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Fuzzy Logic and Intelligent Agents: Towards the Next Step of Capital Budgeting Decision Support

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Author Info

  • Collan, Mikael
  • Liu, Shuhua

Abstract

The economic life of large investments is long and thus necessitates constant dynamic managerial actions. To be able to act in an optimal way in the dynamic management of large investments managers need the support of advanced analytical tools. They need to have constant access to information about the real time situation of the investment, as well as, access to up-to-date information about changes in the business environment. What is more challenging, they need to integrate qualitative information into quantitative analysis process, and to integrate foresight information into the capital budgeting process. In this paper we will look at how emerging soft computing technologies, specifically fuzzy logic and intelligent agents, will help to provide a better support in such a context and then to frame a support system that will make an integrated application of the aforementioned technologies. We will first develop a holistic framework for an agent-facilitated capital budgeting system using a fuzzy real option approach. We will then discuss how intelligent agents can be applied to collect decision information, both qualitative and quantitative, and to facilitate the integration of foresight information into capital budgeting process. Integration of qualitative information into quantitative analysis process will be discussed. Methods for integrating qualitative and quantitative information into fuzzy numbers, as well as, methods for using the fuzzy numbers in capital budgeting will be presented. A specification of how the agents can be constructed is elaborated.

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Bibliographic Info

Paper provided by IAMSR, Åbo Akademi in its series Working Papers with number 398.

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Length: 1-18
Date of creation: 2002
Date of revision:
Handle: RePEc:amr:wpaper:398

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Web page: http://iamsr.abo.fi/

Related research

Keywords: Intelligent Agents; Fuzzy Sets; Capital Budgeting; Real Options; DSS;

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Cited by:
  1. Rossi De Mio, Ruggero, 2003. "Emotional Intelligence in Virtual and Face-to-Face Communication: A Pilot Laboratory Experiment," Working Papers 473, IAMSR, Åbo Akademi.
  2. Collan, Mikael, 2004. "Fuzzy Real Investment Valuation Model for Giga-Investments, and a Note on Giga-Investment Lifecycle and Valuation," MPRA Paper 4329, University Library of Munich, Germany.
  3. Liu, Shuhua, 2004. "Theory and Methods on Text Summarization," Working Papers 474, IAMSR, Åbo Akademi.
  4. Tétard, Franck & Patokorpi, Erkki & Carlsson, Joanna, 2008. "A Conceptual Framework for Mobile Learning," Working Papers 464, IAMSR, Åbo Akademi.
  5. Ling-Zhong Lin & Tsuen-Ho Hsu, 2012. "A modular fuzzy inference system approach in integrating qualitative and quantitative analysis of store image," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(6), pages 1847-1864, October.
  6. Lindholm, Christer K. & Liu, Shuhua, 2003. "Fuzzy Clustering Analysis of the Early Warning Signs of Financial Crisis," Working Papers 472, IAMSR, Åbo Akademi.
  7. Georgescu, Irina, 2008. "Revealed Preference Indicators for Fuzzy Choice Functions," Working Papers 475, IAMSR, Åbo Akademi.
  8. Collan, Mikael, 2004. "Giga-Investments: Modelling the Valuation of Very Large Industrial Real Investments," MPRA Paper 4328, University Library of Munich, Germany.

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