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Take The Best, Dawes' Rule, and Compensatory Decision Strategies: A Regression-based Classification Method

Abstract

Strategy descriptions like the ``Take The Best''-heuristic (G. Gigerenzer et al., 1991), the weighted additive rule, and the equal weight decision rule are competing theories about information integration in multi-attribute decision tasks. Behavioral decision research is confronted with the problem of drawing conclusions about unobservable decision strategies from behavioral data. Although there has been considerable progress due to methodical traditions like `Structural Modeling' and `Process Tracing', these paradigms have certain limitations in testing specific hypotheses about individual strategies. Some of these problems are summarized briefly. A deductive method for classifying individual response patterns is introduced. Predictions about regression coefficients are deduced from competing substantial hypotheses about strategies for decision making. These can be tested at the level of individual participants. The validity of this classification procedure is demonstrated with a Monte Carlo simulation. Some useful applications of the method are described, limitations of the method and potential generalizations are discussed.

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Bröder, A. Take The Best, Dawes' Rule, and Compensatory Decision Strategies: A Regression-based Classification Method. Quality & Quantity 36, 219–238 (2002). https://doi.org/10.1023/A:1016080517126

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  • DOI: https://doi.org/10.1023/A:1016080517126

  • decision making
  • take the best
  • probabilistic inference
  • hypothesis testing
  • strategy classification