Homo heuristicus Outnumbered: Comment on Gigerenzer and Brighton (2009)

Gigerenzer and Brighton (2009) have argued for a "Homo heuristicus" view of judgment and decision making, claiming that there is evidence for a majority of individuals using fast and frugal heuristics. In this vein, they criticize previous studies that tested the descriptive adequacy of some of these heuristics. In addition, they provide a reanalysis of experimental data on the recognition heuristic that allegedly supports Gigerenzer and Brighton's view of pervasive reliance on heuristics. However, their arguments and reanalyses are both conceptually and methodologically problematic. We provide counterarguments and a reanalysis of the data considered by Gigerenzer and Brighton. Results clearly replicate previous findings, which are at odds with the claim that simple heuristics provide a general description of inferences for a majority of decision makers.

[1]  B. Hilbig,et al.  Multinomial processing tree models: A review of the literature. , 2009 .

[2]  Arndt Bröder,et al.  Challenging some common beliefs: Empirical work within the adaptive toolbox metaphor , 2008, Judgment and Decision Making.

[3]  B. Hilbig One-reason decision making in risky choice? A closer look at the priority heuristic , 2008 .

[4]  Benjamin E Hilbig,et al.  Recognizing users of the recognition heuristic. , 2008, Experimental psychology.

[5]  Arndt Bröder,et al.  Criterion knowledge: A moderator of using the recognition heuristic? , 2009 .

[6]  Morten Moshagen,et al.  multiTree: A computer program for the analysis of multinomial processing tree models , 2010, Behavior research methods.

[7]  Editors-in-chief,et al.  Encyclopedia of statistics in behavioral science , 2005 .

[8]  I. J. Myung,et al.  The Importance of Complexity in Model Selection. , 2000, Journal of mathematical psychology.

[9]  Arndt Bröder,et al.  The use of recognition information and additional cues in inferences from memory. , 2006, Acta psychologica.

[10]  Gerd Gigerenzer,et al.  Homo Heuristicus: Why Biased Minds Make Better Inferences , 2009, Top. Cogn. Sci..

[11]  Edgar Erdfelder,et al.  One-reason decision making unveiled: a measurement model of the recognition heuristic. , 2010, Journal of experimental psychology. Learning, memory, and cognition.

[12]  Gerd Gigerenzer,et al.  Models of ecological rationality: the recognition heuristic. , 2002, Psychological review.

[13]  P. Todd,et al.  The Quest for Take The Best - Insights and Outlooks from Experimental Research , 2011 .

[14]  Ben R. Newell,et al.  On the binary quality of recognition and the inconsequentiality of further knowledge: two critical tests of the recognition heuristic , 2006 .

[15]  W. Batchelder,et al.  The statistical analysis of general processing tree models with the EM algorithm , 1994 .

[16]  Benjamin E. Hilbig,et al.  Precise models deserve precise measures: A methodological dissection , 2010, Judgment and Decision Making.

[17]  A. Glöckner,et al.  Multiple-reason decision making based on automatic processing. , 2008, Journal of experimental psychology. Learning, memory, and cognition.

[18]  Tobias Richter,et al.  Recognition is used as one cue among others in judgment and decision making. , 2006, Journal of experimental psychology. Learning, memory, and cognition.

[19]  Arndt Bröder,et al.  Bayesian strategy assessment in multi‐attribute decision making , 2003 .

[20]  J. Rieskamp The probabilistic nature of preferential choice. , 2008, Journal of experimental psychology. Learning, memory, and cognition.

[21]  Rüdiger F. Pohl,et al.  Empirical tests of the recognition heuristic , 2006 .

[22]  A. Glöckner,et al.  Beyond dual-process models: A categorisation of processes underlying intuitive judgement and decision making , 2010 .

[23]  B. Hilbig Individual differences in fast-and-frugal decision making : neuroticism and the recognition heuristic , 2008 .

[24]  Benjamin E. Hilbig,et al.  Ignorance- versus evidence-based decision making: a decision time analysis of the recognition heuristic. , 2009, Journal of experimental psychology. Learning, memory, and cognition.

[25]  A. Glöckner Investigating intuitive and deliberate processes statistically: The multiple-measure maximum likelihood strategy classification method , 2009, Judgment and Decision Making.

[26]  B. Newell,et al.  The Right Tool for the Job? Comparing an Evidence Accumulation and a Naive Strategy Selection Model of Decision Making , 2011 .

[27]  E. Wagenmakers A practical solution to the pervasive problems ofp values , 2007, Psychonomic bulletin & review.

[28]  Benjamin E. Hilbig,et al.  Reconsidering “evidence” for fast-and-frugal heuristics , 2010, Psychonomic bulletin & review.

[29]  Andreas Glöckner,et al.  Accounting for critical evidence while being precise and avoiding the strategy selection problem in a parallel constraint satisfaction approach – A reply to Marewski , 2010 .

[30]  Andreas Glöckner,et al.  Modeling Option and Strategy Choices with Connectionist Networks: Towards an Integrative Model of Automatic and Deliberate Decision Making , 2008, Judgment and Decision Making.