CHARACTERISTICS OF SELECTED APPROACHES OF UNCERTAINTY MODELLING IN THE CONTEXT OF MANAGEMENT SCIENCES

• Published in: Humanities and Social Sciences Vol. 26; no. 1; pp. 67 - 77
• Main Author: MIERZWIAK, Rafał
• Format: Journal Article
• Language: English
• Published: Rzeszow University of Technology 01.03.2019
• Subjects: fuzzy logic; grey systems theory; probability; rough set theory; uncertainty
• ISSN: 2300-5327; 2300-9918
• DOI: 10.7862/rz.2019.hss.7

Information uncertainty in management systems is a subject of research of many scientific disciplines. Research concerning a quantitative modelling of uncertainty verging on mathematics, information technology, and logic is extremely important. The purpose of the article is to present possibilities and limitations of the most popular approaches in uncertainty modelling in social sciences with particular emphasis on management problems. Achieving such a goal always requires accepting certain basic epistemological assumptions. Therefore, an assumption was accepted about a subjective character of information. Information is treated as a feature of a cognition process. It is not an objective element of the reality. Furthermore, there was also assumed that uncertainty is a result of interference in mapping information and processes connected with cognition of the reality by human. Starting from these assumptions, the article demonstrates the way uncertainty is modelled and perceived in terms of probability, in fuzzy logic, in rough set theory, and in grey systems theory, which has been gaining popularity in recent years. In order to compare the mentioned approaches to a maximum extent, an attempt was made to show their essence referring to a classical set theory. To this aim, mathematical formalism was used for a precise description of particular types of uncertainty. It is of great significance as in management sciences and generally in social sciences some terms, for example, fuzziness, probability or greyness are overinterpreted and used in descriptive way to pretend scientific statements.