Expert deference as a belief revision schema

Synthese (1-2):1-28 (2020)
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Abstract

When an agent learns of an expert's credence in a proposition about which they are an expert, the agent should defer to the expert and adopt that credence as their own. This is a popular thought about how agents ought to respond to (ideal) experts. In a Bayesian framework, it is often modelled by endowing the agent with a set of priors that achieves this result. But this model faces a number of challenges, especially when applied to non-ideal agents (who nevertheless interact with ideal experts). I outline these problems, and use them as desiderata for the development of a new model. Taking inspiration from Richard Jeffrey's development of Jeffrey conditioning, I develop a model in which expert reports are taken as exogenous constraints on the agent's posterior probabilities. I show how this model can handle a much wider class of expert reports (for example reports of conditional probabilities), and can be naturally extended to cover propositions for which the agent has no prior.

Author's Profile

Joe Roussos
Institute for Futures Studies

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