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Support for Geometric Pooling

Baccelli, Jean and Stewart, Rush T. (2020) Support for Geometric Pooling. The Review of Symbolic Logic.

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Abstract

Supra-Bayesianism is the Bayesian response to learning the opinions of others. Probability pooling constitutes an alternative response. One natural question is whether there are cases where probability pooling gives the supra-Bayesian result. This has been called the problem of Bayes-compatibility for pooling functions. It is known that in a common prior setting, under standard assumptions, linear pooling cannot be non-trivially Bayes-compatible. We show by contrast that geometric pooling can be non-trivially Bayes-compatible. Indeed, we show that, under certain assumptions, geometric and Bayes-compatible pooling are equivalent. Granting supra-Bayesianism its usual normative status, one upshot of our study is thus that, in a certain class of epistemic contexts, geometric pooling enjoys a normative advantage over linear pooling as a social learning mechanism. We discuss the philosophical rami�cations of this advantage, which we show to be robust to variations in our statement of the Bayes-compatibility problem.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Baccelli, Jeanjean.baccelli@gmail.com0000-0003-0275-0377
Stewart, Rush T.rush.stewart@lrz.uni-muenchen.de0000-0003-2777-5263
Keywords: supra-Bayesianism; linear pooling; geometric pooling; Bayes-compatibility; common prior; total evidence; deference; synergy
Subjects: General Issues > Decision Theory
Specific Sciences > Economics
General Issues > Evidence
General Issues > Formal Learning Theory
General Issues > Game Theory
Specific Sciences > Probability/Statistics
General Issues > Social Epistemology of Science
Depositing User: Jean Baccelli
Date Deposited: 16 Sep 2021 18:40
Last Modified: 16 Sep 2021 18:40
Item ID: 19567
Journal or Publication Title: The Review of Symbolic Logic
DOI or Unique Handle: 10.1017/S1755020320000416
Subjects: General Issues > Decision Theory
Specific Sciences > Economics
General Issues > Evidence
General Issues > Formal Learning Theory
General Issues > Game Theory
Specific Sciences > Probability/Statistics
General Issues > Social Epistemology of Science
Date: 2020
URI: http://philsci-archive.pitt.edu/id/eprint/19567

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