• A. Vališevskis University of Latvia



risk analysis, fuzzy logic, f-granules, reasoning under uncertainty, entropy


In the real life almost all of the decisions that we have to make incorporate uncertainty about the future events. Assessment of the uncertainty and, thus, the risk that is inherent in these decisions models can be critical. It is even truer if we are talking about the possibility of negative impact on the environment. It is very important to assess all the environmental risks in a project if there is any hazard to the environment. In this paper the possibility of using granular information is considered. The main advantage of the granular information is that it can be used to assess risks in situations when information about future events is incomplete and imprecise. Moreover, we can use natural language to describe the problem area, as granular information paradigm uses both fuzzy and probabilistic information. We propose to use entropy as the measure of uncertainty. However, the definition of entropy should be generalised, as values of probabilities, upon which the calculation of entropy is based on, are interval-valued. We propose several possibilities of generalizing the definition of entropy. Furthermore, we analyse these approaches to see whether the additivity feature holds for the generalized entropy.


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Yager R.R. Fuzzy Sets, Probabilities and Decision, Journal of Cybernetics, 10:1-18, 1980, pp. 1-18.

Vališevskis A. Granular-Information-Based Decisions, to be published in the proceedings of Information Society and Modern Business, Ventspils, Latvia, January 30 - February 1, 2003.

Shannon C.E., Weaver W. The mathematical theory of communication, Urbana: University of Illinois Press, 1949.

Zadeh L.A. Fuzzy Sets and Systems. Proceedings of Symposium on System Theory, Polytechnic Institute of Brooklyn, 1965, pp. 29-37.

Zadeh L.A. Fuzzy Logic, Computer Magazine, No.4, 1988, pp. 83-93.

Zadeh L.A. Fuzzy Sets and Information Granularity, Advances in Fuzzy Set Theory and Applications, Editors: Gupta M.M., Ragade R.K et al., North-Holland Publishing Company, 1979, pp. 3-18.

Vališevskis A., Borisov A., ANGIE: Adaptive Network for Granular Information and Evidence Processing, Fifth International Conference on Application of Fuzzy Systems and Soft Computing ICAFS-2002, Milan, Italy, September 17-19, 2002, pp. 166-173.




How to Cite

A. Vališevskis, “GRANULAR-INFORMATION-BASED RISK ANALYSIS IN UNCERTAIN SITUATIONS”, ETR, vol. 1, pp. 385–391, Jun. 2006, doi: 10.17770/etr2003vol1.2021.