Modern studies of human capital using methods of fuzzy modeling make it possible to make predictive assessments of the state of its development for the near term, as well as to carry out strategic planning of the activities of both an individual enterprise and a number of sectors of the economy as a whole. In addition to predictive tasks, the fuzzy model allows for a comparative analysis of various economic objects based on rating assessments. When using operational input data for evaluation, the problem of correcting the model itself often arises. At the same time, changes most often relate to the base of fuzzy knowledge or the rules of fuzzy derivation. Another approach proposed by the authors is the correction of the scale range of the input data with an unchanged set of rules. In this situation, the correction consists in determining the smallest and largest values of the input data that are outside the range of the input scale. If there are several inputs, each of the scales must be adjusted. The above method uses a dynamic scale of input parameters for fuzzy estimation. The developed fuzzy model is a model with dynamic boundaries. Based on Mamdani's study of the fuzzy model, it is shown that when the scale of the input parameter is expanded, an estimation error occurs, which the authors proposed to call the «fuzzy scale expansion error». As a result of the study, numerical values of this error were obtained. The reason for the error is a decrease in the sensitivity of the fuzzy model, as a result of which the model reduces its resolution. At the same time, it is observed that several objects receive the same grades. The importance of the conducted research lies in the need to increase the reliability of the fuzzy model when working with dynamic input data.