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Why Average When You Can Stack? Better Methods for Generating Accurate Group Credences.

Formal and social epistemologists have devoted significant attention to the question of how to aggregate the credences of a group of agents who disagree about the probabilities of events. Moss (2011) and Pettigrew (2019) argue that group credences can be a linear mean of the credences of each indivi...

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Published in:Philosophy of Science Vol. 89; no. 4; pp. 845 - 864
Main Author: Kinney, David
Format: Article
Published: Cambridge University Press Oct2022
Subjects:
Generating functions
Machine learning
Statistical models
Online Access:View this record in EBSCOhost
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        atl: Why Average When You Can Stack? Better Methods for Generating Accurate Group Credences.
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        au: Kinney, David
        affil: Princeton University, 222 Peretsman Scully Hall, Princeton , NJ , US
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        Generating functions
        Machine learning
        Statistical models
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          Generating functions
          Machine learning
          Statistical models
      ab: Formal and social epistemologists have devoted significant attention to the question of how to aggregate the credences of a group of agents who disagree about the probabilities of events. Moss (2011) and Pettigrew (2019) argue that group credences can be a linear mean of the credences of each individual in the group. By contrast, I argue that if the epistemic value of a credence function is determined solely by its accuracy, then we should, where possible, aggregate the underlying statistical models that individuals use to generate their credence functions, using "stacking" techniques from statistics and machine learning first developed by Wolpert (1992).
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