Summary
Often in economic reality only partial information (PI) is available. Decision problems under PI can be dealt with Decision Support Systems (DSS). A theoretical foundation of processing linear and nonlinear PI in DSS is attained by combining comparative/interval probability theory, probabilistic logic, and decision theory. The consistency of the decision maker’s subjective statements is examined by a system of probability axioms. A representation of the PI as a system of equations/inequalities is shown. Bounds of unknown probabilities are determined by solving a linear or signomial programming problem. Applying the Max E min principle even to nonlinear PI is proposed to resolve decision problems.
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Dellmann, F. (1996). Processing Partial Information in Decision Support Systems. In: Gaul, W., Pfeifer, D. (eds) From Data to Knowledge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79999-0_44
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DOI: https://doi.org/10.1007/978-3-642-79999-0_44
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