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The Empirical content of theories in judgment and decision making: Shortcomings and remedies

  • Andreas Glockner
  • Tilmann Betsch
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    According to Karl Popper, we can tell good theories from poor ones by assessing their empirical content (empirischer Gehalt), which basically reflects how much information they convey concerning the world. ``The empirical content of a statement increases with its degree of falsifiability: the more a statement forbids, the more it says about the world of experience.'' Two criteria to evaluate the empirical content of a theory are their level of universality (Allgemeinheit) and their degree of precision (Bestimmtheit). The former specifies how many situations it can be applied to. The latter refers to the specificity in prediction, that is, how many subclasses of realizations it allows. We conduct an analysis of the empirical content of theories in Judgment and Decision Making (JDM) and identify the challenges in theory formulation for different classes of models. Elaborating on classic Popperian ideas, we suggest some guidelines for publication of theoretical work.

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    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: 711-721

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    Handle: RePEc:jdm:journl:v:6:y:2011:i:8:p:711-721
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    1. 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.
    2. Natalia Karelaia & Robin Hogarth, 2007. "Determinants of linear judgment: A meta-analysis of lens model studies," Economics Working Papers 1007, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Charles A. Holt & Susan K. Laury, 2002. "Risk Aversion and Incentive Effects," American Economic Review, American Economic Association, vol. 92(5), pages 1644-1655, December.
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    5. 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.
    6. Andreas Glöckner & Tilmann Betsch, 2008. "Modeling Option and Strategy Choices with Connectionist Networks: Towards an Integrative Model of Automatic and Deliberate Decision Making," Working Paper Series of the Max Planck Institute for Research on Collective Goods 2008_02, Max Planck Institute for Research on Collective Goods.
    7. Tracy Tomlinson & Julian N. Marewski & Michael Dougherty, 2011. "Four challenges for cognitive research on the recognition heuristic and a call for a research strategy shift," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(1), pages 89-99, February.
    8. 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.
    9. Tversky, Amos & Kahneman, Daniel, 1992. " Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    10. Fiedler, Klaus & Freytag, Peter & Meiser, Thorsten, 2009. "Pseudocontingencies: An Integrative Account of an Intriguing Cognitive Illusion," Sonderforschungsbereich 504 Publications 08-36, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    11. Slovic, Paul & Finucane, Melissa & Peters, Ellen & MacGregor, Donald G., 2002. "Rational actors or rational fools: implications of the affect heuristic for behavioral economics," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 31(4), pages 329-342.
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