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Author: Maria Letizia Guerra
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 15
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Book Description
Value at Risk (VaR) has become a crucial measure for decision making in risk management over the last thirty years and many estimation methodologies address the finding of the best performing measure at taking into account unremovable uncertainty of real financial markets. One possible and promising way to include uncertainty is to refer to the mathematics of fuzzy numbers and to its rigorous methodologies which offer flexible ways to read and to interpret properties of real data which may arise in many areas. The paper aims to show the effectiveness of two distinguished models to account for uncertainty in VaR computation; initially, following a non parametric approach, we apply the Fuzzy-transform approximation function to smooth data by capturing fundamental patterns before computing VaR.
Author: Maria Letizia Guerra
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 15
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Book Description
Value at Risk (VaR) has become a crucial measure for decision making in risk management over the last thirty years and many estimation methodologies address the finding of the best performing measure at taking into account unremovable uncertainty of real financial markets. One possible and promising way to include uncertainty is to refer to the mathematics of fuzzy numbers and to its rigorous methodologies which offer flexible ways to read and to interpret properties of real data which may arise in many areas. The paper aims to show the effectiveness of two distinguished models to account for uncertainty in VaR computation; initially, following a non parametric approach, we apply the Fuzzy-transform approximation function to smooth data by capturing fundamental patterns before computing VaR.
Author: Didier Dubois
Publisher: Springer Science & Business Media
ISBN: 1468452878
Category : Mathematics
Languages : en
Pages : 274
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Book Description
In the evolution of scientific theories, concern with uncertainty is almost invariably a concomitant of maturation. This is certainly true of the evolution· of physics, economics, operations research, communication sciences, and a host of other fields. And it is true of what has been happening more recently in the area of artificial intelligence, most notably in the development of theories relating to the management of uncertainty in knowledge-based systems. In science, it is traditional to deal with uncertainty through the use of probability theory. In recent years, however, it has become increasingly clear that there are some important facets of uncertainty which do not lend themselves to analysis by classical probability-based methods. One such facet is that of lexical elasticity, which relates to the fuzziness of words in natural languages. As a case in point, even a simple relation X, Y, and Z, expressed as if X is small and Y is very large then between Z is not very small, does not lend itself to a simple interpretation within the framework of probability theory by reason of the lexical elasticity of the predicates small and large.
Author: Gülin Feryal Can
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 32
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Book Description
Traditional risk assessment (RA) methodologies cannot model vagueness in risk and cannot prioritize corrective-preventive measures (CPMs) by considering effectiveness of those on risk types (RTs).
Author: Bingyuan Cao
Publisher: Springer Science & Business Media
ISBN: 3642036643
Category : Technology & Engineering
Languages : en
Pages : 1687
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Book Description
This book is the proceedings of the Third International Conference on Fuzzy Information and Engineering (ICFIE 2009) held in the famous mountain city Chongqing in Southwestern China, from September 26-29, 2009. Only high-quality papers are included. The ICFIE 2009, built on the success of previous conferences, the ICFIE 2007 (Guangzhou, China), is a major symposium for scientists, engineers and practitioners in the world to present their updated results, ideas, developments and applications in all areas of fuzzy information and engineering. It aims to strengthen relations between industry research laboratories and universities, and to create a primary symposium for world scientists in fuzzy fields as follows: Fuzzy Information; Fuzzy Sets and Systems; Soft Computing; Fuzzy Engineering; Fuzzy Operation Research and Management; Artificial Intelligence; Fuzzy Mathematics and Systems in Applications, etc.
Author: Hamidreza Seiti
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 15
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Book Description
Fuzzy sets were initially proposed to address ambiguities and uncertainties. However, in certain cases, the fuzzy sets show some degree of uncertainty and risk, when the available data are either obtained from unreliable sources or related to future events. To solve this problem, the R-numbers methodology has been recently developed as a powerful approach to model the risk of fuzzy sets and numbers due to risk factors. In R-numbers, only the variability of x values has been taken into account in risk modeling of the fuzzy sets, but not their membership function.
Author:
Publisher:
ISBN:
Category :
Languages : pt-BR
Pages :
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Book Description
W. Kent Muhlbauer fez uma identificação detalhada de aproximadamente 300 diferentes condições que influenciam a avaliação do Risco em uma tubulação e propôs um sistema depontuação que é conhecido mundialmente como o método de Muhlbauer. O método de Muhlbauer avalia as diversas variáveis que influenciam no Risco de dutoviasmediante a atribuição de valores quantitativos. No entanto, sendo um método qualitativo, estas variáveis não podem ser informadas através de valores exatos. Estas variáveis podem ser tratadas como provenientes de distribuições randômicas. Entretanto, identificar as distribuições randômicas pode exigir muito esforço. Sendo assim, em vez de assumir que asavaliações das variáveis têm distribuições randômicas, pode-se considerar que têm distribuições dadas por conjuntos e números nebulosos. No presente trabalho, os valores numéricos presentes no modelo Muhlbauer passaram a seradmitidos como não determinísticos, admitindo uma incerteza. Esta incerteza depende do engenheiro especialista avaliador do Risco. Para incorporar esta incerteza no cálculo do valor do Risco procurou-se trabalhar com os conjuntos e números nebulosos. Na análise das incertezas mediante os conjuntos nebulosos, requer-se definir as variáveis lingüísticas (VL), os valores lingüísticos das VL de saída e entrada, as funções de pertinência, além das regras nebulosas. Com estes, um Sistema de Lógica Fuzzy (SLF) é implementado com base na inferência Mamdani. No caso dos números nebulosos, estes admitem um valor mais provável e uma incerteza. Esta incerteza é avaliada por uma função de pertinêncianormalizada. Operações de soma, subtração, multiplicação e divisão são possíveis para os números fuzzy. Como resultado final torna-se possível encontrar-se não só um número quedefine o Risco como também a incerteza (faixa de valores) que este Risco pode ter, que é uma função das incertezas das avaliações individuais das variáveis. VI O presente trabalho propõe um modelo básico de Gerenciamento de Risco (GR) e Análise de Integridade Estrutural (AIE) para dutos com corrosão externa. Para isso, os resultados de umaAIE nível I aplicado aos dutos é relacionada com a metodologia de análise de Risco de W. Kent Muhlbauer, através de uma Matriz de Risco. A partir de uma AIE nível I o cálculo da Vida Residual (T*) é avaliada. A T* é comparadacom uma Vida Desejada (VD), a qual é obtida da matriz de risco. Se T* é menor que a VD, recomenda-se fazer uma AIE nível II. Se T* é maior que VD determina-se um Tempo deInspeção (TI) baseado na análise de confiabilidade.
Author: Begoña Vitoriano
Publisher: Springer Science & Business Media
ISBN: 9491216740
Category : Computers
Languages : en
Pages : 333
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Book Description
Disaster management is a process or strategy that is implemented when any type of catastrophic event takes place. The process may be initiated when anything threatens to disrupt normal operations or puts the lives of human beings at risk. Governments on all levels as well as many businesses create some sort of disaster plan that make it possible to overcome the catastrophe and return to normal function as quickly as possible. Response to natural disasters (e.g., floods, earthquakes) or technological disaster (e.g., nuclear, chemical) is an extreme complex process that involves severe time pressure, various uncertainties, high non-linearity and many stakeholders. Disaster management often requires several autonomous agencies to collaboratively mitigate, prepare, respond, and recover from heterogeneous and dynamic sets of hazards to society. Almost all disasters involve high degrees of novelty to deal with most unexpected various uncertainties and dynamic time pressures. Existing studies and approaches within disaster management have mainly been focused on some specific type of disasters with certain agency oriented. There is a lack of a general framework to deal with similarities and synergies among different disasters by taking their specific features into account. This book provides with various decisions analysis theories and support tools in complex systems in general and in disaster management in particular. The book is also generated during a long-term preparation of a European project proposal among most leading experts in the areas related to the book title. Chapters are evaluated based on quality and originality in theory and methodology, application oriented, relevance to the title of the book.
Author: Chongfu Huang
Publisher: CRC Press
ISBN: 0203771478
Category : Computers
Languages : en
Pages : 965
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Book Description
In this present internet age, risk analysis and crisis response based on information will make up a digital world full of possibilities and improvements to people‘s daily life and capabilities. These services will be supported by more intelligent systems and more effective decisionmaking. This book contains all the papers presented at the 4th Inter
Author: Yasuo Narukawa
Publisher: Springer Science & Business Media
ISBN: 3642048196
Category : Mathematics
Languages : en
Pages : 382
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Book Description
This volume contains papers presented at the 6th International Conference on ModelingDecisionsforArti?cialIntelligence(MDAI2009),heldinAwajiIsland, Japan, November 30 – December 2, 2009. This conference followed MDAI 2004 (Barcelona, Catalonia), MDAI 2005 (Tsukuba, Japan), MDAI 2006 (Tarragona, Catalonia), MDAI 2007 (Kitakyushu, Japan), and MDAI 2008 (Sabadell, C- alonia) with proceedings also published in the LNAI series (Vols. 3131, 3558, 3885, 4617, and 5285). The aim of this conference was to provide a forum for researchers to d- cuss the theory and tools for modeling decisions, as well as applications that encompass decision-making processes and information-fusion techniques. The organizers received 61 papers from 15 di?erent countries, from Asia, Europe,andAmerica,28ofwhicharepublishedinthis volume.Eachsubmission received at least two reviews from the Program Committee and a few external reviewers. We would like to express our gratitude to them for their work. The plenary talks presented at the conference are also included in this volume. The conference was supported by the Commemorative Organization for The JapanWorldExposition'70,the TsutomuNakauchiFoundation,HyogoInter- tional Association, the Institute of Systems, Control and Information Engineers (ISCIE),the OperationsResearchSocietyofJapan(ORSJ),the UNESCO Chair in Data Privacy, the Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT), the Catalan Association for Arti?cial Intelligence (ACIA), the Eu- pean Society for Fuzzy Logic and Technology (EUSFLAT), and the Spanish MEC (ARES - CONSOLIDER INGENIO 2010 CSD2007-00004).
Author: Yong Fang
Publisher: Springer Science & Business Media
ISBN: 3540779264
Category : Business & Economics
Languages : en
Pages : 170
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Book Description
Most of the existing portfolio selection models are based on the probability theory. Though they often deal with the uncertainty via probabilistic - proaches, we have to mention that the probabilistic approaches only partly capture the reality. Some other techniques have also been applied to handle the uncertainty of the ?nancial markets, for instance, the fuzzy set theory [Zadeh (1965)]. In reality, many events with fuzziness are characterized by probabilistic approaches, although they are not random events. The fuzzy set theory has been widely used to solve many practical problems, including ?nancial risk management. By using fuzzy mathematical approaches, quan- tative analysis, qualitative analysis, the experts’ knowledge and the investors’ subjective opinions can be better integrated into a portfolio selection model. The contents of this book mainly comprise of the authors’ research results for fuzzy portfolio selection problems in recent years. In addition, in the book, the authors will also introduce some other important progress in the ?eld of fuzzy portfolio optimization. Some fundamental issues and problems of po- folioselectionhavebeenstudiedsystematicallyandextensivelybytheauthors to apply fuzzy systems theory and optimization methods. A new framework for investment analysis is presented in this book. A series of portfolio sel- tion models are given and some of them might be more e?cient for practical applications. Some application examples are given to illustrate these models by using real data from the Chinese securities markets.
