The rationality of different kinds of intuitive decision processes

Abstract

Whereas classic work in judgment and decision making has focused on the deviation of intuition from rationality, more recent research has focused on the performance of intuition in real-world environments. Borrowing from both approaches, we investigate to which extent competing models of intuitive probabilistic decision making overlap with choices according to the axioms of probability theory and how accurate those models can be expected to perform in real-world environments. Specifically, we assessed to which extent heuristics, models implementing weighted additive information integration (WADD), and the parallel constraint satisfaction (PCS) network model approximate the Bayesian solution and how often they lead to correct decisions in a probabilistic decision task. PCS and WADD outperform simple heuristics on both criteria with an approximation of 88.8 % and a performance of 73.7 %. Results are discussed in the light of selection of intuitive processes by reinforcement learning.

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References

  1. Bargh J. A., Chartrand T. L. (1999) The unbearable automaticity of being. American Psychologist 54: 462–479

    Article  Google Scholar 

  2. Beach L. R., Mitchell T. R. (1978) A contingency model for the selection of decision strategies. Academy of Management Review 3: 439–449

    Google Scholar 

  3. Betsch T., Glöckner A. (2010) Intuition in judgment and decision making: Extensive thinking without effort. Psychological Inquiry 21: 279–294

    Article  Google 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: 432–459

    Article  Google Scholar 

  5. Claxton G. (1998) Knowing without knowing why. The Psychologist 11: 217–220

    Google Scholar 

  6. Cohen L. J. (1981) Can human irrationality be experimentally demonstrated?. The Behavioral and Brian Sciences 4: 317–370

    Article  Google Scholar 

  7. Czerlinski J., Gigerenzer G., Goldstein D.G. (1999) How good are simple heuristics?. In: Gigerenzer G., Todd P.M., The ABC Research Group (eds) Simple heuristics that make us smart. Oxford University Press, New York, pp 97–118

    Google Scholar 

  8. Dougherty M. R. P., Gettys C. F., Ogden E. E. (1999) MINERVA-DM: A memory processes model for judgments of likelihood. Psychological Review 106: 180–209

    Article  Google Scholar 

  9. Gigerenzer G. (2007) Gut feelings: The intelligence of the unconscious. Penguin Books, London

    Google Scholar 

  10. Gigerenzer G., Brighton H. (2009) Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Sciences 1: 107–143

    Article  Google Scholar 

  11. Gigerenzer G., Todd P.M., The ABC Research Group (1999) Simple heuristics that make us smart. Oxford University Press, New York

    Google Scholar 

  12. Gilovich T., Griffin D., Kahneman D. (2002) Heuristics and biases: The psychology of intuitive judgment. Cambridge University Press, New York

    Google Scholar 

  13. Glöckner A., Betsch T. (2008a) Modeling option and strategy choices with connectionist networks: Towards an integrative model of automatic and deliberate decision making. Judgment and Decision Making 3: 215–228

    Google Scholar 

  14. Glöckner A., Betsch T. (2008b) Multiple-reason decision making based on automatic processing. Journal of Experimental Psychology: Learning, Memory, and Cognition 34: 1055–1075

    Article  Google Scholar 

  15. Glöckner A., Betsch T. (2012) Decisions beyond boundaries: When more information is processed faster than less. Acta Psychologica 139: 532–542

    Article  Google Scholar 

  16. Glöckner A., Betsch T., Schindler N. (2010) Coherence shifts in probabilistic inference tasks. Journal of Behavioral Decision Making 23: 439–462

    Article  Google Scholar 

  17. Glöckner A., Bröder A. (2011) Processing of recognition information and additional cues: A model-based analysis of choice, confidence, and response time. Judgment and Decision Making 6: 23–42

    Google Scholar 

  18. Glöckner A., Witteman C. L. M. (2010) Beyond dual-process models: A categorization of processes underlying intuitive judgment and decision making. Thinking & Reasoning 16: 1–25

    Article  Google Scholar 

  19. Hammond K. R., Hamm R. M., Grassia J., Pearson T. (1987) Direct comparison of the efficacy of intuitive and analytical cognition in expert judgment. IEEE Transactions on Systems, Man, and Cybernetics 17: 753–770

    Google Scholar 

  20. Hilbig B. E., Richter T. (2011) Homo heuristicus outnumbered: Comment on Gigerenzer and Brighton (2009). Topics in Cognitive Science 3: 187–196

    Article  Google Scholar 

  21. Hochman G., Ayal S., Glöckner A. (2010) Physiological arousal in processing recognition information: Ignoring or integrating cognitive cues?.  Judgment and Decision Making 5: 285–299

    Google Scholar 

  22. Hogarth R. M. (2001) Educating intuition. University of Chicago Press, Chicago

    Google Scholar 

  23. Hogarth R.M. (2005) Deciding analytically or trusting your intuition? The advantages and disadvantages of analytic and intuitive thought. In: Betsch T., Haberstroh S. (eds) The routines of decision making. Erlbaum, Mahway, NJ, pp 67–82

    Google Scholar 

  24. Hogarth R. M., Karelaia N. (2007) Heuristic and linear models of judgment: Matching rules and environments. Psychological Review 114: 733–758

    Article  Google Scholar 

  25. Holyoak K. J., Simon D. (1999) Bidirectional reasoning in decision making by constraint satisfaction. Journal of Experimental Psychology: General 128: 3–31

    Article  Google Scholar 

  26. Juslin P., Nilsson H., Winman A. (2009) Probability theory, not the very guide of life. Psychological Review 116: 856–874

    Article  Google Scholar 

  27. Kahneman D., Frederick S. (2002) Representativeness revisited: Attribute substitution in intuitive judgment. In: Gilovich T., Griffin D., Kahneman D. (eds) Heuristics and biases: The psychology of intuitive judgment. Cambridge University Press, New York, pp 49–81

    Google Scholar 

  28. Lee M. D., Cummins T. D. R. (2004) Evidence accumulation in decision making: Unifying the “take the best” and the “rational”models. Psychonomic Bulletin and Review 11: 343–352

    Article  Google Scholar 

  29. Lopes L. (1991) The rhetoric of irrationality. Theory and Psychology 1: 65–82

    Article  Google Scholar 

  30. Payne J. W., Bettman J. R., Johnson E. J. (1988) Adaptive strategy selection in decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition 14: 534–552

    Article  Google Scholar 

  31. Roe R., Busemeyer J. R., Townsend J. (2001) Multiattribute decision field theory: A dynamic, connectionist model of decision making. Psychological Review 108: 370–392

    Article  Google Scholar 

  32. Thagard P., Millgram E. (1995) Inference to the best plan: A coherence theory of decision. In: Ram A., Leake D. B. (eds) Goal-driven learning. MIT Press, Cambridge MA, pp 439–454

    Google Scholar 

  33. Usher M., McClelland J. L. (2001) The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review 108: 550–592

    Article  Google Scholar 

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Correspondence to Marc Jekel.

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Jekel, M., Glöckner, A., Fiedler, S. et al. The rationality of different kinds of intuitive decision processes. Synthese 189, 147–160 (2012). https://doi.org/10.1007/s11229-012-0126-7

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Keywords

  • Rationality
  • Intuitive decision processes
  • Model comparison