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Methodological notes on model comparisons and strategy classification: A falsificationist proposition

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  • Morten Moshagen
  • Benjamin E. Hilbig
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    Abstract

    Taking a falsificationist perspective, the present paper identifies two major shortcomings of existing approaches to comparative model evaluations in general and strategy classifications in particular. These are (1) failure to consider systematic error and (2) neglect of global model fit. Using adherence measures to evaluate competing models implicitly makes the unrealistic assumption that the error associated with the model predictions is entirely random. By means of simple schematic examples, we show that failure to discriminate between systematic and random error seriously undermines this approach to model evaluation. Second, approaches that treat random versus systematic error appropriately usually rely on relative model fit to infer which model or strategy most likely generated the data. However, the model comparatively yielding the best fit may still be invalid. We demonstrate that taking for granted the vital requirement that a model by itself should adequately describe the data can easily lead to flawed conclusions. Thus, prior to considering the relative discrepancy of competing models, it is necessary to assess their absolute fit and thus, again, attempt falsification. Finally, the scientific value of model fit is discussed from a broader perspective.

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    Bibliographic Info

    Article provided by Society for Judgment and Decision Making in its journal Judgment and Decision Making.

    Volume (Year): 6 (2011)
    Issue (Month): 8 (December)
    Pages: 814-820

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    Handle: RePEc:jdm:journl:v:6:y:2011:i:8:p:814-820

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    Related research

    Keywords: falsification; error; model testing; model fit.;

    References

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    1. Kahneman, Daniel & Tversky, Amos, 1979. "Prospect Theory: An Analysis of Decision under Risk," Econometrica, Econometric Society, vol. 47(2), pages 263-91, March.
    2. Benjamin E. Hilbig, 2008. "One-reason decision making in risky choice? A closer look at the priority heuristic," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 3(6), pages 457-462, August.
    3. Andreas Glöckner & Tilmann Betsch, 2008. "Multiple-Reason Decision Making Based on Automatic Processing," Working Paper Series of the Max Planck Institute for Research on Collective Goods 2008_12, Max Planck Institute for Research on Collective Goods.
    4. Benjamin E. Hilbig, 2010. "Precise models deserve precise measures: A methodological dissection," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 5(4), pages 272-284, July.
    5. Klaus Fiedler, 2010. "How to study cognitive decision algorithms: The case of the priority heuristic," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 5(1), pages 21-32, February.
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    Cited by:
    1. Andreas Glockner & Arndt Broder, 2014. "Cognitive integration of recognition information and additional cues in memory-based decisions," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(1), pages 35-50, January.
    2. Benjamin E. Hilbig, 2014. "On the role of recognition in consumer choice: A model comparison," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(1), pages 51-57, January.
    3. Marc Jekel & Andreas Glockner & Arndt Broder & Viktoriya Maydych, 2014. "Approximating rationality under incomplete information: Adaptive inferences for missing cue values based on cue-discrimination," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(2), pages 129-147, March.

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