Calibration and the Aggregation of Probabilities

In order to avoid the task of assessing a complicated likelihood function, Morris uses an axiomatic approach to develop a multiplicative rule for aggregating a decision maker's and an expert's probabilities. An essential shortcoming of the multiplicative rule is that it does not allow the decision maker to model his beliefs about the dependence between his assessment and the expert's. The root of the problem lies in the fact that the decision maker must calibrate the expert's information. When the calibration is done properly, the decision maker is forced to tackle the task which Morris proposes to avoid.calibration, expert resolution, dependence

Combining Overlapping Information

A decision maker trying to learn about an uncertain quantity may obtain divergent information from a number of sources (e.g., experts). In this paper we study the decision maker's problem of aggregating this information to form his posterior distribution when he believes that the experts' opinions are dependent due to shared information. Examples in both normal and Bernoulli frameworks lead to some surprising general insights regarding the impact of the dependence on the posterior distribution. The study has implications for practitioners who face real-world information-aggregation problems and for groups of experts who seek a consensus.Bayesian combination of information, information aggregation, consensus, dependence

Making hard decision: an introduction to decision analysis/ Clemen

xviii, 557 hal.: ill.; 26 c