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Distributed Computing and Artificial Intelligence pp 61–68Cite as

Optimal Saving and Prudence in a Possibilistic Framework

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 217))

Abstract

In this paper we study the optimal saving problem in the framework of possibility theory. The notion of possibilistic precautionary saving is introduced as a measure of the way the presence of risk (represented by a fuzzy number) influences a consumer in establishing the level of optimal saving. The equivalence between the prudence condition (in the sense of Kimball) and a positive possibilistic precautionary saving is proved. Some relations between possibilistic risk aversion, prudence and possibilistic precautionary saving are established.

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Author information

Authors and Affiliations

  1. Faculty of Business Administration, Department of Sociology, Cordoba University, Cordoba, Spain

    Ana Maria Lucia Casademunt

  2. Department of Quantitative Methods, Universidad Loyola–Andalucia, Cordoba, Spain

    Irina Georgescu

  3. Department of Economic Cybernetics, Academy of Economic Studies, Bucharest, Romania

    Irina Georgescu

Authors
  1. Ana Maria Lucia Casademunt
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  2. Irina Georgescu
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Corresponding author

Correspondence to Ana Maria Lucia Casademunt .

Editor information

Editors and Affiliations

  1. Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka, Japan

    Sigeru Omatu

  2. Department of Computing Science, University of Minho, Braga, Portugal

    José Neves

  3. Dept. of Computing Science and Control, University of Salamanca, Salamanca, Spain

    Juan M. Corchado Rodriguez

  4. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Juan F Paz Santana

  5. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Sara Rodríguez Gonzalez

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© 2013 Springer International Publishing Switzerland

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Casademunt, A.M.L., Georgescu, I. (2013). Optimal Saving and Prudence in a Possibilistic Framework. In: Omatu, S., Neves, J., Rodriguez, J., Paz Santana, J., Gonzalez, S. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-00551-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-00551-5_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-00550-8

  • Online ISBN: 978-3-319-00551-5

  • eBook Packages: EngineeringEngineering (R0)

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