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  1. Rational factionalization for agents with probabilistically related beliefs.David Peter Wallis Freeborn - 2024 - Synthese 203 (2):1-27.
    General epistemic polarization arises when the beliefs of a population grow further apart, in particular when all agents update on the same evidence. Epistemic factionalization arises when the beliefs grow further apart, but different beliefs also become correlated across the population. I present a model of how factionalization can emerge in a population of ideally rational agents. This kind of factionalization is driven by probabilistic relations between beliefs, with background beliefs shaping how the agents’ beliefs evolve in the light of (...)
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  • From Knowledge-based Programs to Graded Belief-based Programs, Part I: On-line Reasoning.Noël Laverny & Jérôme Lang - 2005 - Synthese 147 (2):277-321.
    Knowledge-based programs (KBPs) are a powerful notion for expressing action policies in which branching conditions refer to implicit knowledge and call for a deliberation task at execution time. However, branching conditions in KBPs cannot refer to possibly erroneous beliefs or to graded belief, such as “if my belief that φ holds is high then do some action α else perform some sensing action β”.
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  • The value of cost-free uncertain evidence.Patryk Dziurosz-Serafinowicz & Dominika Dziurosz-Serafinowicz - 2021 - Synthese 199 (5-6):13313-13343.
    We explore the question of whether cost-free uncertain evidence is worth waiting for in advance of making a decision. A classical result in Bayesian decision theory, known as the value of evidence theorem, says that, under certain conditions, when you update your credences by conditionalizing on some cost-free and certain evidence, the subjective expected utility of obtaining this evidence is never less than the subjective expected utility of not obtaining it. We extend this result to a type of update method, (...)
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  • Probabilistic Belief Contraction.Raghav Ramachandran, Arthur Ramer & Abhaya C. Nayak - 2012 - Minds and Machines 22 (4):325-351.
    Probabilistic belief contraction has been a much neglected topic in the field of probabilistic reasoning. This is due to the difficulty in establishing a reasonable reversal of the effect of Bayesian conditionalization on a probabilistic distribution. We show that indifferent contraction, a solution proposed by Ramer to this problem through a judicious use of the principle of maximum entropy, is a probabilistic version of a full meet contraction. We then propose variations of indifferent contraction, using both the Shannon entropy measure (...)
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  • Learning Bayesian network parameters under equivalence constraints.Tiansheng Yao, Arthur Choi & Adnan Darwiche - 2017 - Artificial Intelligence 244 (C):239-257.
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  • Reflection and conditionalization: Comments on Michael Rescorla.Bas C. van Fraassen - 2023 - Noûs 57 (3):539-552.
    Rescorla explores the relation between Reflection, Conditionalization, and Dutch book arguments in the presence of a weakened concept of sure loss and weakened conditions of self‐transparency for doxastic agents. The literature about Reflection and about Dutch Book arguments, though overlapping, are distinct, and its history illuminates the import of Rescorla's investigation. With examples from a previous debate in the 70s and results about Reflection and Conditionalization in the 80s, I propose a way of seeing the epistemic enterprise in the light (...)
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  • Interventions and belief change in possibilistic graphical models.Salem Benferhat - 2010 - Artificial Intelligence 174 (2):177-189.
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