Evidence and Objective Bayesian Epistemology

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Subjective Bayesian epistemology is currently widely perceived to be the paradigm of evidential reasoning in the philosophy of science and of rational degrees of belief underpinning the dominant account of decision making, as well as many of our statistical methods. Objective(!) Bayesian epistemology is emerging as an alternative epistemology challenging the subjective account. Objective Bayesians hold that rational degrees of belief are determined by the agent's evidence while subjective Bayesians claim that there is a range of rational degrees of belief given an agent's evidence. We urgently need to determine which Bayesian epistemology we should employ. This project aims to strengthen the case for the objective approach.Evidential reasoning in subjective Bayesian epistemology is based on two long-standing interconnected parts: it is grounded in expected utility maximisation which is implemented by updating beliefs via conditionalisation. It will be argued that these parts face so-far under-appreciated difficulties. Currently, evidential reasoning in objective Bayesian epistemology is lacking solid foundations for evidential reasoning to stand upon and is also lacking a framework for an implementation of evidential reasoning. Without these foundations and such an implementation objective Bayesian epistemology cannot aspire to stand up to the subjective approach.This project will hence develop foundations for evidential reasoning in objective Bayesian epistemology. Furthermore, the objective approach will be supplied with a comprehensive framework for evidential reasoning. It will be argued that the provided foundations and the developed implementation are superior to those of the subjective approach in key aspects and thus strengthen the case for the objective approach.This research will inform a core debate within formal epistemology, namely the question of whether the subjective or the objective approach better characterises the norms governing belief. The outcome of this debate is highly relevant to rational decision making in policy-making, science and daily life since the two approaches single out different actions as rational, in general. The outcome is furthermore relevant to statistics and artificial intelligence, where Bayesian principles are widely used and where there is little agreement as to whether subjective or objective Bayesian principles are best.