Judgment under uncertainty: The robust beauty of improper linear models in decision making

By Robyn M. Dawes. 1979

Subfields: Statistics and Probability, Cognitive Science & Psychology

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Citations in the corpus

Learning Optimized Or's of And's (2015, methodology): “We present two optimization-based machine learning frameworks for constructing OA models, Optimized OA (OOA) and its faster version, Optimized OA with Approximations (OOAx).”
Risk and Rationality in Adolescent Decision Making (2006, background): “For example, Dawes and Corrigan (1974; Dawes, 1979) found that many competing models of decision-making processes were inherently indistinguishable because of their shared statistical properties.1 Additionally, the commonalities among laboratory and ‘‘real world’’ tasks argued to reflect risk…”
The recalcitrance of overconfidence and its contribution to decision aid neglect (2005, background): “As an example of how this might come about, Dawes (1979) has suggested that selective memory about the brilliance of one’s own unaided decisions might be a factor.”
Measuring Job-Related Situational Strength and Assessing Its Interactive Effects With Personality on Voluntary Work Behavior (2014, methodology): “Following the arguments of Wainer (1976) and Dawes (1979) regarding the effectiveness and elegance of linear, unit-weighted combinations, this global composite was based on a simple average (mean) of all 28 situational strength items.”
Measuring Team Knowledge (2000, methodology): “Task analysis methods, which analyze a task into its components (Kirwan & Ainsworth, 1992), and policy-capturing approaches, which quantify decision policies (Dawes, 1979), target very specific aspects of individual knowledge and are additional possibilities for measuring team knowledge.”
The robust beauty of majority rules in group decisions. (2005, unknown): “Five strategy alternatives were provided: equal cue weighting (Dawes, 1979); take-the-best strategy (Gigerenzer et al., 1999); weighted additive (regression) strategy; configural weighting (e.g., Mellers, 1980); or any other strategy described by the participant.”
Exploring the Strength of Association between the Components of Emotion Syndromes: The Case of Surprise (2000, background): “Dawes, 1979 ), particularly with errorful data, it underestimates the strength of association between variables in cases of pronounced nonlinearity.”
An Experimental Comparison of Different Approaches to Determining Weights in Additive Utility Models (1982, background): “Fourthly, it may be that linear models are more robust under misspecification than nonlinear ones [4].”