Aggregation of Probabilistic Opinions

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This proposal focuses on the aggregation process used by individual Decision-Makers (DMs) to combine probabilistic information from multiple, non-independent sources. Typical examples are investors who combine forecasts from various financial advisors regarding the chances of certain stocks to appreciate in value, and patients who must aggregate information from various experts about the chances of success of a given medical procedure.A model is described that assumes that (a) the DM combines information by averaging the various forecasts, and (b) the DM's confidence in the aggregate is inversely related to the variance of the (possibly weighted) mean forecast. This model is used to derive a series of predictions about the factors that affect and drive the DM's confidence. They will be tested in a series of experiments that would clarify (a) the nature of the aggregation rules used by DMs, (b) the factors that affect the DMs' confidence in the final aggregate, (c) the nature and level of dependence between the aggregates and the confidence they inspire, and (d) the factors that determine the DM's preference for certain advisors.In addition to contributing to our understanding of the basic psychological process involved in aggregating opinions, the results have immediate implications for several questions of considerable practical interest that arise in many fields including, but not restricted to, financial, military and medical decision-making. The results will provide (at least partial) guidelines for approaching questions such as:(a) What kind of, and how many, advisors should a DM consult to achieve a desired level of confidence?(b) What factors make some advisors more or less attractive in certain types of decision problems?(c) What are the sources of the surprisingly high correlation among advisors? What is the relative importance of the various factors and how can one use this information to choose advisors in an optimal fashion?