Abstract
In this paper we open a new avenue for applications of the rough set concept to decision support. We consider the classical problem of decision under risk proposing a rough set model based on stochastic dominance. We start with the case of traditional additive probability distribution over the set of states of the world, however, the model is rich enough to handle non-additive probability distributions and even qualitative ordinal distributions. The rough set approach gives a representation of decision maker’s preferences in terms of “if..., then...” decision rules induced from rough approximations of sets of exemplary decisions.
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© 2001 Springer-Verlag Berlin Heidelberg
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Greco, S., Matarazzo, B., Slowinski, R. (2001). Rough Set Approach to Decisions under Risk. In: Ziarko, W., Yao, Y. (eds) Rough Sets and Current Trends in Computing. RSCTC 2000. Lecture Notes in Computer Science(), vol 2005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45554-X_19
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DOI: https://doi.org/10.1007/3-540-45554-X_19
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43074-2
Online ISBN: 978-3-540-45554-7
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