Representing Diversity in Communities of Bayesian Decision-makers

High-quality information has emerged from the contributions of many using the wiki paradigm. A logical next step is to use the wisdom of the crowd philosophy to solve complex problems and produce informed policy. We introduce a new approach to aggregating the beliefs and preferences of many individuals to form models that can be used in social policy and decision-making. Traditional social choice functions used to aggregate beliefs and preferences attempt to find a single solution for the whole population, but may produce an irrational social choice when a stalemate between opposing objectives occurs. Our approach, called collective belief aggregation, partitions a population into collectives that share a preference order over the expected utilities of decision options or the posterior likelihoods of a probabilistic variable. It can be shown that if a group of individuals share a preference order over the options, their aggregate will uphold principles of rational aggregation defined by social choice theorists. Super-agents can then be formed for each collective that accurately represent the preferences of their collective. These super-agents can be used to represent the collectives in decision analysis and decision-making tasks. We demonstrate the potential of using collective belief aggregation to incorporate the objectives of stakeholders in policy-making using preferences elicited from people about healthcare policy.