Skip to main content
Log in

Fast, frugal, and fit: Simple heuristics for paired comparison

  • Published:
Theory and Decision Aims and scope Submit manuscript

Abstract

This article provides an overview of recent results on lexicographic, linear, and Bayesian models for paired comparison from a cognitive psychology perspective. Within each class, we distinguish subclasses according to the computational complexity required for parameter setting. We identify the optimal model in each class, where optimality is defined with respect to performance when fitting known data. Although not optimal when fitting data, simple models can be astonishingly accurate when generalizing to new data. A simple heuristic belonging to the class of lexicographic models is Take The Best (Gigerenzer & Goldstein (1996) Psychol. Rev. 102: 684). It is more robust than other lexicographic strategies which use complex procedures to establish a cue hierarchy. In fact, it is robust due to its simplicity, not despite it. Similarly, Take The Best looks up only a fraction of the information that linear and Bayesian models require; yet it achieves performance comparable to that of models which integrate information. Due to its simplicity, frugality, and accuracy, Take The Best is a plausible candidate for a psychological model in the tradition of bounded rationality. We review empirical evidence showing the descriptive validity of fast and frugal heuristics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

REFERENCES

  • Ayton, P. and Önkal, D. (1997), Forecasting football fixtures: Confidence and judged proportion correct, Unpublished manuscript.

  • Berretty, P. M. (2001), Cue preference in a multidimensional categorizataion task, Manuscript submitted for publication.

  • Berretty, P. M., Todd, P. M. and Martignon, L. (1999), Using few cues to choose: Fast and frugal categorization, In G. Gigerenzer, P. M. Todd and the ABC Research Group, Simple Heuristics That Make Us Smart (pp. 235–254), New York: Oxford University Press.

    Google Scholar 

  • Borges, B., Goldstein, D. G., Ortmann, A. and Gigerenzer, G. (1999), Can ignorance beat the stock market?, in G. Gigerenzer, P. M. Todd and the ABC Research Group, Simple Heuristics That Make Us Smart (pp. 59–72), New York: Oxford University Press.

    Google Scholar 

  • Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1993), Classification and Regression Trees, New York: Chapman and Hall.

    Google Scholar 

  • Bröder, A. (2000), Assessing the empirical validity of the ‘Take The Best’ heuristic as a model of human probabilistic inference, Journal of Experimental Psychology: Learning, Memory, and Cognition 26: 1332–1346.

    Google Scholar 

  • Cooksey, R. W. (1996), Judgment Analysis: Theory, Methods, and Applications, San Diego, CA: Academic Press.

    Google Scholar 

  • Cooper, G. (1990), The computational complexity of probabilistic inferences. Artificial Intelligence 42: 393–405.

    Google Scholar 

  • Czerlinski, J., Gigerenzer, G. and Goldstein, D. G. (1999), How good are simple heuristics?, in G. Gigerenzer, P. M. Todd, and the ABC Research Group, Simple Heuristics That Make Us Smart (pp. 97–118), New York: Oxford University Press.

    Google Scholar 

  • Dawes, R. M. (1979), The robust beauty of improper linear models in decision making, American Psychologist 34: 571–582.

    Google Scholar 

  • Dawes, R. M. and Corrigan, B. (1974), Linear models in decision making, Psychological Bulletin 81: 95–106.

    Google Scholar 

  • Dhami, M. and Harris, C. (2001), Fast and frugal versus regression models of human judgement, Thinking and Reasoning 7: 5–27.

    Google Scholar 

  • Friedman, N. and Goldszmit, M. (1996), Learning Bayesian networks with local structure, in Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence (UAI) (pp. 252–262), San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  • Garey, M. R. and Johnson, D. S. (1979), Computers and Intractability: A Guide to the Theory of NP-Completeness, San Francisco, CA: W. H. Freeman.

    Google Scholar 

  • Gigerenzer, G. (1981), Messung und Modellbildung in der Psychologie, Munich: Ernst Reinhard Verlag.

    Google Scholar 

  • Gigerenzer, G., Czerlinski, J. and Martignon, L. (1999), How good are fast and frugal heuristics? in J. Shanteau, B. Mellers, and D. Schum (eds.), Decision Research from Bayesian Approaches to Normative Systems: Reflections on the Contributions of Ward Edwards. Norwell, MA: Kluwer Academic Publishers.

    Google Scholar 

  • Gigerenzer, G. and Goldstein, D. G. (1996), Reasoning the fast and frugal way: Models of bounded rationality, Psychological Review 103: 650–669.

    Google Scholar 

  • Gigerenzer, G. and Hoffrage, U. (1995), How to improve Bayesian reasoning without instruction: Frequency formats, Psychological Review 102: 684–704.

    Google Scholar 

  • Gigerenzer, G., Hoffrage, U. and Kleinbölting, H. (1991). Probabilistic mental models: A brunswikian theory of confidence, Psychological Review 98: 506–528.

    Google Scholar 

  • Gigerenzer, G. and Selten, R. (eds.) (2001), Bounded Rationality: The Adaptive Toolbox, Cambridge, MA: MIT Press.

    Google Scholar 

  • Gigerenzer, G., Todd, P. M. and the ABC Research Group (1999), Simple Heuristics That Make Us Smart, New York: Oxford University Press.

    Google Scholar 

  • Goldstein, D. G. and Gigerenzer, G. (1999), How ignorance makes us smart: The recognition heuristic, in G. Gigerenzer, P. M. Todd and the ABC Research Group, Simple Heuristics That Make Us Smart (pp. 37–58), New York: Oxford University Press.

    Google Scholar 

  • Hasher, L. and Zacks, R. T. (1984), Automatic processing of fundamental information: The case of frequency of occurrence, American Psychologist 39: 1372–1388.

    Google Scholar 

  • Hoffrage, U., Hertwig, R. and Gigerenzer, G. (2000), Hindsight bias: A by-product of knowledge updating?, Journal of Experimental Psychology: Learning, Memory, and Cognition 26: 566–581.

    Google Scholar 

  • Hoffrage, U., Lindsey, S., Hertwig, R. and Gigerenzer, G. (2000), Communicating statistical information, Science 290: 2261–2262.

    Google Scholar 

  • Holte, R. C. (1993), Very simple classification rules perform well on most commonly used datasets, Machine Learning 3 (11): 63–91.

    Google Scholar 

  • Kahneman, D., Slovic, P. and Tversky, A. (1982), Judgment under Uncertainty: Heuristics and Biases, New York: Cambridge University Press.

    Google Scholar 

  • Kass, R. and Raftery, A. (1995), Bayes Factors, Journal of the American Statistical Association 90: 430.

  • Krauss, S., Martignon, L. and Hoffrage U. (1999), Simplifying Bayesian inference: The general case, in L. Magnani, N. Nersessian and P. Thagard, (eds.), Model-Based Reasoning in Scientific Discovery (pp. 165–179), New York: Plenum Press.

    Google Scholar 

  • Kurz, E. and Martignon, L. (1999), Weighing, then summing: The triumph and tumbling of a modeling practice in psychology, in L. Magnani, N. Nersessian and P. Thagard, (eds.), Model-Based Reasoning in Scientific Discovery (pp. 26–31), Pavia: Cariplo.

    Google Scholar 

  • Lages, M., Hoffrage, U. and Gigerenzer, G. (1999), How heuristics produce intransitivity and how intransitivity can discriminate between heuristics, Manuscript submitted for publication.

  • Martignon, L. and Hoffrage, U. (1999), Why does one-reason decision making work? A case study in ecological rationality, in G. Gigerenzer, P. M. Todd, and the ABC Research Group, Simple Heuristics That Make Us Smart (pp. 119–140), New York: Oxford University Press.

    Google Scholar 

  • Martignon, L. and Krauss, S. (in press), Can l’homme éclairé be fast and frugal? Reconciling Bayesianism and bounded rationality, in S. Schneider and J. Shanteau (eds.), Emerging Perspectives on Decision Research, Oxford, UK: Oxford University Press.

  • Martignon, L. and Laskey, K. B. (1999), Bayesian benchmarks for fast and frugal heuristics, in G. Gigerenzer, P. M. Todd and the ABC Research Group, Simple Heuristics That Make Us Smart (pp. 169–188), New York: Oxford University Press.

    Google Scholar 

  • Martignon, L. and Schmitt, M. (1999), Simplicity and robustness of fast and frugal heuristics, Minds and machines 9: 565–593.

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

    Google Scholar 

  • Payne, J. W., Bettman, J. R. and Johnson, E. J. (1993), The Adaptive Decision Maker, New York: Cambridge University Press.

    Google Scholar 

  • Pearl, J. (1988), Probabilistic Reasoning in Intelligent Systems, San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

  • Rieskamp, J. and Hoffrage, U. (1999), When do people use simple heuristics, and how can we tell?, in G. Gigerenzer, P. M. Todd and the ABC Research Group, Simple Heuristics That Make Us Smart (pp. 141–167). New York: Oxford University Press.

    Google Scholar 

  • Rivest, R. J. (1987), Learning decision lists, Machine Learning 2: 229–246.

    Google Scholar 

  • Shannon, C. (1948), A mathematical theory of communication, Bell Systems Technical Journal 27: 379–423, 623–656.

    Google Scholar 

  • Slegers, D. W., Brake, G. L. and Doherty, M. E. (2000), Probabilistic mental models with continuous predictors, Organizational Behavior and Human Decision Processes 81: 98–114.

    Google Scholar 

  • Todd, P.M., Gigerenzer, G. and the ABC Research Group (2000), How can we open up the adaptive toolbox? (Reply to commentaries) Behavioral and Brain Sciences 23: 767–780.

    Google Scholar 

  • Tversky, A. and Kahneman, D. (1974), Judgment under uncertainty: Heuristics and biases, Science 185: 1124–1131.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Martignon, L., Hoffrage, U. Fast, frugal, and fit: Simple heuristics for paired comparison. Theory and Decision 52, 29–71 (2002). https://doi.org/10.1023/A:1015516217425

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1015516217425

Navigation