In many problems, information is available from many different sources. Aggregating these diverse bits of information is important to good decision-making but also leads to special statistical problems in developing models to characterize the information. Prior research in this areas has relied primarily on the use of historical data as a basis for modelling the information sources. The proposed research develops a Bayesian framework that a decision maker can use to encode his subjective knowledge about the information sources in order to aggregate their information. This framework features a highly flexible environment for modelling the probabilistic nature and interrelationships of the information sources and requires straightforward and intuitive subjective judgements using proven decision-analytic assessment techniques. Analysis of the assessments to produce a posterior distribution for the event of interest is accomplished through analytical, numerical or Monte Carlo techniques.
