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  1. Probabilistic Opinion Pooling.Franz Dietrich & Christian List - 2016 - In A. Hajek & C. Hitchcock (eds.), Oxford Handbook of Philosophy and Probability. 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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  • Judgment Aggregation: A Survey.Christian List & Clemens Puppe - 2009 - In Christian List & Clemens Puppe (eds.), Handbook of Rational and Social Choice. Oxford University Press.
    Our aim in this survey article is to provide an accessible overview of some key results and questions in the theory of judgment aggregation. We omit proofs and technical details, focusing instead on concepts and underlying ideas.
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  • Probabilistic Opinion Pooling Generalized -- Part One: General Agendas.Franz Dietrich & Christian List - 2017 - Social Choice and Welfare 48:747–786.
    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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  • The Similarity of Causal Structure.Benjamin Eva, Reuben Stern & Stephan Hartmann - unknown
    Does y obtain under the counterfactual supposition that x? The answer to this question is famously thought to depend on whether y obtains in the most similar world in which x obtains. What this notion of ‘similarity’ consists in is controversial, but in recent years, graphical causal models have proved incredibly useful in getting a handle on considerations of similarity between worlds. One limitation of the resulting conception of similarity is that it says nothing about what would obtain were the (...)
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  • Peer Disagreement and Independence Preservation.Carl Wagner - 2011 - Erkenntnis 74 (2):277-288.
    It has often been recommended that the differing probability distributions of a group of experts should be reconciled in such a way as to preserve each instance of independence common to all of their distributions. When probability pooling is subject to a universal domain condition, along with state-wise aggregation, there are severe limitations on implementing this recommendation. In particular, when the individuals are epistemic peers whose probability assessments are to be accorded equal weight, universal preservation of independence is, with a (...)
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  • Aggregating Infinitely Many Probability Measures.Frederik Herzberg - 2015 - Theory and Decision 78 (2):319-337.
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