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The paper is concerned with measuring and assessment of risk scenes in managerial decision-making. It builds upon the uncertainty of economic information, which is converted into the concept of risk scene expressed in terms of probability and using confidence intervals of the predicted quantities. The paper explains the relation of a degree of risk expressed by the classical information measure, bit, by the concept of confidence intervals, or possibly by the standard deviation. When making decisions, the manager is interested not only in the quantitatively expressed value of risk scene with the use of forecasting models, but mainly in the impact of decrease/increase of decision-making risk expressed by the effect, i.e. profit/loss caused by such a decision to achieve targets. A method of decision effect calculation is proposed which is derived from the information entropy change and the change in risk scene in managerial decision-making. Forecasting models are applied which are based on an expert estimate and a statistical theory, and the risk scenes are assessed in forecasting models based on neural networks.
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