Estimating Quantities: Comparing Simple Heuristics and Machine Learning Algorithms

  • Jan K. Woike
  • Ulrich Hoffrage
  • Ralph Hertwig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)


Estimating quantities is an important everyday task. We analyzed the performance of various estimation strategies in ninety-nine real-world environments drawn from various domains. In an extensive simulation study, we compared two classes of strategies: one included machine learning algorithms such as general regression neural networks and classification and regression trees, the other two psychologically plausible and computationally much simpler heuristics (QEst and Zig-QEst). We report the strategies’ ability to generalize from training sets to new data and explore the ecological rationality of their use; that is, how well they perform as a function of the statistical structure of the environment. While the machine learning algorithms outperform the heuristics when fitting data, Zig-QEst is competitive when making predictions out-of-sample.


estimation simple heuristics general regression neural networks QuickEst ecological rationality 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gigerenzer, G., Todd, P.M., ABC Research Group: Simple heuristics that make us smart. Oxford UP, New York (1999)Google Scholar
  2. 2.
    Gigerenzer, G., Selten, R. (eds.): The adaptive toolbox. MIT Press, Cambridge (2001)Google Scholar
  3. 3.
    Todd, P.M., Gigerenzer, G.: and the ABC Research Group, Ecological rationality: Intelligence in the world. Oxford UP, New York (2012)Google Scholar
  4. 4.
    Hertwig, R., Hoffrage, U., Martignon, L.: Quick estimation: Letting the environment do some of the work. In: Gigerenzer, G., Todd, P.M., The ABC Research Group (eds.) Simple Heuristics that Make Us Smart, pp. 209–234. Oxford UP, New York (1999)Google Scholar
  5. 5.
    Hertwig, R., Hoffrage, U., Sparr, R.: The QuickEst heuristic: How to benefit from an imbalanced world. In: Todd, P.M., Gigerenzer, G., The ABC Research Group (eds.) Ecological Rationality: Intelligence in the World, pp. 379–406. Oxford UP, New York (2012)Google Scholar
  6. 6.
    Specht, D.E.: A general regression neural network. IEEE Transactions on Neural Networks 2(6), 568–576 (1991)CrossRefGoogle Scholar
  7. 7.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth, Monterey (1984)zbMATHGoogle Scholar
  8. 8.
    Dawes, R.M.: The robust beauty of improper linear models in decision making. American Psychologist 34(7), 571–582 (1979)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2007), Google Scholar
  10. 10.
    Statlib on-line data base,
  11. 11.
    DASL - Data and Story Library,
  12. 12.
    OzDASL - Australasian Data and Story Library,
  13. 13.
    Journal of Statistics Education Data Archive,
  14. 14.
  15. 15.
  16. 16.
    Inter-University Consortium for Political and Social Research (ICPSR),
  17. 17.
    National Institute for Occupational Safety and Health (NIOSH) Mining Division,
  18. 18.
    UCLA Statistics Data Sets,
  19. 19.
    Weisberg, S.: Applied linear regression. John Wiley and Sons, New York (1985)zbMATHGoogle Scholar
  20. 20.
    Hettich, S., Bay, S.D.: The UCI KDD Archive. University of California, Department of Information and Computer Science, Irvine (1999), Google Scholar
  21. 21.
    Czerlinski, J., Gigerenzer, G., Goldstein, D.G.: How good are simple heuristics. In: Gigerenzer, G., Todd, P.M., The ABC Reseach Group (eds.) Simple Heuristics that Make Us Smart, pp. 97–118. Oxford UP, New York (1999)Google Scholar
  22. 22.
    Martignon, L., Katsikopoulos, K.V., Woike, J.K.: Categorization with limited resources: A family of simple heuristics. Journal of Mathematical Psychology 52(6), 352–361 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Todd, P.M., Gigerenzer, G.: Environments that make us smart: Ecological rationality. Current Directions in Psychological Science 16(3), 167–171 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jan K. Woike
    • 1
    • 2
  • Ulrich Hoffrage
    • 1
  • Ralph Hertwig
    • 2
  1. 1.Faculty of Business and EconomicsUniversity of LausanneSwitzerland
  2. 2.Faculty of PsychologyUniversity of BaselSwitzerland

Personalised recommendations