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His current collaborations with Kadane and Schervish incude a theory for indexing the degree of incoherence in non-Bayesian statistical decisions, work on the representation of coherent choice-functions using sets of probabilitis, and investigations involving scoring rules for probabilistic forecasts. The three also work together on the development of finitely additive expectations for unbounded random variables. In collaboration with M.J. Schervish and J.B. Kadane (Statistics, CMU), they relax the norms of Bayesian theory to permit a unified standard, both for individuals acting as separate decision makers and collectively, in forming a cooperative group agent. By contrast, this is an impossibility for strict Bayesian theory. For a second example, in collaboration with Larry Wasserman (Statistics, CMU), they examine the short-run consequences of using Bayes rule for updating a set of expert Bayesian opinions with shared information. They focus on anomalous cases (they call dilation), where an experiment is certain to result in new evidence that increases the experts: uncertainty about an event of common interest where uncertainty is reflected in the extent of probabilistic disagreements among the experts.
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Erkenntnispp.1-24, (2024)
Springer eBookspp.15-35, (2022)
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Statistical Methods & Applicationsno. 4 (2022): 1069-1069
SSRN Electronic Journal (2021)
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