The Role of Fuzziness in Decision Making

Montero, Javier; López, Victoria; Gómez, Daniel

doi:10.1007/978-3-540-71258-9_16

Cited by 40 publications

(9 citation statements)

References 31 publications

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“…The practical relevance requirement specification processes is a standard within classical software development. This argument also applies to a fuzzy context, see [4]. The introduction of fuzzy techniques is a must whenever software process deals with some kind of fuzzy uncertainty.…”

Section: Fuzzy Specificationmentioning

confidence: 99%

Fuzzy Logic in Real Estate Valuation

López

Freitas

Montero

2008

Computational Intelligence in Decision and Control

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Real estate valuation is a complex problem where experts make their job subject to an input information that is mostly fuzzy in nature. In this paper we present a real estate valuation model that allows the management of such a fuzzy information. In particular, we realize that our reference set of experts considers at a first stage a division of all potential clients into four main clusters, according to their preferences. An inference engine is then designed in order to obtain the degree as valuation of a real state described by the client and their own characteristics. A relevant feature of our proposal is that we allow fuzzy specifications.

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Section: Fuzzy Specificationmentioning

confidence: 99%

Fuzzy Logic in Real Estate Valuation

López

Freitas

Montero

2008

Computational Intelligence in Decision and Control

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“…In fact, as pointed out in [11], whenever a decision has to be crisp, fuzziness does not properly fit. Fuzzy models properly fit to concept creation, concept representation and concept management, and to those strategic political decisions we make and that by definition are poorly defined since their details will be necessarily fixed depending on yet unknown circumstances (see, e.g., [5] but also [10]). As a Spanish famous poet wrote, there is no road, you make your path as you walk ("Songs", by Antonio Machado).…”

Section: Computational Intelligence In Information and Knowledge Manamentioning

confidence: 99%

Computational intelligence in decision making

Espinilla¹,

Montero²,

Rodríguez³

2014

IJCIS

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In this preface we stress the relevance of the traditional collaboration between Engineering and any field of Mathematics in order to build intelligent decision-aid tools, as it is illustrated by the twelve papers contained in this Special Issue. These papers, selected by means of a standard peer review process after an open call, offer an interesting variety of models, approaches and techniques, to be applied within different specific problems. Each paper is introduced in this preface and is developed in the subsequent article. Moreover, in this preface we also claim for a more intense collaboration between decision engineers and other fields that study human brain behavior, like Neurology, Psychology, Sociology and Linguistics. Because if we really want to procure intelligent tools for decision aid, we should start by taking the human brain as our first reference, as it is the most efficient machinery we have found in order to deal with complex, uncertain, incomplete and even apparently inconsistent information. We hope that readers will enjoy this Special Issue devoted to Computational Intelligence in Decision Making.

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“…Such a comprehensive stage represents the natural field for decision aiding [27,29]. The development of analytical representation models plays here the main role, of course including natural language but also the fusion aggregative models needed to compress information and those image representation and graphical techniques that allow a better knowledge of the structure of information and the problem itself (see, e.g., [7]).…”

Section: Decision Making Under Uncertaintymentioning

confidence: 99%