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  1. Probabilistic Opinion Pooling.Franz Dietrich & Christian List - 2016 - In Alan Hájek & Christopher Hitchcock (eds.), The Oxford Handbook of Probability and Philosophy. Oxford: Oxford University Press.
    Suppose several individuals (e.g., experts on a panel) each assign probabilities to some events. How can these individual probability assignments be aggregated into a single collective probability assignment? This article reviews several proposed solutions to this problem. We focus on three salient proposals: linear pooling (the weighted or unweighted linear averaging of probabilities), geometric pooling (the weighted or unweighted geometric averaging of probabilities), and multiplicative pooling (where probabilities are multiplied rather than averaged). We present axiomatic characterisations of each class of (...)
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  • A Mathematical Theory of Communication.Claude Elwood Shannon - 1948 - Bell System Technical Journal 27 (April 1924):379–423.
    The mathematical theory of communication.
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  • In Defence of Objective Bayesianism.Jon Williamson - 2010 - Oxford University Press.
    Objective Bayesianism is a methodological theory that is currently applied in statistics, philosophy, artificial intelligence, physics and other sciences. This book develops the formal and philosophical foundations of the theory, at a level accessible to a graduate student with some familiarity with mathematical notation.
  • Accuracy and the Laws of Credence.Richard Pettigrew - 2016 - New York, NY.: Oxford University Press UK.
    Richard Pettigrew offers an extended investigation into a particular way of justifying the rational principles that govern our credences. The main principles that he justifies are the central tenets of Bayesian epistemology, though many other related principles are discussed along the way. Pettigrew looks to decision theory in order to ground his argument. He treats an agent's credences as if they were a choice she makes between different options, gives an account of the purely epistemic utility enjoyed by different sets (...)
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  • Probability Theory. The Logic of Science.Edwin T. Jaynes - 2002 - Cambridge University Press: Cambridge. Edited by G. Larry Bretthorst.
  • In defense of the maximum entropy inference process.J. Paris & A. Vencovská - 1997 - International Journal of Approximate Reasoning 17 (1):77-103.
    This paper is a sequel to an earlier result of the authors that in making inferences from certain probabilistic knowledge bases the maximum entropy inference process, ME, is the only inference process respecting “common sense.” This result was criticized on the grounds that the probabilistic knowledge bases considered are unnatural and that ignorance of dependence should not be identified with statistical independence. We argue against these criticisms and also against the more general criticism that ME is representation dependent. In a (...)
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  • Beyond Uncertainty: Reasoning with Unknown Possibilities.Katie Steele & H. Orri Stefánsson - 2021 - Cambridge University Press.
    The main aim of this book is to introduce the topic of limited awareness, and changes in awareness, to those interested in the philosophy of decision-making and uncertain reasoning.
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  • Decision Theory with a Human Face.Richard Bradley - 2017 - Cambridge University Press.
    When making decisions, people naturally face uncertainty about the potential consequences of their actions due in part to limits in their capacity to represent, evaluate or deliberate. Nonetheless, they aim to make the best decisions possible. In Decision Theory with a Human Face, Richard Bradley develops new theories of agency and rational decision-making, offering guidance on how 'real' agents who are aware of their bounds should represent the uncertainty they face, how they should revise their opinions as a result of (...)
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  • New theory about old evidence. A framework for open-minded Bayesianism.Sylvia9 Wenmackers & Jan-Willem Romeijn - 2016 - Synthese 193 (4).
    We present a conservative extension of a Bayesian account of confirmation that can deal with the problem of old evidence and new theories. So-called open-minded Bayesianism challenges the assumption—implicit in standard Bayesianism—that the correct empirical hypothesis is among the ones currently under consideration. It requires the inclusion of a catch-all hypothesis, which is characterized by means of sets of probability assignments. Upon the introduction of a new theory, the former catch-all is decomposed into a new empirical hypothesis and a new (...)
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  • Aggregating incoherent agents who disagree.Richard Pettigrew - 2019 - Synthese 196 (7):2737-2776.
    In this paper, we explore how we should aggregate the degrees of belief of a group of agents to give a single coherent set of degrees of belief, when at least some of those agents might be probabilistically incoherent. There are a number of ways of aggregating degrees of belief, and there are a number of ways of fixing incoherent degrees of belief. When we have picked one of each, should we aggregate first and then fix, or fix first and (...)
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  • Awareness growth and dispositional attitudes.Anna Mahtani - 2020 - Synthese 198 (9):8981-8997.
    Richard Bradley and others endorse Reverse Bayesianism as the way to model awareness growth. I raise a problem for Reverse Bayesianism—at least for the general version that Bradley endorses—and argue that there is no plausible way to restrict the principle that will give us the right results. To get the right results, we need to pay attention to the attitudes that agents have towards propositions of which they are unaware. This raises more general questions about how awareness growth should be (...)
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  • Three Kinds of Collective Attitudes.Christian List - 2014 - Erkenntnis 79 (S9):1601-1622.
    This paper offers a comparison of three different kinds of collective attitudes: aggregate, common, and corporate attitudes. They differ not only in their relationship to individual attitudes—e.g., whether they are “reducible” to individual attitudes—but also in the roles they play in relation to the collectives to which they are ascribed. The failure to distinguish them can lead to confusion, in informal talk as well as in the social sciences. So, the paper’s message is an appeal for disambiguation.
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  • Probabilistic Opinion Pooling Generalized. Part One: General Agendas.Franz Dietrich & Christian List - 2017 - Social Choice and Welfare 48 (4):747–786.
    How can different individuals' probability assignments to some events be aggregated into a collective probability assignment? Classic results on this problem assume that the set of relevant events -- the agenda -- is a sigma-algebra and is thus closed under disjunction (union) and conjunction (intersection). We drop this demanding assumption and explore probabilistic opinion pooling on general agendas. One might be interested in the probability of rain and that of an interest-rate increase, but not in the probability of rain or (...)
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  • Do Collaborators in Science Need to Agree?Haixin Dang - 2019 - Philosophy of Science 86 (5):1029-1040.
    I argue that collaborators do not need to reach broad agreement over the justification of a consensus claim. This is because maintaining a diversity of justifiers within a scientific collaboration has important epistemic value. I develop a view of collective justification that depends on the diversity of epistemic perspectives present in a group. I argue that a group can be collectively justified in asserting that P as long as the disagreement among collaborators over the reasons for P is itself justified. (...)
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  • A Role for Judgment Aggregation in Coauthoring Scientific Papers.Liam Kofi Bright, Haixin Dang & Remco Heesen - 2018 - Erkenntnis 83 (2):231-252.
    This paper addresses the problem of judgment aggregation in science. How should scientists decide which propositions to assert in a collaborative document? We distinguish the question of what to write in a collaborative document from the question of collective belief. We argue that recent objections to the application of the formal literature on judgment aggregation to the problem of judgment aggregation in science apply to the latter, not the former question. The formal literature has introduced various desiderata for an aggregation (...)
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  • Aggregating disparate estimates of chance.Daniel Osherson - manuscript
    We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistic incoherent. We address the problem of revising the probability estimates of the panel so as to produce a coherent set that best represents the group’s expertise.
     
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  • Foundations and Applications of Inductive Probability.Roger D. Rosenkrantz - 1981 - Ridgeview Press.
  • Probabilistic opinion pooling generalised. Part two: The premise-based approach.Franz Dietrich & Christian List - 2017 - Social Choice and Welfare 48 (4):787–814.
    How can different individuals' probability functions on a given sigma-algebra of events be aggregated into a collective probability function? Classic approaches to this problem often require 'event-wise independence': the collective probability for each event should depend only on the individuals' probabilities for that event. In practice, however, some events may be 'basic' and others 'derivative', so that it makes sense first to aggregate the probabilities for the former and then to let these constrain the probabilities for the latter. We formalize (...)
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