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  1. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.Judea Pearl - 1988 - Morgan Kaufmann.
    The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
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  • The computational complexity of probabilistic inference using bayesian belief networks.Gregory F. Cooper - 1990 - Artificial Intelligence 42 (2-3):393-405.
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  • Are disagreements honest.Tyler Cowen & Robin Hanson - forthcoming - Journal of Economic Methodology.
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  • Isagreement is unpredictable.Robin Hanson - unknown
    Given common priors, no agent can publicly estimate a non-zero sign for the difference between his estimate and another agent’s future estimate. Thus rational agents cannot publicly anticipate the direction in which other agents will disagree with them.  2002 Elsevier Science B.V. All rights reserved.
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  • Eliciting Objective Probabilities via Lottery Insurance Games.Robin Hanson - unknown
    Since utilities and probabilities jointly determine choices, event-dependent utilities complicate the elicitation of subjective event probabilities. However, for the usual purpose of obtaining the information embodied in agent beliefs, it is sufficient to elicit objective probabilities, i.e., probabilities obtained by updating a known common prior with that agent’s further information. Bayesians who play a Nash equilibrium of a certain insurance game before they obtain relevant information will afterward act regarding lottery ticket payments as if they had event-independent risk-neutral utility and (...)
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  • Elicitation of Personal Probabilities and Expectations.Leonard Savage - 1971 - Journal of the American Statistical Association 66 (336):783-801.
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  • Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.J. Pearl, F. Bacchus, P. Spirtes, C. Glymour & R. Scheines - 1988 - Synthese 104 (1):161-176.
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