Nasir Bedewi Siraj, Aminah Robinson Fayek and Mohamed M. G. Elbarkouky
Most decision-making problems in construction are complex and difficult to solve, as they involve multiple criteria and multiple decision makers in addition to subjective…
Abstract
Most decision-making problems in construction are complex and difficult to solve, as they involve multiple criteria and multiple decision makers in addition to subjective uncertainties, imprecisions and vagueness surrounding the decision-making process. In many instances, the decision-making process is based on linguistic terms rather than numerical values. Hence, structured fuzzy consensus-reaching processes and fuzzy aggregation methods are instrumental in multi-criteria group decision-making (MCGDM) problems for capturing the point of view of a group of experts. This chapter outlines different fuzzy consensus-reaching processes and fuzzy aggregation methods. It presents the background of the basic theory and formulation of these processes and methods, as well as numerical examples that illustrate their theory and formulation. Application areas of fuzzy consensus reaching and fuzzy aggregation in the construction domain are identified, and an overview of previously developed frameworks for fuzzy consensus reaching and fuzzy aggregation is provided. Finally, areas for future work are presented that highlight emerging trends and the imminent needs of fuzzy consensus reaching and fuzzy aggregation in the construction domain.
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The theory of possibility (Zadeh, Sugeno) and the theory of relative information (Jumarie) both aim to deal with the meaning of information, but their mathematical frameworks are…
Abstract
The theory of possibility (Zadeh, Sugeno) and the theory of relative information (Jumarie) both aim to deal with the meaning of information, but their mathematical frameworks are quite different. In the first approach, possibility is described either by fuzziness (Zadeh) or by generalized measures (Sugeno), and in the second, possibility is obtained as the result of observing probability via an observation process with informational invariance. Shows that a combination of (classical) information theory with generalized maximum likelihood via geometric programming exhibits a link between relative information, fuzziness and possibility. Some consequences are outlined.
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In this paper, one combines information theory, and more especially the concept of entropy, with the statistical theory of decision to derive new criteria for pattern recognition…
Abstract
In this paper, one combines information theory, and more especially the concept of entropy, with the statistical theory of decision to derive new criteria for pattern recognition. A generalized definition of entropy is considered as a risk function, and the generalized decision rules so obtained contain the family of the Bayesian decisions as special cases. These criteria may help to check the results obtained by usual techniques; they can be used in adaptive and learning systems, and more generally they can be useful in cybernetic systems.