The Representation of Uncertainty for Validation and Analysis of Social Simulations
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Authors
Duong, Deborah
Makovoz, David
Singer, Hyam
Subjects
Advisors
Date of Issue
2010
Date
Fall 2010
Publisher
Language
en_US
Abstract
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.
Type
Working Paper
Description
Documents include Paper and Presentation.
Simulation Interoperability Standards Organization (SISO) SIW Conference Paper
Simulation Interoperability Standards Organization (SISO) SIW Conference Paper
Series/Report No
10F-SIW-021;
Department
Organization
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NPS Report Number
Sponsors
TRADOC Analysis Center – Monterey
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.