Abstract

AbstractThis paper explores the fact that linear opinion pooling can be represented as a Bayesian update on the opinions of others. It uses this fact to propose a new interpretation of the pooling weights. Relative to certain modelling assumptions the weights can be equated with the so-called truth-conduciveness known from the context of Condorcet's jury theorem. This suggests a novel way to elicit the weights.

Highlights

  • Say that Raquel consults her peer Quassim about the proposition S

  • They show that certain constraints on the model suffice, and that we do not need to specify the Bayesian model in full detail to obtain an outcome in accordance with linear pooling

  • The approach of this paper differs from that of the afore-mentioned ones. It does not take a Bayesian model with some additional assumptions as starting point, to investigate if and when we arrive at some form of pooling

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Summary

Introduction

Say that Raquel consults her peer Quassim about the proposition S. Genest and Schervish (1985) consider a Bayesian model of a decision maker learning the opinion of an expert, and investigate when the result of the update coincides with a general version of the linear opinion pool. They show that certain constraints on the model suffice, and that we do not need to specify the Bayesian model in full detail to obtain an outcome in accordance with linear pooling. An important benefit of this interpretation is that it facilitates the empirical elicitation of weights

Bayesian models of linear pooling
Likelihood functions for the linear pool
Other Bayesian models of pooling
The Bayesian model of this paper
Trust and truth-conduciveness
Truth-conduciveness in voting
Voting as pooling
Weight as truth-conduciveness
Interpreting the weights
Weight elicitation
Existing interpretations of weights
Future research

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