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The beauty of simple models: Themes in recognition heuristic research

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  • Daniel G. Goldstein
  • Gerd Gigerenzer

Abstract

The advantage of models that do not use flexible parameters is that one can precisely show to what degree they predict behavior, and in what situations. In three issues of this journal, the recognition heuristic has been examined carefully from many points of view. We comment here on four themes, the use of optimization models to understand the rationality of heuristics, the generalization of the recognition input beyond a binary judgment, new conditions for less-is-more effects, and the importance of specifying boundary conditions for cognitive heuristics.

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Bibliographic Info

Article provided by Society for Judgment and Decision Making in its journal Judgment and Decision Making.

Volume (Year): 6 (2011)
Issue (Month): 5 (July)
Pages: 392-395

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Handle: RePEc:jdm:journl:v:6:y:2011:i:5:p:392-395

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Related research

Keywords: recognition heuristic; less-is-more; memory; model comparison.;

References

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  1. C. Philip Beaman & Philip T. Smith & Caren A. Frosch & Rachel McCloy, 2010. "Less-is-more effects without the recognition heuristic," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 5(4), pages 258-271, July.
  2. Gerd Gigerenzer & Daniel G. Goldstein, 2011. "The recognition heuristic: A decade of research," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(1), pages 100-121, February.
  3. Andreas Glockner & Arndt Broder, 2011. "Processing of recognition information and additional cues: A model-based analysis of choice, confidence, and response time," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(1), pages 23-42, February.
  4. Goldstein, Daniel G. & Gigerenzer, Gerd, 2009. "Fast and frugal forecasting," International Journal of Forecasting, Elsevier, vol. 25(4), pages 760-772, October.
  5. Edgar Erdfelder & Carolina E. Kupper-Tetzel & Sandra D. Mattern, 2011. "Threshold models of recognition and the recognition heuristic," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(1), pages 7-22, February.
  6. Michael Smithson, 2010. "When less is more in the recognition heuristic," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 5(4), pages 230-243, July.
  7. Clintin P. Davis-Stober & Jason Dana & David V. Budescu, 2010. "Why recognition is rational: Optimality results on single-variable decision rules," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 5(4), pages 216-229, July.
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Cited by:
  1. Julian N. Marewski & Rudiger F. Pohl & Oliver Vitouch, 2011. "Recognition-based judgments and decisions: What we have learned (so far)," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(5), pages 359-380, July.
  2. Anton Cheremukhin & Anna Popova & Antonella Tutino, 2011. "Experimental evidence on rational inattention," Working Papers 1112, Federal Reserve Bank of Dallas.
  3. Andreas Glockner & Arndt Broder, 2014. "Cognitive integration of recognition information and additional cues in memory-based decisions," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(1), pages 35-50, January.
  4. Julian N. Marewski & Katja Mehlhorn, 2011. "Using the ACT-R architecture to specify 39 quantitative process models of decision making," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(6), pages 439-519, August.

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