Voting Processes in Complex Adaptive Systems to Combine Perspectives of Disparate Social Simulations into a Coherent Picture

If computational social science is to find practical application in informing policy decisions and proportionately analyzing courses of action, then it will have to make progress in the area of composition of social models.  Since a single simulation cannot hold a world of information, policy makers need to switch in and out modules in federations of simulations to test policies against all possible social environments.  Voting processes as they occur in nature, both in the form of cognition in a human mind of disparate world views, and in the form of equilibria seeking coevolution of species, inform how to combine model results externally and deeply, respectively.  These algorithms, which use the same principles of soft computation found in nature, enable any models to mesh together, even if they have different ontologies, or their data conflict, regardless of the degree they overlap.  A whiteboard architecture in which models report in their own ontologies how other models may inform them and what they have to offer other models, is a framework for the arbitrary meshing of social models.