Measurement Reduction in the Presence of Subjective Information

Abstract

This paper considers an application of the mathematical formalism of subjective modeling to improve the quality of the interpretation of measurement data given incomplete and unreliable subjective information about the research object. It is shown that the mathematical formalism of subjective modeling allows the researcher to use measurement data to test the adequacy of a subjective model to a particular research objective, correct the subjective model, combine the observation data and the researcher’s subjective notions about the research object (in order to optimize their conclusions about its features), and check the information about the research object for the presence of misinformation. The results are illustrated by computational experiments.