Robust decision making in a nonlinear world.

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  • Author(s): Dougherty MR;Dougherty MR; Thomas RP
  • Source:
    Psychological review [Psychol Rev] 2012 Apr; Vol. 119 (2), pp. 321-44. Date of Electronic Publication: 2012 Feb 13.
  • Publication Type:
    Evaluation Study; Journal Article; Research Support, U.S. Gov't, Non-P.H.S.
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: American Psychological Association Country of Publication: United States NLM ID: 0376476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1939-1471 (Electronic) Linking ISSN: 0033295X NLM ISO Abbreviation: Psychol Rev Subsets: MEDLINE
    • Publication Information:
      Original Publication: Washington, DC : American Psychological Association
    • Subject Terms:
      Decision Making* ; Models, Psychological* ; Models, Statistical*; Social Sciences/*statistics & numerical data; Algorithms ; Bayes Theorem ; Choice Behavior ; Data Interpretation, Statistical ; Humans ; Judgment ; Least-Squares Analysis ; Psychological Theory
    • Abstract:
      The authors propose a general modeling framework called the general monotone model (GeMM), which allows one to model psychological phenomena that manifest as nonlinear relations in behavior data without the need for making (overly) precise assumptions about functional form. Using both simulated and real data, the authors illustrate that GeMM performs as well as or better than standard statistical approaches (including ordinary least squares, robust, and Bayesian regression) in terms of power and predictive accuracy when the functional relations are strictly linear but outperforms these approaches under conditions in which the functional relations are monotone but nonlinear. Finally, the authors recast their framework within the context of contemporary models of behavioral decision making, including the lens model and the take-the-best heuristic, and use GeMM to highlight several important issues within the judgment and decision-making literature.
    • Publication Date:
      Date Created: 20120215 Date Completed: 20120725 Latest Revision: 20191210
    • Publication Date:
      20221213
    • Accession Number:
      10.1037/a0027039
    • Accession Number:
      22329684