Invariant Equivocation

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• 作者：Jürgen Landes ; George Masterton
• 刊名：Erkenntnis
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：82
• 期：1
• 页码：141-167
• 全文大小：
• 刊物主题：Philosophy, general; Epistemology; Ontology; Ethics; Logic;
• 出版者：Springer Netherlands
• ISSN：1572-8420
• 卷排序：82

文摘

Objective Bayesians hold that degrees of belief ought to be chosen in the set of probability functions calibrated with one’s evidence. The particular choice of degrees of belief is via some objective, i.e., not agent-dependent, inference process that, in general, selects the most equivocal probabilities from among those compatible with one’s evidence. Maximising entropy is what drives these inference processes in recent works by Williamson and Masterton though they disagree as to what should have its entropy maximised. With regard to the probability function one should adopt as one’s belief function, Williamson advocates selecting the probability function with greatest entropy compatible with one’s evidence while Masterton advocates selecting the expected probability function relative to the density function with greatest entropy compatible with one’s evidence. In this paper we discuss the significant relative strengths of these two positions. In particular, Masterton’s original proposal is further developed and investigated to reveal its significant properties; including its equivalence to the centre of mass inference process and its ability to accommodate higher order evidence.