Testimony as Evidence: More Problems for Linear Pooling [Book Review]
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Journal of Philosophical Logic 41 (6):983-999 (2012)
This paper considers a special case of belief updating—when an agent learns testimonial data, or in other words, the beliefs of others on some issue. The interest in this case is twofold: (1) the linear averaging method for updating on testimony is somewhat popular in epistemology circles, and it is important to assess its normative acceptability, and (2) this facilitates a more general investigation of what it means/requires for an updating method to have a suitable Bayesian representation (taken here as the normative standard). The paper initially defends linear averaging against Bayesian-compatibility concerns raised by Bradley (Soc Choice Welf 29:609-632, 2007), as well as problems associated with multiple testimony updates. The resolution of these issues, however, requires an extremely nuanced interpretation of the parameters of the linear averaging model—the so-called weights of respect. We go on to propose a role that the parameters of any 'shortcut' updating function should play, by way of minimal interpretation of these parameters. The class of updating functions that is consistent with this role, however, excludes linear averaging, at least in its standard form
|Keywords||Testimony Linear pooling Bayesian belief change|
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References found in this work BETA
Richard Bradley (2007). Reaching a Consensus. Social Choice and Welfare 29:609-632.
David Christensen (2007). Epistemology of Disagreement: The Good News. Philosophical Review 116 (2):187-217.
Persi Diaconis & Sandy L. Zabell (1982). Updating Subjective Probability. Journal of the American Statistical Association 77 (380):822-830.
Adam Elga (2007). Reflection and Disagreement. Noûs 41 (3):478–502.
Hartry Field (1978). A Note on Jeffrey Conditionalization. Philosophy of Science 45 (3):361-367.
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