Effective Decision Making in Changeable Spaces, Covering and Discovering Processes: A Habitual Domain Approach

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 502)

Abstract

This chapter proposes a model of covering and discovering processes for solving non-trivial decision making problems in changeable spaces, which encompass most of the decision making problems that a person or a group of people encounter at individual, family, organization and society levels. The proposed framework fully incorporates two important aspects of the real-decision making process that are not fully considered in most of the traditional decision theories: the cognitive aspect and the psychological states of the decision makers and their dynamics. Moreover, the proposed model does not assume that the set of alternatives, criteria, outcomes, preferences, etc. are fixed or depend on some probabilistic and/or fuzzy parameter with known probability distribution and/or membership function. The model allows the creation of new ideas and restructuring of the decision parameters to solve problems. Therefore, it is called decision making/optimization in changeable spaces (DM/OCS). 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. Some illustrative examples of challenging problems that cannot be solved by traditional decision making/optimization techniques are formulated as DM/OCS problems and solved. Finally, some directions of research are provided in conclusion.

Keywords

Habitual domains Decision making Changeable spaces Parameters Covering Discovering Competence set Decision blinds Decision traps 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Business AdministrationIIUM UniversityKuala LumpurMalaysia
  2. 2.Institute of Information ManagementNational Chiao Tung UniversityHsinchuTaiwan
  3. 3.School of BusinessUniversity of KansasLawrenceUSA

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