (e.g., T. Betsch, chap. 1, this volume; Deutsch & Strack, chap. 3, this volume; Epstein, chap. 2, this volume; Hogarth, chap. 6, this volume; Weber & Lindemann, chap. 12, this volume), the importance of automatic processes and intuitive strategies in decision making has been highlighted. Despite the increased interest in this topic, rigorous empirical tests that measure how many people indeed use such intuitive strategies compared with simplifying fast and frugal heuristics (Gigerenzer, Todd, & the ABC Research Group, 1999) and complex-rational serial weighted additive strategies (Payne, Bettman, & Johnson, 1988) are still rare. At least two reasons for this can be identied. First, some of the automatic models make it hard to derive testable and unique predictions (e.g., Damasio, 1994; Slovic, Finucane, Peters, & McGregor, 2002). Second, classical methods to detect decision strategies like information search analysis (Payne et al., 1988) or analysis of think-aloud protocols (Montgomery & Svenson, 1983) are not capable of detecting intuitive decision strategies and might even hamper their application (Glöckner & Betsch, 2006; Hamm, chap. 4, this volume). My aims in this chapter are to outline a method that avoids these problems and to present data from a research program that closes the empirical gap.
