David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Mind and Society 5 (1):1-38 (2006)
This paper aims to make explicit the methodological conditions that should be satisfied for the Bayesian model to be used as a normative model of human probability judgment. After noticing the lack of a clear definition of Bayesianism in the psychological literature and the lack of justification for using it, a classic definition of subjective Bayesianism is recalled, based on the following three criteria: an epistemic criterion, a static coherence criterion and a dynamic coherence criterion. Then it is shown that the adoption of this framework has two kinds of implications. The first one regards the methodology of the experimental study of probability judgment. The Bayesian framework creates pragmatic constraints on the methodology that are linked to the interpretation of, and the belief in, the information presented, or referred to, by an experimenter in order for it to be the basis of a probability judgment by individual participants. It is shown that these constraints have not been satisfied in the past, and the question of whether they can be satisfied in principle is raised and answered negatively. The second kind of implications consists of two limitations in the scope of the Bayesian model. They regard (1) the background of revision (the Bayesian model considers only revising situations but not updating situations), and (2) the notorious case of the null priors. In both cases Lewisâ rule is an appropriate alternative to Bayesâ rule, but its use faces the same operational difficulties
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References found in this work BETA
Daniel Kahneman, Paul Slovic & Amos Tversky (eds.) (1982). Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press.
Judea Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
Gerd Gigerenzer (1999). Simple Heuristics That Make Us Smart. Oxford University Press.
Richard Jeffrey (1983). The Logic of Decision. University of Chicago Press.
Citations of this work BETA
David R. Mandel (2015). Instruction in Information Structuring Improves Bayesian Judgment in Intelligence Analysts. Frontiers in Psychology 6.
Eric D. Johnson & Elisabet Tubau (2015). Comprehension and Computation in Bayesian Problem Solving. Frontiers in Psychology 6.
Gorka Navarrete, Rut Correia, Miroslav Sirota, Marie Juanchich & David Huepe (2015). Doctor, What Does My Positive Test Mean? From Bayesian Textbook Tasks to Personalized Risk Communication. Frontiers in Psychology 6.
Jean Baratgin & Guy Politzer (2007). The Psychology of Dynamic Probability Judgment: Order Effect, Normative Theories, and Experimental Methodology. Mind and Society 6 (1):53-66.
Darya V. Filatova, Sacha Bourgeois-Gironde, Jean Baratgin, Frank Jamet & Jing Shao (forthcoming). Cycles of Maximin and Utilitarian Policies Under the Veil of Ignorance. Mind and Society.
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