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Decision Making and Optimization in Changeable Spaces, a New Paradigm

Author

Listed:
  • Moussa Larbani

    (IIUM university)

  • Po Lung Yu

    (National Chiao Tung University
    University of Kansas)

Abstract

This paper proposes a new decision making/optimization paradigm, the decision making/optimization in changeable spaces (DM/OCS). The unique feature of DM/OCS is that it incorporates human psychology and its dynamics as part of the decision making process and allows the restructuring of the decision parameters. DM/OCS is based on Habitual Domain theory, the decision parameters, the concept of competence set, and the mental operators 7-8-9 principles of deep knowledge. The covering and discovering processes are formulated as DM/OCS problems. Some illustrative examples of challenging problems that cannot be solved by traditional decision making/optimization techniques are formulated as DM/OCS problems and solved. In addition, some directions of research related to innovation dynamics, management, artificial intelligence, artificial and e-economics, scientific discovery, and knowledge extraction are provided in the conclusion.

Suggested Citation

  • Moussa Larbani & Po Lung Yu, 2012. "Decision Making and Optimization in Changeable Spaces, a New Paradigm," Journal of Optimization Theory and Applications, Springer, vol. 155(3), pages 727-761, December.
    • Handle: RePEc:spr:joptap:v:155:y:2012:i:3:d:10.1007_s10957-012-0103-9
    DOI: 10.1007/s10957-012-0103-9
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    References listed on IDEAS

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    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. Yu, Po L. & Zhang, Dazhi, 1990. "A foundation for competence set analysis," Mathematical Social Sciences, Elsevier, vol. 20(3), pages 251-299, December.
    3. Masatoshi Sakawa & Ichiro Nishizaki & Hideki Katagiri, 2011. "Fuzzy Multiobjective Stochastic Programming," International Series in Operations Research & Management Science, in: Fuzzy Stochastic Multiobjective Programming, chapter 0, pages 49-99, Springer.
    4. M. Larbani & P. L. Yu, 2009. "Two-Person Second-Order Games, Part 2: Restructuring Operations to Reach a Win-Win Profile," Journal of Optimization Theory and Applications, Springer, vol. 141(3), pages 641-659, June.
    5. P. L. Yu & M. Larbani, 2009. "Two-Person Second-Order Games, Part 1: Formulation and Transition Anatomy," Journal of Optimization Theory and Applications, Springer, vol. 141(3), pages 619-639, June.
    6. Marco Li Calzi & Lucia Milone & Paolo Pellizzari (ed.), 2010. "Progress in Artificial Economics," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-642-13947-5, December.
    7. Masatoshi Sakawa & Ichiro Nishizaki & Hideki Katagiri, 2011. "Fuzzy Stochastic Multiobjective Programming," International Series in Operations Research and Management Science, Springer, number 978-1-4419-8402-9, September.
    8. M. Larbani & P. L. Yu, 2011. "n-Person Second-Order Games: A Paradigm Shift in Game Theory," Journal of Optimization Theory and Applications, Springer, vol. 149(3), pages 447-473, June.
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    Cited by:

    1. Moussa Larbani & Po Lung Yu, 2020. "Empowering Data Mining Sciences by Habitual Domains Theory, Part II: Reaching Wonderful Solutions," Annals of Data Science, Springer, vol. 7(4), pages 549-580, December.
    2. T. Q. Bao & B. S. Mordukhovich & A. Soubeyran, 2015. "Variational Analysis in Psychological Modeling," Journal of Optimization Theory and Applications, Springer, vol. 164(1), pages 290-315, January.
    3. G. C. Bento & A. Soubeyran, 2015. "Generalized Inexact Proximal Algorithms: Routine’s Formation with Resistance to Change, Following Worthwhile Changes," Journal of Optimization Theory and Applications, Springer, vol. 166(1), pages 172-187, July.

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