We present ongoing research on large-scale decision models in which there are many invested individuals. We apply our unique Bayesian belief aggregation approach to decision problems, taking into consideration the beliefs and utilities of each individual. Instead of averaging all beliefs to form a single consensus, our aggregation approach allows divergence in beliefs and utilities to emerge. In decision models this divergence has implications for game theory- enabling the competitive aspects in an apparent cooperative situation to emerge. Current approaches to belief aggregation assume cooperative situations by forming one consensus from diverse beliefs. However, many decision problems have individuals and groups with opposing goals, therefore this forced consensus does not accurately represent the decision problem. By applying our approach to the topical issue of stem cell research using input from many diverse individuals, we analyze the behavior of a decision model including the groups of agreement that emerge. We show how to find the Pareto optimal solutions, which represent the decisions in which no group can do better without another group doing worse. We analyze a range of solutions, from attempting to "please everybody," with the solution that minimizes all emerging group's losses, to optimizing the outcome for a subset of individuals. Our approach has the long-reaching potential to help define policy and analyze the effect of policy change on individuals.
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