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On heuristic and linear models of judgment: Mapping the demand for knowledge

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Abstract

Research on judgment and decision making presents a confusing picture of human abilities. For example, much research has emphasized the dysfunctional aspects of judgmental heuristics, and yet, other findings suggest that these can be highly effective. A further line of research has modeled judgment as resulting from “as if” linear models. This paper illuminates the distinctions in these approaches by providing a common analytical framework based on the central theoretical premise that understanding human performance requires specifying how characteristics of the decision rules people use interact with the demands of the tasks they face. Our work synthesizes the analytical tools of “lens model” research with novel methodology developed to specify the effectiveness of heuristics in different environments and allows direct comparisons between the different approaches. We illustrate with both theoretical analyses and simulations. We further link our results to the empirical literature by a meta-analysis of lens model studies and estimate both human and heuristic performance in the same tasks. Our results highlight the trade-off between linear models and heuristics. Whereas the former are cognitively demanding, the latter are simple to use. However, they require knowledge – and thus “maps” – of when and which heuristic to employ.

Suggested Citation

  • Robin Hogarth & Natalia Karelaia, 2006. "On heuristic and linear models of judgment: Mapping the demand for knowledge," Economics Working Papers 974, Department of Economics and Business, Universitat Pompeu Fabra.
  • Handle: RePEc:upf:upfgen:974
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    References listed on IDEAS

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    1. Rothstein, Howard G., 1986. "The effects of time pressure on judgment in multiple cue probability learning," Organizational Behavior and Human Decision Processes, Elsevier, vol. 37(1), pages 83-92, February.
    2. Manel Baucells & Juan A. Carrasco & Robin M. Hogarth, 2008. "Cumulative Dominance and Heuristic Performance in Binary Multiattribute Choice," Operations Research, INFORMS, vol. 56(5), pages 1289-1304, October.
    3. Ashton, Rh, 1981. "A Descriptive Study Of Information Evaluation," Journal of Accounting Research, Wiley Blackwell, vol. 19(1), pages 42-61.
    4. Kessler, L & Ashton, Rh, 1981. "Feedback And Prediction Achievement In Financial Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 19(1), pages 146-162.
    5. Robin M. Hogarth & Natalia Karelaia, 2005. "Simple Models for Multiattribute Choice with Many Alternatives: When It Does and Does Not Pay to Face Trade-offs with Binary Attributes," Management Science, INFORMS, vol. 51(12), pages 1860-1872, December.
    6. Robin Hogarth & Natalia Karelaia, 2004. "Ignoring information in binary choice with continuous variables: When is less 'more'?," Economics Working Papers 742, Department of Economics and Business, Universitat Pompeu Fabra, revised Oct 2004.
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    Cited by:

    1. David Leiser & Dov-Ron Schatzberg, 2008. "On the complexity of traffic judges' decisions," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 3(8), pages 667-678, December.

    More about this item

    Keywords

    Decision making; heuristics; linear models; lens model; judgmental biases;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General

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