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A Model For Optimal Investment Project Choice Using Fuzzy Probability

Author

Listed:
  • Joan Carles FERRER-COMALAT

    (Department of Business Administration, University of Girona, Spain)

  • Salvador LINARES-MUSTAROS

    (Department of Business Administration, University of Girona, Spain)

  • Dolors COROMINAS-COLL

    (Department of Business Administration, University of Girona, Spain)

Abstract

In this paper we present a model for classifying exclusive investments. The model uses Bellman and Zadeh’s decision-making criterion, determining the degree of convergence when the objective is to maximize the net present value of the project under the constraint of minimizing risk. The original aspect of this work consists in incorporating uncertainty into the model by considering variables such as project life, net income and capitalization rate as uncertain in order to determine net present value and risk. The concept of a fuzzy event is used to calculate the net present value and assess the risk of each investment project. This allows us to establish the degree to which a project is a good investment, understanding this as a fuzzy event and establishing the degree to which a project has a high net present value, understood as another fuzzy event.

Suggested Citation

  • Joan Carles FERRER-COMALAT & Salvador LINARES-MUSTAROS & Dolors COROMINAS-COLL, 2016. "A Model For Optimal Investment Project Choice Using Fuzzy Probability," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(4), pages 187-203.
    • Handle: RePEc:cys:ecocyb:v:50:y:2016:i:4:p:187-203
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    References listed on IDEAS

    as
    1. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    2. Remer, Donald S. & Nieto, Armando P., 1995. "A compendium and comparison of 25 project evaluation techniques. Part 1: Net present value and rate of return methods," International Journal of Production Economics, Elsevier, vol. 42(1), pages 79-96, November.
    3. Mehdi KESHAVARZ GHORABAEE & Edmundas Kazimieras ZAVADSKAS & Maghsoud AMIRI & Jurgita ANTUCHEVICIENE, 2016. "A New Method Of Assessment Based On Fuzzy Ranking And Aggregated Weights (Afraw) For Mcdm Problems Under Type-2 Fuzzy Environment," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(1), pages 39-68.
    4. Remer, Donald S. & Nieto, Armando P., 1995. "A compendium and comparison of 25 project evaluation techniques. Part 2: Ratio, payback, and accounting methods," International Journal of Production Economics, Elsevier, vol. 42(2), pages 101-129, December.
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    Cited by:

    1. Joan Carles Ferrer-Comalat & Dolors Corominas-Coll & Salvador Linares-Mustarós, 2021. "A Fuzzy Economic Dynamic Model," Mathematics, MDPI, vol. 9(8), pages 1-15, April.

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    More about this item

    Keywords

    fuzzy logic; fuzzy set theory; decision-making; fuzzy arithmetic; probability of a fuzzy event; investment.;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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