ALGORITHM OF EFFECTIVE CONTROLS FOR NONSTOCHASTIC CAUSAL MODELS IN THE ABSENCE OF OBSERVABLE VARIABLES FOR SYSTEMS OF DECISION MAKING CONTROL
Abstract
The paper deals with the problem of reproducing the decision-making process by a person under
conditions of uncertainty and incompleteness of the initial data. The decision-maker relies on his
belief system, which includes a shared vision of the system in relation to which the decision is being
made. The system is presented in the form of a causal model created on the basis of human mental
representations. These models are directed graphs, on the arcs of which the causal relationship is
expressed in the form of labels with a sign that determines the direction of change in the state of the
system. The vertices of this directed graph are high-level abstraction concepts. This graph simulates
the functioning of a real system. Thus, we investigate the problem of predicting and controlling human
actions based on non-stochastic causal models in the absence of observable variables for use in
decision support systems and expert systems. Decision-making is considered from the point of view of
the choice of objects of application of managerial influences - the factors of the model. In this study,
we show that the application of the proposed algorithm can facilitate decision-making regarding the
choice of control actions that support the achievement of the tactical and strategic goals of the decision
maker. It should be noted that the algorithm implements an automatic selection of the regularization
parameter, which makes the development and application of the proposed algorithm available
to users who do not have sufficient mathematical training. The convergence of the sequence of Lagrange
multipliers of an effective control algorithm is proved. The theorem on resonance in a nonstochastic
causal mod-el, represented by a directed graph, which is determined by the range of admissible
values of the damping coefficient in the control model, is proved. It is expected that the introduction
of this tool into decision support systems will in-crease the reliability of decisions regarding
the operation of the system as a whole. The choice of control actions using the proposed algorithm
has high efficiency and productivity. Thus, the results presented in the study can be useful for
developing applications in intelligent systems.
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