Abstract
This paper deals with solutions for numeric evaluation of risks containing several different risk factors assessed by experts. The proposed methods can be used to assess the risks and obtain the risk scores in different industries, including financial industry, but they are also suitable for assessing risks in other areas, e.g. project management. While risk is usually considered as a function of probability and impact with strong quantitative background, there are many practical cases when only qualitative risk assessment based on expert opinions can be used. At the same time there are still requirements and needs for applying numerical values and mathematical models to such qualitative assessments. We consider the options for aggregation of risk levels for corresponding risk factors and obtaining consolidated risk level using transparent and self-explanatory approach. The proposed models are constructed using maximum t-conorm and Łukasiewicz t-conorm. Practical example is provided for calculation of consolidated risk score.
Partially supported by the project No LZP-2018/2-0338 ‘Development of fuzzy logic based technologies for risk assessment by means of relation-grounded aggregation’ of the Latvian Council of Science.
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Krastiņš, M. (2019). On Aggregation of Risk Levels Using T-Conorms. In: Torra, V., Narukawa, Y., Pasi, G., Viviani, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2019. Lecture Notes in Computer Science(), vol 11676. Springer, Cham. https://doi.org/10.1007/978-3-030-26773-5_10
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DOI: https://doi.org/10.1007/978-3-030-26773-5_10
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