Leveraging Consensus and Divergence in Bayesian Belief Aggregation

Many fields have a need to build representative or predictive models from a number of unique individuals who each can contribute their experience and beliefs to the whole. For instance, intelligence agencies may wish to build a model from a number of experts to analyze potential terrorist attacks. In addition, a sociological survey may want a model representing the beliefs of cultural or political groups. However, challenges remain that have limited the success of merging opinions to form consensus models. Our research in progress presents a new approach to combine, or aggregate the beliefs of many individuals using graphical models. Existing Bayesian belief aggregation methods utilize an opinion pool function to find a single consensus on a given probability distribution. These opinion pool functions have many theoretical problems including breaking several assumptions for Bayesian reasoning. More practically, existing opinion pool functions do not represent reality well, especially in cases of diverse opinions.