Uncertainty: qualitative and quantitative aspects

Abstract

The analysis the concept of uncertainty, its origin, interpretation in various scientific fields, sources of uncertainty, qualitative and quantitative aspects of this concept, as well as approaches to quantifying uncertainty are presented. Methods of historical and comparative analysis were used in the study. The main conclusion is the necessity of taking into account the presence of uncertainty and quantification of uncertainty of the results of mathematical modeling of objects and phenomena of the surrounding world in the same way as we do when assessing measurement uncertainty of experimental data. In is shown that the interval approach to estimating uncertainty of modeling results currently seems to be the most promising. The concept of uncertainty first proposed at the beginning of the last century refers to epistemic situations involving imperfect or unknown information. This concept had only a qualitative aspect for a long time. In the second half of the twentieth century, almost simultaneously with the development of a risk-based approach in the field of technological security, there was an interest in understanding uncertainty, its origin and typification. We are indebted to metrology for giving uncertainty a quantitative aspect, in which, instead of the measurement error paradigm, a measurement uncertainty paradigm was developed, and approaches (partly controversial) to its quantitative assessment were proposed. Uncertainty is an attribute of any data obtained both experimentally or theoretically (currently, usually by mathematical modeling). In the field of experimental research, specifying the uncertainty interval of the result has long been a scientific standard and routine. The time has come to make it mandatory for the results of theoretical research. To date, three alternative methods of quantitative estimation of uncertainty have been developed: probabilistic, fuzzy and interval methods. Methods for leveling the negative features of its initial «naive» version have been proposed in modern interval analysis. It seems to be the most promising method of quantifying uncertainty of the results of mathematical modeling today.