Bayesian Decision Theory and Stochastic Independence

Autor:	Mongin, Philippe
Rok vydání:	2017
Předmět:	Computer Science - Computer Science and Game Theory J2
Zdroj:	Philos. sci. 87 (2020) 152-178
Druh dokumentu:	Working Paper
DOI:	10.1086/706083
Popis:	Stochastic independence has a complex status in probability theory. It is not part of the definition of a probability measure, but it is nonetheless an essential property for the mathematical development of this theory. Bayesian decision theorists such as Savage can be criticized for being silent about stochastic independence. From their current preference axioms, they can derive no more than the definitional properties of a probability measure. In a new framework of twofold uncertainty, we introduce preference axioms that entail not only these definitional properties, but also the stochastic independence of the two sources of uncertainty. This goes some way towards filling a curious lacuna in Bayesian decision theory. Comment: In Proceedings TARK 2017, arXiv:1707.08250
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1707.08757 Zobrazit plný text záznamu View this record from Arxiv