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)

Abstract

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.

Keywords

estimation simple heuristics general regression neural networks QuickEst ecological rationality 

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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

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