Browsing by Author "Larbani, Moussa"

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A Novel Method for Fuzzy Measure Identification
(2011-03-01)
Larbani, Moussa
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Huang, Chi-Yo
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Tzeng, Gwo-Hshiung
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交大名義發表
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National Chiao Tung University
Fuzzy measure and Choquet integral are effective tools for handling complex multiple criteria decision making (MCDM) problems in which criteria are inter-dependent. The identification of a fuzzy measure requires the determination of 2(n) - 2 values when the number of criteria is n. The complexity of this problem increases exponentially, which makes it practically very difficult to solve. Many methods have been proposed to reduce the number of values to be determined including the introduction of new special fuzzy measures like the lambda -fuzzy measures. However, manipulations of the proposed methods are difficult from the aspects of high data complexity as well as low computation efficiency. Thus, this paper proposed a novel fuzzy measure identification method by reducing the data complexity to n(n - 1)/2 and enhancing the computation efficiency by leveraging a relatively small number of variables and constraints for linear programming. The proposed method was developed based on the evaluation of pair-wise additivity degrees or interdependence coefficients between the criteria. Depending on the information being provided by decision-makers on the individual density of each criterion, the fuzzy measure can be constructed by solving a simple system of linear inequalities or a linear programming problem. This novel method is validated through a supplier selection problem which occurs frequently in real-world decision-making problems. Validation results demonstrate that the newly-proposed method can model real-world MCDM problems successfully.
Decision Making and Optimization in Changeable Spaces, a New Paradigm
(2012-12-01)
Larbani, Moussa
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Yu, Po Lung
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
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.
REACHING WIN-WIN STATE OF MIND IN GAMES, FORMULATION AND SOLUTION
(2009)
Larbani, Moussa
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Yu, Po Lung
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
Human psychology determines his/her action and behavior. This fact has not been fully incorporated in game theory. This paper intends to incorporate human psychology in formulating games as people play them. Using Habitual Domains and Markov chains theory, we present a new model for describing the evolution of the states of mind of players over time, the two person second order game. We introduce the concept of focal mind profile as well as the solution concept of win-win mind profile. Under some suitable assumptions, the restructuring game to reach a win-win mind profile can be formulated into a discrete optimal control problem. We illustrate the formulation with an example.