The Representation of Uncertainty for Validation and Analysis of Social Simulations
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An iterative process of social science theory improvement through computational social science includes theory, computation, and then readjustment of theory. The key to iterative improvement is the ability to make use of the partially correct enough to improve upon it. The techniques of soft computation can help us to make full use of partially correct and inconsistent data. Probabilistic ontologies serve to repurpose data for use by drawing probabilistic correspondence. Probability theory can accommodate all types of uncertainty, from credibility in the theories or the data, to intrinsic uncertainty, to uncertainty of match. Probabilistic ontologies can process this data, whether the processing involves Bayesian data generation, the computation of a scalar value of match for validation, the representation of dynamic in a Markov Process, or expressing the data with gradient so that data mining may be used to help readjust theory.
Documents include Paper and Presentation.Approved for public release; distribution is unlimited.Simulation Interoperability Standards Organization (SISO) SIW Conference Paper
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