E.P. Chistokletov’s scientific contributions

There are studied Informational methods for analyzing and managing systems under uncertainty. The expediency of the use of information uncertainty in the tasks of identification of control objects and the synthesis of regulatory systems is substantiated. For the numerical evaluation of information uncertainty, the amount of disinformation is used as negative Bongard useful information. Such information uncertainty can serve as a criterion for the adequacy of the mathematical model of the control object. This makes it possible to compare several models and select the most adequate model that contributes the least amount of disinformation, and also provides a check of the statistical hypothesis about the adequacy of a particular model. If the criterion value exceeds a certain critical value, the adequacy hypothesis must be rejected. To calculate the critical values of the information adequacy criterion, a statistical experiment was performed. Using the Monte Carlo method, the probability distribution of the information criterion was investigated. A sufficiently smooth empirical criterion distribution function was constructed. The distribution of the information criterion has a pronounced asymmetry and a small positive kurtosis. It is revealed that this distribution is best approximated by the Generalized Extreme Value Distribution law. The critical value can be defined as a quantile of the level of 0.01 or 0.05 of this distribution. Keywords: adequacy, computer modeling, distribution law, identification, information criterion, Monte Carlo method, controlled object, statistical hypothesis, uncertainty.