Risk Aversion with Mixed Parameters

Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 274)

Abstract

In several cases of economic and social world we deal with situations of uncertainty with several risk components. Some of these risk parameters can be described probabilistically, and others possibilistically. In the first case parameters will be considered random variables and in the second case possibilistic distributions ( in particular fuzzy numbers). From here the idea of mixed vector appears, in which some risk components are random variables and others are fuzzy numbers. If all components are random variables, we deal with a probabilistic risk vector, and if all components are fuzzy numbers, we deal with a possibilistic risk vector. There is a rich literature dedicated to multidimensional risk aversion [27], [49], [50], [54]. Most of these papers try to generalize the theory of risk aversion developed by Arrow–Pratt in the unidimensional case. There are although notable differences between the multidimensional case and the unidimensional case. The multidimensional risk premium is not unique, which produces complications in comparing the risk premiums of different agents. Paper [36] is an attempt to treat risk aversion in case of possibilistic vectors.

Keywords

Utility Function Weighting Function Risk Aversion Fuzzy Number Grid Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Economic CyberneticsAcademy of Economic StudiesBucharestRomania

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