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  1. Aggregating Causal Judgments.Richard Bradley, Franz Dietrich & Christian List - 2014 - Philosophy of Science 81 (4):491-515.
    Decision-making typically requires judgments about causal relations: we need to know the causal effects of our actions and the causal relevance of various environmental factors. We investigate how several individuals' causal judgments can be aggregated into collective causal judgments. First, we consider the aggregation of causal judgments via the aggregation of probabilistic judgments, and identify the limitations of this approach. We then explore the possibility of aggregating causal judgments independently of probabilistic ones. Formally, we introduce the problem of causal-network aggregation. (...)
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  • Awareness Revision and Belief Extension.Joe Roussos - 2024 - Australasian Journal of Philosophy:1-24.
    What norm governs how an agent should change their beliefs when they encounter a completely new possibility? Orthodox Bayesianism has no answer, as it takes all learning to involve updating prior beliefs. A partial proposal is Reverse Bayesianism, which mandates the preservation of ratios of prior probabilities, but it faces counterexamples introduced by Mahtani (2021). I propose to separate awareness growth into two stages: awareness revision and belief extension. I argue that Mahtani’s cases highlight that we need to theorize awareness (...)
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  • Foundations for Knowledge-Based Decision Theories.Zeev Goldschmidt - 2024 - Australasian Journal of Philosophy 102 (4):939-958.
    Several philosophers have proposed Knowledge-Based Decision Theories (KDTs)—theories that require agents to maximize expected utility as yielded by utility and probability functions that depend on the agent’s knowledge. Proponents of KDTs argue that such theories are motivated by Knowledge-Reasons norms that require agents to act only on reasons that they know. However, no formal derivation of KDTs from Knowledge-Reasons norms has been suggested, and it is not clear how such norms justify the particular ways in which KDTs relate knowledge and (...)
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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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  • Expert deference as a belief revision schema.Joe Roussos - 2020 - Synthese (1-2):1-28.
    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 (...)
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  • What is conditionalization, and why should we do it?Richard Pettigrew - 2020 - Philosophical Studies 177 (11):3427-3463.
    Conditionalization is one of the central norms of Bayesian epistemology. But there are a number of competing formulations, and a number of arguments that purport to establish it. In this paper, I explore which formulations of the norm are supported by which arguments. In their standard formulations, each of the arguments I consider here depends on the same assumption, which I call Deterministic Updating. I will investigate whether it is possible to amend these arguments so that they no longer depend (...)
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  • Representation in Models of Epistemic Democracy.Patrick Grim, Aaron Bramson, Daniel J. Singer, William J. Berger, Jiin Jung & Scott E. Page - 2020 - Episteme 17 (4):498-518.
    Epistemic justifications for democracy have been offered in terms of two different aspects of decision-making: voting and deliberation, or ‘votes’ and ‘talk.’ The Condorcet Jury Theorem is appealed to as a justification in terms votes, and the Hong-Page “Diversity Trumps Ability” result is appealed to as a justification in terms of deliberation. Both of these, however, are most plausibly construed as models of direct democracy, with full and direct participation across the population. In this paper, we explore how these results (...)
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  • Weighted averaging, Jeffrey conditioning and invariance.Denis Bonnay & Mikaël Cozic - 2018 - Theory and Decision 85 (1):21-39.
    Jeffrey conditioning tells an agent how to update her priors so as to grant a given probability to a particular event. Weighted averaging tells an agent how to update her priors on the basis of testimonial evidence, by changing to a weighted arithmetic mean of her priors and another agent’s priors. We show that, in their respective settings, these two seemingly so different updating rules are axiomatized by essentially the same invariance condition. As a by-product, this sheds new light on (...)
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  • Introduction to the special issue “Beliefs in Groups” of Theory and Decision.Franz Dietrich & Wlodek Rabinowicz - 2018 - Theory and Decision 85 (1):1-4.
    This symposium in the overlap of philosophy and decision theory is described well by its title “Beliefs in Groups”. Each word in the title matters, with one intended ambiguity. The symposium is about beliefs rather than other attitudes such as preferences; these beliefs take the form of probabilities in the first three contributions, binary yes/no beliefs (‘judgments’) in the fourth contribution, and qualitative probabilities (‘probability grades’) in the fifth contribution. The beliefs occur in groups, which is ambiguous between beliefs of (...)
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  • A Theory of Bayesian Groups.Franz Dietrich - 2017 - Noûs 53 (3):708-736.
    A group is often construed as one agent with its own probabilistic beliefs (credences), which are obtained by aggregating those of the individuals, for instance through averaging. In their celebrated “Groupthink”, Russell et al. (2015) require group credences to undergo Bayesian revision whenever new information is learnt, i.e., whenever individual credences undergo Bayesian revision based on this information. To obtain a fully Bayesian group, one should often extend this requirement to non-public or even private information (learnt by not all or (...)
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  • Practical reasoning, rule-following and belief revision: an account in terms of Jeffrey’s rule.Cyril Hédoin - 2020 - Synthese 198 (8):7627-7645.
    This paper provides a conceptual exploration of the implication of Jeffrey’s rule of belief revision to account for rule-following behavior in a game-theoretic framework. Jeffrey’s rule reflects the fact that in many cases learning something new does not imply that one has full assurance about the true content of the information. In other words, the same information may be both perceived and interpreted in several different ways. I develop an account of rule-following behavior according to which, in the context of (...)
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  • A note on deterministic updating and van Fraassen’s symmetry argument for conditionalization.Richard Pettigrew - 2021 - Philosophical Studies 178 (2):665-673.
    In a recent paper, Pettigrew argues that the pragmatic and epistemic arguments for Bayesian updating are based on an unwarranted assumption, which he calls deterministic updating, and which says that your updating plan should be deterministic. In that paper, Pettigrew did not consider whether the symmetry arguments due to Hughes and van Fraassen make the same assumption Scientific inquiry in philosophical perspective. University Press of America, Lanham, pp. 183–223, 1987). In this note, I show that they do.
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  • Probability, coherent belief and coherent belief changes.John Cantwell & Hans Rott - 2019 - Annals of Mathematics and Artificial Intelligence 87 (3):259-291.
    This paper is about the statics and dynamics of belief states that are represented by pairs consisting of an agent's credences (represented by a subjective probability measure) and her categorical beliefs (represented by a set of possible worlds). Regarding the static side, we argue that the latter proposition should be coherent with respect to the probability measure and that its probability should reach a certain threshold value. On the dynamic side, we advocate Jeffrey conditionalisation as the principal mode of changing (...)
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  • Bayesian Belief Revision Based on Agent’s Criteria.Yongfeng Yuan - 2021 - Studia Logica 109 (6):1311-1346.
    In the literature of belief revision, it is widely accepted that: there is only one revision phase in belief revision which is well characterized by the Bayes’ Rule, Jeffrey’s Rule, etc.. However, as I argue in this article, there are at least four successive phases in belief revision, namely first/second order evaluation and first/second order revision. To characterize these phases, I propose mainly four rules of belief revision based on agent’s criteria, and make one composition rule to characterize belief revision (...)
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  • Justifying Lewis’s Kinematics of Chance.Patryk Dziurosz-Serafinowicz - 2021 - British Journal for the Philosophy of Science 72 (2):439-463.
    In his ‘A Subjectivist’s Guide to Objective Chance’, Lewis argued that a particular kinematical model for chances follows from his principal principle. According to this model, any later chance function is equal to an earlier chance function conditional on the complete intervening history of non-modal facts. This article first investigates the conditions that any kinematical model for chance needs to satisfy to count as Lewis’s kinematics of chance. Second, it presents Lewis’s justification for his kinematics of chance and explains why (...)
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