Theory and Decision

, Volume 66, Issue 2, pp 149–179 | Cite as

The Collapsing Choice Theory: Dissociating Choice and Judgment in Decision Making

  • Jeffrey M. StibelEmail author
  • Itiel E. Dror
  • Talia Ben-Zeev


Decision making theory in general, and mental models in particular, associate judgment and choice. Decision choice follows probability estimates and errors in choice derive mainly from errors in judgment. In the studies reported here we use the Monty Hall dilemma to illustrate that judgment and choice do not always go together, and that such a dissociation can lead to better decision-making. Specifically, we demonstrate that in certain decision problems, exceeding working memory limitations can actually improve decision choice. We show across four experiments that increasing the number of choice alternatives forces people to collapse choices together, resulting in better decision-making. While choice performance improves, probability judgments do not change, thus demonstrating an important dissociation between choice and probability judgments. We propose the Collapsing Choice Theory (CCT) which explains how working memory capacity, probability estimation, choice alternatives, judgment, and regret all interact and effect decision quality.


choice judgment working memory mental models decision making monty hall dilemma 

JEL Classifications

D70 D80 D81 D84 


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  1. Ariely D. (2008) Predictably Irrational: The Hidden Forces that Shape our Decisions. HarperCollins, New YorkGoogle Scholar
  2. Ariely D., Zakay D. (2001) A timely account of the role of duration in decision-making. Acta Psychologica 108(2): 187–207CrossRefGoogle Scholar
  3. Biggs S., Bedard J., Gaber B., Linsmeier T. (1985) The effect of task size and similarity on the decision behavior of bank loan officers. Management Science 31, 970–987CrossRefGoogle Scholar
  4. Busemeyer J.R., Townsend J.T. (1993) Decision field theory: A dynamic-cognitive approach to decision-making in an uncertain environment. Psychological Review 100, 432–459CrossRefGoogle Scholar
  5. Cosmides L., Tooby J. (1996) Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty. Cognition 58, 1–73CrossRefGoogle Scholar
  6. Craik K. (1943) The Nature of Explanation. Cambridge University Press, CambridgeGoogle Scholar
  7. Dror I.E. (2007) Perception of risk and the decision to use force. Policing 1, 265–272CrossRefGoogle Scholar
  8. Dror I.E., Busemeyer J.R., Basola B. (1999) Decision making under time pressure: An independent test of sequential sampling models. Memory & Cognition 27, 713–725Google Scholar
  9. Dror I.E., Charlton D. (2006). Why experts make errors. Journal of Forensic Identification 56(4): 600–616Google Scholar
  10. Dror, I.E. and Rosenthal, R. (2008) Meta-analytically quantifying the reliability and biasability of forensic experts, Journal of Forensic Sciences 53.Google Scholar
  11. Evans, J.St. B.T. (1989) Bias in Human Reasoning: Causes and Consequences, Earlbaum, Hillsdale, NJ, pp. 111.Google Scholar
  12. Evans J.St.B.T., Over D.E. (1996). Reasoning and Rationality. Psychology Press, Hove, UKGoogle Scholar
  13. Falk R. (1992) A closer look at the probabilities of the notorious three prisoners. Cognition 43, 197–223CrossRefGoogle Scholar
  14. Fox C.R., Rottenstreich Y. (2003) Partition priming in judgment under uncertainty. Psychological Science 14, 195–200CrossRefGoogle Scholar
  15. Gigerenzer G. (2004). The irrationality paradox. Behavioral and Brain Sciences 27, 336–338CrossRefGoogle Scholar
  16. Gigerenzer G., Goldstein D.G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review 103, 650–669CrossRefGoogle Scholar
  17. Gigerenzer, G., Todd, P.M., and the ABC Research Group (1999) Simple Heuristics That Make us Smart, Oxford University Press, New York.Google Scholar
  18. Gilovich T., Medvec V.H., Chen S. (1995) Commission, omission, and dissonance reduction: Coping with regret in the Monty Hall problem. Personality and Social Psychology Bulletin 21, 182–190CrossRefGoogle Scholar
  19. Hadjichristidis, C., Stibel, J.M., Sloman, S.A., Over, D.E. and Stevenson, R.J. (1999) Opening Pandora’s Box: Selective Unpacking and Superadditivity, ECCS, 265–270.Google Scholar
  20. Hoelzl E., Loewenstein G. (2005) Wearing out your shoes to prevent someone else from stepping into them: Social takeover and anticipated regret in sequential decisions. Organizational Behavior and Human Decision Processes 98, 15–27CrossRefGoogle Scholar
  21. Hogarth R.M., Karelaia N. (2007) Heuristics and linear models of judgment: Matching rules and environments. Psychological Review 114(3): 733–758CrossRefGoogle Scholar
  22. Johnson-Laird P.N. (1983) Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Harvard University Press, Cambridge MAGoogle Scholar
  23. Johnson-Laird, P.N. and Byrne, R. (2000) Published online, May 2000, Mental Models,
  24. Johnson-Laird P.N., Legrenzi P., Girotto V., Legrenzi M.S. (1999). Probability: A mental model of extensional reasoning, Psychological Review 106, 62–88Google Scholar
  25. Kahneman D., Tversky A. (1973). On the psychology of prediction. Psychological Review 80, 237–251CrossRefGoogle Scholar
  26. Lichtenstein S., Slovic P. (1971). Reversal of preferences between bids and choices in gambling decisions. Journal of Experimental Psychology 89, 46–55CrossRefGoogle Scholar
  27. Lichtenstein S., Slovic P. (1973) Reversal of preferences between bids and choices in gambling decisions: An extended replication in Las Vegas. Journal of Experimental Psychology 101, 16–20CrossRefGoogle Scholar
  28. Link S.W. (1992) The Wave Theory of Difference Similarity. Erlbaum, Hillsdale, NJGoogle Scholar
  29. Miller G.A. (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review 63, 81–97CrossRefGoogle Scholar
  30. Moyer R.S., Landauer T.K. (1967) Times required for judgments of numerical inequality. Nature 215, 1519–1520CrossRefGoogle Scholar
  31. Nosofsky R.M., Palmeri T.J. (1997) An exemplar based random walk model of speeded classification. Psychological Review 104, 266–300CrossRefGoogle Scholar
  32. Ratcliff R. (1978) A theory of memory retrieval. Psychological Review 85, 59–108CrossRefGoogle Scholar
  33. Shafer G. (1990) The Bayesian and belief-function formalisms: A general perspective for auditing. Auditing: A Journal of Practice & Theory 9, 110–137Google Scholar
  34. Shields M.D. (1983) Some effects of information load on search patterns used to analyse performance reports. Accounting, Organizations, and Society 5, 429–442CrossRefGoogle Scholar
  35. Shimojo S., Ichikawa S. (1989) Intuitive reasoning about probability: Theoretical and experimental analyses of the problem of three prisoners. Cognition 32, 1–24CrossRefGoogle Scholar
  36. Sloman S.A., Over D., Slovak L., Stibel J.M. (2003) Frequency illusions and other fallacies. Organizational Behavior and Human Decision Processes 91, 296–309CrossRefGoogle Scholar
  37. Stanovich K.E., West R.F. (2000) Individual differences n reasoning: Implications for the rationality debate?. Behavioral & Brain Sciences 23, 645–726CrossRefGoogle Scholar
  38. Stibel J.M. (2005a) Mental models and online consumer behavior. Behavior & Information Technology 24, 147–150CrossRefGoogle Scholar
  39. Stibel J.M. (2005b) Increasing productivity through framing effects for interactive consumer choice. Cognition, Technology & Work 7, 63–68CrossRefGoogle Scholar
  40. Stibel J.M. (2006a) The role of explanation in categorization decisions. International Journal of Psychology 41, 132–144CrossRefGoogle Scholar
  41. Stibel J.M. (2006b) Categorization and Technology Innovation. Distributed Cognition: Special Issue of Pragmatics & Cognition 14, 343–355Google Scholar
  42. Stibel J.M. (2007) Discounting do’s and don’ts. MIT Sloan Management Review 49, 8–9Google Scholar
  43. Tetlock P.E., Boettger R. (1994) Accountability amplifies the status quo effect when change creates victims. Journal of Behavioral Decision Making 7, 1–23CrossRefGoogle Scholar
  44. Tversky A., Kahneman D. (1983) Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological Review 90, 293–315CrossRefGoogle Scholar
  45. Tversky A., Koehler D.J. (1994) Support theory: A nonextensional representation of subjective probability. Psychological Review 101, 547–567CrossRefGoogle Scholar
  46. vos Savant, M. (1990a, September 9), Ask Marilyn, Parade, p. 15.Google Scholar
  47. vos Savant, M. (1990b, December 2), Ask Marilyn, Parade, p. 25.Google Scholar
  48. Wittgenstein L. (1922) Tractatus Logico-Philosophicus. Routledge & Kegan Paul, LondonGoogle Scholar
  49. Wright C., Ayton P. (2005) Focusing on what might happen and how it could feel: Can the anticipation of regret change students’ computing-related choices?. International Journal of Human-Computer Studies 62, 759–783CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2008

Authors and Affiliations

  • Jeffrey M. Stibel
    • 1
    Email author
  • Itiel E. Dror
    • 2
  • Talia Ben-Zeev
    • 3
  1., Inc.Ponte Vedra BeachUSA
  2. 2.School of PsychologyUniversity of SouthamptonSouthamptonUK
  3. 3.Department of PsychologySan-Francisco State UniversitySan FranciscoUSA

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