David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
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
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
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.
Similar books and articles
Michael Strevens (2004). Bayesian Confirmation Theory: Inductive Logic, or Mere Inductive Framework? Synthese 141 (3):365 - 379.
Piers Rawling (1999). Reasonable Doubt and the Presumption of Innocence: The Case of the Bayesian Juror. Topoi 18 (2):117-126.
Patrick Maher (2010). Bayesian Probability. Synthese 172 (1):119 - 127.
Jukka Corander & Pekka Marttinen (2006). Bayesian Model Learning Based on Predictive Entropy. Journal of Logic, Language and Information 15 (1-2):5-20.
Frederick Eberhardt & David Danks (2011). Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. [REVIEW] Minds and Machines 21 (3):389-410.
Kenny Easwaran (2011). Bayesianism I: Introduction and Arguments in Favor. Philosophy Compass 6 (5):312-320.
Jon Williamson (2011). Objective Bayesianism, Bayesian Conditionalisation and Voluntarism. Synthese 178 (1):67-85.
Jon Williamson (2008). Objective Bayesianism with Predicate Languages. Synthese 163 (3):341 - 356.
Added to index2010-08-10
Total downloads17 ( #93,950 of 1,096,702 )
Recent downloads (6 months)1 ( #271,187 of 1,096,702 )
How can I increase my downloads?