In many decision situations information is available from a number of
different sources. Aggregating the diverse bits of information is an i
mportant aspect of the decision-making process but entails special sta
tistical modeling problems in characterizing the information. Prior re
search in this area has relied primarily on the use of historical data
as a basis for modeling the information sources. We develop a Bayesia
n framework that a decision maker can use to encode subjective knowled
ge about the information sources in order to aggregate point estimates
of an unknown quantity of interest. This framework features a highly
flexible environment for modeling the probabilistic nature and interre
lationships of the information sources and requires straightforward an
d intuitive subjective judgments using proven decision-analysis assess
ment techniques. Analysis of the constructed model produces a posterio
r distribution for the quantity of interest. An example based on healt
h risks due to ozone exposure demonstrates the technique.