Methodological notes on model comparisons and strategy classification: A falsificationist proposition
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.
Volume (Year): 6 (2011)
Issue (Month): 8 (December)
|Contact details of provider:|| |
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- Kahneman, Daniel & Tversky, Amos, 1979.
"Prospect Theory: An Analysis of Decision under Risk,"
Econometric Society, vol. 47(2), pages 263-291, March.
- Amos Tversky & Daniel Kahneman, 1979. "Prospect Theory: An Analysis of Decision under Risk," Levine's Working Paper Archive 7656, David K. Levine.
- 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.
- 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. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:jdm:journl:v:6:y:2011:i:8:p:814-820. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jonathan Baron)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.