Effective decision making in changeable spaces, covering and discovering processes: a habitual domain approach

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 propos...

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Main Authors: Larbani, Moussa, Yu, Po-Lung
Other Authors: Guo, Peijun
Format: Article
Language:English
English
Published: Springer Berlin Heidelberg 2014
Subjects:
Online Access:http://irep.iium.edu.my/41677/
http://irep.iium.edu.my/41677/
http://irep.iium.edu.my/41677/
http://irep.iium.edu.my/41677/1/41677_Effective%20decision%20making_complete.pdf
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spelling iium-416772017-09-19T05:38:53Z http://irep.iium.edu.my/41677/ Effective decision making in changeable spaces, covering and discovering processes: a habitual domain approach Larbani, Moussa Yu, Po-Lung HB131 Methodology.Mathematical economics. Quantitative methods 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. Springer Berlin Heidelberg Guo, Peijun Pedrycz, Witold 2014 Article PeerReviewed application/pdf en http://irep.iium.edu.my/41677/1/41677_Effective%20decision%20making_complete.pdf application/pdf en http://irep.iium.edu.my/41677/2/41677_Effective%20decision%20making_scopus.pdf Larbani, Moussa and Yu, Po-Lung (2014) Effective decision making in changeable spaces, covering and discovering processes: a habitual domain approach. Studies in Computational Intelligence, 502. pp. 131-161. ISSN 1860-949X E-ISSN 1860-9503 https://link.springer.com/chapter/10.1007%2F978-3-642-39307-5_7 10.1007/978-3-642-39307-5_7
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
English
topic HB131 Methodology.Mathematical economics. Quantitative methods
spellingShingle HB131 Methodology.Mathematical economics. Quantitative methods
Larbani, Moussa
Yu, Po-Lung
Effective decision making in changeable spaces, covering and discovering processes: a habitual domain approach
description 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.
author2 Guo, Peijun
author_facet Guo, Peijun
Larbani, Moussa
Yu, Po-Lung
format Article
author Larbani, Moussa
Yu, Po-Lung
author_sort Larbani, Moussa
title Effective decision making in changeable spaces, covering and discovering processes: a habitual domain approach
title_short Effective decision making in changeable spaces, covering and discovering processes: a habitual domain approach
title_full Effective decision making in changeable spaces, covering and discovering processes: a habitual domain approach
title_fullStr Effective decision making in changeable spaces, covering and discovering processes: a habitual domain approach
title_full_unstemmed Effective decision making in changeable spaces, covering and discovering processes: a habitual domain approach
title_sort effective decision making in changeable spaces, covering and discovering processes: a habitual domain approach
publisher Springer Berlin Heidelberg
publishDate 2014
url http://irep.iium.edu.my/41677/
http://irep.iium.edu.my/41677/
http://irep.iium.edu.my/41677/
http://irep.iium.edu.my/41677/1/41677_Effective%20decision%20making_complete.pdf
http://irep.iium.edu.my/41677/2/41677_Effective%20decision%20making_scopus.pdf
first_indexed 2023-09-18T20:59:36Z
last_indexed 2023-09-18T20:59:36Z
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