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• Introduction.- Investment decision making based on the asymmetric hesitant fuzzy sigmoid preference relations.- Investment decision making based on the hesitant fuzzy trade-off and portfolio selection.- Investment decision making based on the hesitant fuzzy preference envelopment analysis.- Investment decision making based on the hesitant fuzzy peer-evaluation and strategy fusion.- Investment decision making based on the EHVaR measurement and tail analysis.- Conclusions.
• (source: Nielsen Book Data)

This book describes five qualitative investment decision-making methods based on the hesitant fuzzy information. They are: (1) the investment decision-making method based on the asymmetric hesitant fuzzy sigmoid preference relations, (2) the investment decision-making method based on the hesitant fuzzy trade-off and portfolio selection, (3) the investment decision-making method based on the hesitant fuzzy preference envelopment analysis, (4) the investment decision-making method based on the hesitant fuzzy peer-evaluation and strategy fusion, and (5) the investment decision-making method based on the EHVaR measurement and tail analysis.
(source: Nielsen Book Data)

• Preface.- 1 Hesitant Fuzzy Set and Its Extensions.- 2 Novel Correlation and Entropy Measures for Hesitant Fuzzy Set.- 3 Multiple Criteria Decision Making with Hesitant Fuzzy Hybrid Weighted Aggregation Operators.- 4 Hesitant Fuzzy Multiple Criteria Decision Making with Complete Weight Information.- 5 Hesitant Fuzzy Multiple Criteria Decision Making with Incomplete Weights.- 6 Decision Making with Hesitant Fuzzy Preference Relation.
• (source: Nielsen Book Data)

This book offers a comprehensive and systematic introduction to the latest research on hesitant fuzzy decision-making theory. It includes six parts: the hesitant fuzzy set and its extensions, novel hesitant fuzzy measures, hesitant fuzzy hybrid weighted aggregation operators, hesitant fuzzy multiple-criteria decision-making with incomplete weights, hesitant fuzzy multiple criteria decision-making with complete weights information, and the hesitant fuzzy preference relation based decision-making theory. These methodologies are implemented in various fields such as decision-making, medical diagnosis, cluster analysis, service quality management, e-learning management and environmental management. A valuable resource for engineers, technicians, and researchers in the fields of fuzzy mathematics, operations research, information science, management science and engineering, it can also be used as a textbook for postgraduate and senior undergraduate students.
(source: Nielsen Book Data)

• Introduction: The Spanish School of Fuzzy Economics (Jaime Gil-Aluja)
• Theoretical Foundations: Specific Max. Traveling Salesman Problems (Raul O Dichiara, Blanca I Niel)
• Solving Fuzzy Mathematical Programming: A Parametric Approach (Ricardo Coelho Silva, Carlos Cruz Corona, Jose L Verdegay)
• Study of Fuzzy Model With Constant Development (Xavier Bertran, Narcis Clara, Dolors Corominas)
• Finance: A Decision Support System for Scoring Distressed Debts and Planning their Collection (Gianluca Murgia, Simone Sbrilli)
• An Assessment of Abnormal Returns in Socially Responsible Firms Using Fuzzy Jensen's Alpha Values (Klender Aimer Cortez Alejandro, Martha del Pilar Rodriguez Garcia, Maria Teresa Sorrosal-Forradellas)
• Return and Risk: The Spanish Public Debt Market (Jose M Brotons, Antonio Terceno, M Gloria Barbera-Marine)
• Time Analysis of Responsibility in Finance (David Ceballos)
• Online Change-Point Detection in Financial Time Series: Challenges and Experimental Evidence with Frequentist and Bayesian Setups (Vasile Georgescu)
• Application of a Fuzzy Model of Economic-Financial Diagnosis to SMEs (Antonio Tercenno, Hernan Vigier, Valeria Scherger)
• Economics and Politics: The Eurozone Crisis and the "Argentinean" Solution: Fuzzy Similarity and Default Risk (Jorge Pazzi, Fernando Tohme)
• A Discrete Particle Swarm Optimization Algorithm for Designing Electoral Zones (Eric A Rinc on-Garcia, Miguel A Gutierrez-Andrade, Sergio G de-los-Cobos-Silva, Pedro Lara-Velazquez, Roman A Mora-Gutierrez, Antonin S Ponsich)
• Locating enterprises using a fuzzy logic-based strategy (Gonzalez Santoyo Federico, Flores Romero Beatriz, Flores Romero Juan Jose)
• A Mathematical Model of Price Stabilization in Context of Uncertainty (Joan Bonet Amat, Joan Carles Ferrer Comalat, Elvira Cassu Serra, Salvador Linares Mustaros)
• An Stochastic Model of the Precautionary Principle (Maria Teresa Casparri, Javier Garcia Fronti)
• Management: An Analysis of the Quality Cost in a Business (Jose M Brotons Martinez, Manuel E Sansalvador Selles)
• Environmental Performance Measurement in Agri-Food Companies Using Fuzzy Inference System (Elena Escrig-Olmedo, Maria Jesus Munoz-Torres, Juana Maria Rivera-Lirio, Maria Angeles Fernandez-Izquierdo)
• Evaluation of the Quality of a Tourist Service Applied to the Case of Student Tourism (Luisa L Lazzari, Patricia Moulia, Andrea Parma)
• Fuzzy Analytic Hierarchy Process Approach for E-Commerce Websites Evaluation (Huan Liu, Viktor V Krasnoproshin, Shuang Zhang)
• Analysis of Industrial Cluster Agent Relations Using Fuzzy Subrelations (Antoni Vidal-Sune, Albert Fonts-Ribas)
• Neural Networks: Evolutionary Neural Network Classifiers for Monitoring Research, Development and Innovation Performance in European Union Member States (Monica de la Paz-Marin, Pilar Campoy-Munoz, Cesar Hervas-Martinez)
• Memory in Financial Time Series: From Investor Behavior to Artificial Neural Networks (M Teresa Sorrosal-Forradellas, M Gloria Barbera-Marine, Lisana B Martinez, Maria-Jose Garbajosa-Cabello)
• Capital Structure in the Spanish Chemical Industry. How Does the Financial Crisis Affect? (Xavier Camara-Turull, Xavier Borras Balsells, M Teresa Sorrosal-Forradellas, M Angeles Fernandez-Izquierdo)
• Using Neural Networks to Model Sovereign Credit Ratings: Application to the European Union (Raul Leon, Maria Jesus Munoz)
• The Effect of Changes of the Hurst Exponent in Return Predictability: The Case of the Dutch Market (Laura Lanzarini, Aurelio Fernandez Bariviera, M Belen Guercio, Cristina Tomas-Monterde)
• Knowledge Management and Innovation: Organizational Learning in Complex Environments: Exploration and Exploitation in a NK Landscape (Bocanet Anca, Iandoli Luca, Cristina Ponsiglione, Giuseppe Zollo)
• Variables Fostering Knowledge Management Classical Theory vs. Uncertainty Theory (Gerardo Gabriel Alfaro Calderon, Federico Gonzalez Santoyo, Juan J Flores, Victor Gerardo Alfaro Garcia)
• Multi-Objective Fuzzy Models in the Selection of Innovations in the Agricultural Area (Luis M Rivera Vilas, Natalia Lopez-Mosquera Garcia, Mercedes Sanchez Garciaxiv).
• (source: Nielsen Book Data)

This book contains a selection of the papers presented at the XVII SIGEF Congress. It presents fuzzy logic, neural networks and other intelligent techniques applied to economic and business problems. This book is very useful for researchers and graduate students aiming to introduce themselves to the field of quantitative techniques for overcoming uncertain environments. The contributors are experienced scholars of different countries who offer real world applications of these mathematical techniques.
(source: Nielsen Book Data)

• Sustainable Development
• Management
• Finance and Economics
• Applications in Other Fields.
• (source: Nielsen Book Data)

This book analyzes some of the most recent advances in the field of decision making and fuzzy systems applied to business and economics presented at the International Conference on Modeling and Simulation (MS'12 Rio de Janeiro), 10-13 December, 2012. In this conference, a special focus is given to the fundamental concept of sustainable development. Other key applications in business, economics and finance are also considered.In general, it is very useful for graduate students and researchers interested in pursuing research that combines quantitative techniques such as modeling and simulation and decision making with business and economic problems. This is especially useful when dealing with complex environments where the information is very uncertain and additional mathematical and statistical techniques are needed in order to understand the specific situations considered.
(source: Nielsen Book Data)

• Fuzzy Sets and their Extensions.- Aggregation in Interval-valued Settings.- Decision Making using Interval-valued Aggregation.- Optimization Problem of k-NN classifier in DNA Microarray Methods.- Interval-valued Methods in Medical Decision Support Systems.
• (source: Nielsen Book Data)

This book describes novel algorithms based on interval-valued fuzzy methods that are expected to improve classification and decision-making processes under incomplete or imprecise information. At first, it introduces interval-valued fuzzy sets. It then discusses new methods for aggregation on interval-valued settings, and the most common properties of interval-valued aggregation operators. It then presents applications such as decision making using interval-valued aggregation, and classification in case of missing values. Interesting applications of the developed algorithms to DNA microarray analysis and in medical decision support systems are shown. The book is intended not only as a timely report for the community working on fuzzy sets and their extensions but also for researchers and practitioners dealing with the problems of uncertain or imperfect information.
(source: Nielsen Book Data)

• A Fuzzy Public Choice Model
• Fuzzy Preferences: Extraction from Data and Their Use in Public Choice Models
• Fuzzy Single-Dimensional Public Choice Models
• Fuzzy Single-Dimensional Models
• Multi-Dimensional Models
• Government Formation Process
• The Beginnings of a Weighted Model & New Frontiers.

This book explores the extent to which fuzzy set logic can overcome some of the shortcomings of public choice theory, particularly its inability to provide adequate predictive power in empirical studies. Especially in the case of social preferences, public choice theory has failed to produce the set of alternatives from which collective choices are made. The book presents empirical findings achieved by the authors in their efforts to predict the outcome of government formation processes in European parliamentary and semi-presidential systems. Using data from the Comparative Manifesto Project (CMP), the authors propose a new approach that reinterprets error in the coding of CMP data as ambiguity in the actual political positions of parties on the policy dimensions being coded. The range of this error establishes parties' fuzzy preferences. The set of possible outcomes in the process of government formation is then calculated on the basis of both the fuzzy Pareto set and the fuzzy maximal set, and the predictions are compared with those made by two conventional approaches as well as with the government that was actually formed. The comparison shows that, in most cases, the fuzzy approaches outperform their conventional counterparts.

• Evolutionary Learning.- Modelling Behaviour Cycles for Life-Long Learning in Motivated Agents.- Breaking the Synaptic Dogma: Evolving a Neuro-inspired Developmental Network.- A New Approach to Adapting Control Parameters in Differential Evolution Algorithm.- A Novel Genetic Algorithm with Orthogonal Prediction for Global Numerical Optimization.- Phylogeny Inference Using a Multi-objective Evolutionary Algorithm with Indirect Representation.- Evolved Look-Up Tables for Simulated DNA Controlled Robots.- Multi-objective Improvement of Software Using Co-evolution and Smart Seeding.- Policy Evolution with Grammatical Evolution.- A PSO Based Adaboost Approach to Object Detection.- Adaptive Non-uniform Distribution of Quantum Particles in mQSO.- Genetically Evolved Fuzzy Rule-Based Classifiers and Application to Automotive Classification.- Improving XCS Performance by Distribution.- Evolving an Ensemble of Neural Networks Using Artificial Immune Systems.- Improving the Performance and Scalability of Differential Evolution.- A Fuzzy-GA Decision Support System for Enhancing Postponement Strategies in Supply Chain Management.- Evolutionary Optimisation.- Solving the Delay-Constrained Capacitated Minimum Spanning Tree Problem Using a Dandelion-Encoded Evolutionary Algorithm.- Generalized Extremal Optimization for Solving Multiprocessor Task Scheduling Problem.- Improving NSGA-II Algorithm Based on Minimum Spanning Tree.- An Island Based Hybrid Evolutionary Algorithm for Optimization.- A Particle Swarm Optimization Based Algorithm for Fuzzy Bilevel Decision Making with Objective-Shared Followers.- Reference Point-Based Particle Swarm Optimization Using a Steady-State Approach.- Genetic Algorithm Based Methods for Identification of Health Risk Factors Aimed at Preventing Metabolic Syndrome.- Extremal Optimisation and Bin Packing.- Extremal Optimisation with a Penalty Approach for the Multidimensional Knapsack Problem.- A Generator for Multimodal Test Functions with Multiple Global Optima.- Choosing Leaders for Multi-objective PSO Algorithms Using Differential Evolution.- Comparison between Genetic Algorithm and Genetic Programming Performance for Photomosaic Generation.- Parameter Tuning of Real-Valued Crossover Operators for Statistics Preservation.- Hybrid Particle Swarm Optimization Based on Thermodynamic Mechanism.- Multiagent Evolutionary Algorithm for T-coloring Problem.- Non-photorealistic Rendering Using Genetic Programming.- Use of Local Ranking in Cellular Genetic Algorithms with Two Neighborhood Structures.- Information Theoretic Classification of Problems for Metaheuristics.- Task Decomposition for Optimization Problem Solving.- Discussion of Search Strategy for Multi-objective Genetic Algorithm with Consideration of Accuracy and Broadness of Pareto Optimal Solutions.- Discussion of Offspring Generation Method for Interactive Genetic Algorithms with Consideration of Multimodal Preference.- Solving Very Difficult Japanese Puzzles with a Hybrid Evolutionary-Logic Algorithm.- Joint Multicast Routing and Channel Assignment in Multiradio Multichannel Wireless Mesh Networks Using Simulated Annealing.- General Game Playing with Ants.- A Generalized Approach to Construct Benchmark Problems for Dynamic Optimization.- A Study on the Performance of Substitute Distance Based Approaches for Evolutionary Many Objective Optimization.- Performance Evaluation of an Adaptive Ant Colony Optimization Applied to Single Machine Scheduling.- Robust Optimization by ?-Ranking on High Dimensional Objective Spaces.- An Evolutionary Method for Natural Language to SQL Translation.- Attributes of Dynamic Combinatorial Optimisation.- A Weighted Local Sharing Technique for Multimodal Optimisation.- Hybrid Learning.- Hybrid Genetic Programming for Optimal Approximation of High Order and Sparse Linear Systems.- Genetic Vector Quantizer Design on Reconfigurable Hardware.- Pattern Learning and Decision Making in a Photovoltaic System.- Using Numerical Simplification to Control Bloat in Genetic Programming.- Horn Query Learning with Multiple Refinement.- Evolving Digital Circuits in an Industry Standard Hardware Description Language.- Parameterised Indexed FOR-Loops in Genetic Programming and Regular Binary Pattern Strings.- Hierarchical Fuzzy Control for the Inverted Pendulum over the Set of Initial Conditions.- Genetic Programming for Feature Ranking in Classification Problems.- Time Series Prediction with Evolved, Composite Echo State Networks.- Adaptive Systems.- Genetic Synthesis of Software Architecture.- Dual Phase Evolution and Self-organisation in Networks.- Heterogeneous Payoffs and Social Diversity in the Spatial Prisoner's Dilemma game.- Theoretical Issues in Evolutionary Computation.- Crossover Can Be Constructive When Computing Unique Input Output Sequences.- Real-World Applications of Evolutionary Computation Techniques.- Power Electronic Circuits Design: A Particle Swarm Optimization Approach.- Computational Intelligence in Radio Astronomy: Using Computational Intelligence Techniques to Tune Geodesy Models.- An Efficient Hybrid Algorithm for Optimization of Discrete Structures.- Evolutionary Multi-Objective Optimization for Biped Walking.- A Method for Assigning Men and Women with Good Affinity to Matchmaking Parties through Interactive Evolutionary Computation.
• (source: Nielsen Book Data)

This LNCS volume contains the papers presented at SEAL 2008, the 7th Int- nationalConference on Simulated Evolutionand Learning, held December 7-10, 2008, in Melbourne, Australia. SEAL is a prestigious international conference series in evolutionary computation and learning. This biennial event was ?rst held in Seoul, Korea, in 1996, and then in Canberra, Australia (1998), Nagoya, Japan (2000), Singapore (2002), Busan, Korea (2004), and Hefei, China (2006). SEAL 2008 received 140 paper submissions from more than 30 countries. After a rigorous peer-review process involving at least 3 reviews for each paper (i.e., over 420 reviews in total), the best 65 papers were selected to be presented at the conference and included in this volume, resulting in an acceptance rate of about 46%. The papers included in this volume cover a wide range of topics in simulated evolution and learning: from evolutionarylearning to evolutionary optimization, from hybrid systems to adaptive systems, from theoretical issues to real-world applications. They represent some of the latest and best research in simulated evolution and learning in the world.
(source: Nielsen Book Data)

• Preliminaries.- Classical Revealed Preference Theory.- Fuzzy Preference Relations.- Fuzzy Choice Functions.- Fuzzy Revealed Preference and Consistency Conditions.- General Results.- Degree of Dominance.- Similarity and Rationality Indicators for Fuzzy Choice Functions.- Applications.- Concluding Remarks.
• (source: Nielsen Book Data)

This book extends the theory of revealed preference to fuzzy choice functions, providing applications to multicriteria decision making problems. The main topics of revealed preference theory are treated in the framework of fuzzy choice functions. New topics, such as the degree of dominance and similarity of vague choices, are developed. The results are applied to economic problems where partial information and human subjectivity involve vague choices and vague preferences.
(source: Nielsen Book Data)

• 1. The theory of fuzzy optimal and approximate social prices
• 2. The theory of optimal social discount rate
• 3. The theoretical structure of cost-benefit criteria, choice of project and discounting process
• 4. The theory of fuzzy present value (FPV)
• 5. The theory of fuzzy decisions, optimal taxation and cost-benefit distribution
• 6. Toward a theory of social goals and objective formation in cost-benefit analysis.

Criticism is the habitus of the contemplative intellect, whereby we try to recognize with probability the genuine quality of a l- erary work by using appropriate aids and rules. In so doing, c- tain general and particular points must be considered. The art of interpretation or hermeneutics is the habitus of the contemplative intellect of probing into the sense of somewhat special text by using logical rules and suitable means. Note : Hermeneutics differs from criticism as the part does from the whole. Antonius Gvilielmus Amo Afer (1727) There is no such thing as absolute truth. At best it is a subj- tive criterion, but one based upon valuation. Unfortunately, too many people place their fate in the hands of subjective without properly evaluating it. Arnold A. Kaufmann and Madan M. Gupta The development of cost benefit analysis and the theory of fuzzy decision was divided into two inter-dependent structures of identification and measurement theory on one hand and fuzzy value theory one the other. Each of them has sub-theories that constitute a complete logical system.
(source: Nielsen Book Data)

• Theories of Decision Making under Uncertainty
• Fuzzy Logic and Approximate Reasoning
• Preferences Framework
• Imperfect Decision-Relevant Information
• Uncertainty Measures in Decision Making
• Fuzzy Logic-Based Decision Theory with Imperfect Information
• Hierarchical Models for Decision Making with Imperfect Information
• Decision Making Model without Utility
• Combined States concept and Behavioral Decision Making
• Decision Making under Unprecisiated Imperfect Information
• The General Theory of Decisions
• Simulations and Applications in Business, Economics, Production and Medicine--.
• (source: Nielsen Book Data)

Every day decision making in complex human-centric systems are characterized by imperfect decision-relevant information. The principal problems with the existing decision theories are that they do not have capability to deal with situations in which probabilities and events are imprecise. In this book, we describe a new theory of decision making with imperfect information. The aim is to shift the foundation of decision analysis and economic behavior from the realm bivalent logic to the realm fuzzy logic and Z-restriction, from external modeling of behavioral decisions to the framework of combined states.This book will be helpful for professionals, academics, managers and graduate students in fuzzy logic, decision sciences, artificial intelligence, mathematical economics, and computational economics.
(source: Nielsen Book Data)

• Fuzzy Social Choice
• Classical Social Choice Theorems
• Rationality of Fuzzy Preferences
• Arrow and the Aggregation of Fuzzy Preferences
• Characteristics of Strategy-Proof Fuzzy Social Choice
• Fuzzy Black's Median Voter Theorem
• Representing Thick Indifference in Spatial Models
• Conclusion.

This book offers a comprehensive analysis of the social choice literature and shows, by applying fuzzy sets, how the use of fuzzy preferences, rather than that of strict ones, may affect the social choice theorems. To do this, the book explores the presupposition of rationality within the fuzzy framework and shows that the two conditions for rationality, completeness and transitivity, do exist with fuzzy preferences. Specifically, this book examines: the conditions under which a maximal set exists; the Arrows theorem; the Gibbard-Satterthwaite theorem; and the median voter theorem. After showing that a non-empty maximal set does exists for fuzzy preference relations, this book goes on to demonstrating the existence of a fuzzy aggregation rule satisfying all five Arrowian conditions, including non-dictatorship. While the Gibbard-Satterthwaite theorem only considers individual fuzzy preferences, this work shows that both individuals and groups can choose alternatives to various degrees, resulting in a social choice that can be both strategy-proof and non-dictatorial. Moreover, the median voter theorem is shown to hold under strict fuzzy preferences, but not under weak fuzzy preferences. By providing a standard model of fuzzy social choice and by drawing the necessary connections between the major theorems, this book fills an important gap in the current literature and encourages future empirical research in the field.

• Intuitionistic Preference Relations
• Interactive Intuitionistic Fuzzy Multi-Attribute Decision Making.

This book offers an in-depth and comprehensive introduction to the priority methods of intuitionistic preference relations, the consistency and consensusimproving procedures for intuitionistic preference relations, the approaches to group decision making based on intuitionistic preference relations, the approaches and models for interactive decision making with intuitionistic fuzzy information, and the extended results in interval-valued intuitionistic fuzzy environments.

• Granularity Helps Explain Seemingly Irrational Features of Human Decision Making
• A Comprehensive Granular Model for Decision Making with Complex Data.-Granularity in Economic Decision Making: An Interdisciplinary Review
• Decision Makers' Opinions Changing Attitude-Driven Consensus Model under Linguistic Environment and Its Application in Dynamic MAGDM
• Using Computing with Words for Managing Non-Cooperative Behaviors in Large Scale Group Decision Making
• A Type-2 Fuzzy Logic Approach for Multi-Criteria Group Decision Making
• Multi-Criteria Influence Diagrams
• A Tool for the Sequential Group Risk Assessment.-Consensus Modeling under Fuzziness
• A Dynamic Approach with Random Iterative Steps
• Decision Making
• Interactive and Interactive Approaches
• Collaborative Decision Making by Ensemble Rule Based Classification Systems
• A GDM Method Based on Granular Computing for Academic Library Management
• Spatial-taxon Information Granules as Used in Iterative Fuzzy-Decision-Making for Image Segmentation
• Group Decision Making in Fuzzy Environment
• An Iterative Procedure Based on Group Dynamics
• Fuzzy Optimization in Decision Making of Air Quality Management
• Group Decision Making in Fuzzy Environment
• An Iterative Procedure Based on Group Dynamics
• Fuzzy Optimization in Decision Making of Air Quality Management.

This volume is devoted to interactive and iterative processes of decision-making? I2 Fuzzy Decision Making, in brief. Decision-making is inherently interactive. Fuzzy sets help realize human-machine communication in an efficient way by facilitating a two-way interaction in a friendly and transparent manner. Human-centric interaction is of paramount relevance as a leading guiding design principle of decision support systems. The volume provides the reader with an updated and in-depth material on the conceptually appealing and practically sound methodology and practice of I2 Fuzzy Decision Making. The book engages a wealth of methods of fuzzy sets and Granular Computing, brings new concepts, architectures and practice of fuzzy decision-making providing the reader with various application studies. The book is aimed at a broad audience of researchers and practitioners in numerous disciplines in which decision-making processes play a pivotal role and serve as a vehicle to produce solutions to existing problems. Those involved in operations research, management, various branches of engineering, social sciences, logistics, and economics will benefit from the exposure to the subject matter. The book may serve as a useful and timely reference material for graduate students and senior undergraduate students in courses on decision-making, Computational Intelligence, operations research, pattern recognition, risk management, and knowledge-based systems.

• Introduction: The Spanish School of Fuzzy Economics (Jaime Gil-Aluja)
• Theoretical Foundations: Specific Max. Traveling Salesman Problems (Raul O Dichiara, Blanca I Niel)
• Solving Fuzzy Mathematical Programming: A Parametric Approach (Ricardo Coelho Silva, Carlos Cruz Corona, Jose L Verdegay)
• Study of Fuzzy Model With Constant Development (Xavier Bertran, Narcis Clara, Dolors Corominas)
• Finance: A Decision Support System for Scoring Distressed Debts and Planning their Collection (Gianluca Murgia, Simone Sbrilli)
• An Assessment of Abnormal Returns in Socially Responsible Firms Using Fuzzy Jensen's Alpha Values (Klender Aimer Cortez Alejandro, Martha del Pilar Rodriguez Garcia, Maria Teresa Sorrosal-Forradellas)
• Return and Risk: The Spanish Public Debt Market (Jose M Brotons, Antonio Terceno, M Gloria Barbera-Marine)
• Time Analysis of Responsibility in Finance (David Ceballos)
• Online Change-Point Detection in Financial Time Series: Challenges and Experimental Evidence with Frequentist and Bayesian Setups (Vasile Georgescu)
• Application of a Fuzzy Model of Economic-Financial Diagnosis to SMEs (Antonio Tercenno, Hernan Vigier, Valeria Scherger)
• Economics and Politics: The Eurozone Crisis and the "Argentinean" Solution: Fuzzy Similarity and Default Risk (Jorge Pazzi, Fernando Tohme)
• A Discrete Particle Swarm Optimization Algorithm for Designing Electoral Zones (Eric A Rinc on-Garcia, Miguel A Gutierrez-Andrade, Sergio G de-los-Cobos-Silva, Pedro Lara-Velazquez, Roman A Mora-Gutierrez, Antonin S Ponsich)
• Locating enterprises using a fuzzy logic-based strategy (Gonzalez Santoyo Federico, Flores Romero Beatriz, Flores Romero Juan Jose)
• A Mathematical Model of Price Stabilization in Context of Uncertainty (Joan Bonet Amat, Joan Carles Ferrer Comalat, Elvira Cassu Serra, Salvador Linares Mustaros)
• An Stochastic Model of the Precautionary Principle (Maria Teresa Casparri, Javier Garcia Fronti)
• Management: An Analysis of the Quality Cost in a Business (Jose M Brotons Martinez, Manuel E Sansalvador Selles)
• Environmental Performance Measurement in Agri-Food Companies Using Fuzzy Inference System (Elena Escrig-Olmedo, Maria Jesus Munoz-Torres, Juana Maria Rivera-Lirio, Maria Angeles Fernandez-Izquierdo)
• Evaluation of the Quality of a Tourist Service Applied to the Case of Student Tourism (Luisa L Lazzari, Patricia Moulia, Andrea Parma)
• Fuzzy Analytic Hierarchy Process Approach for E-Commerce Websites Evaluation (Huan Liu, Viktor V Krasnoproshin, Shuang Zhang)
• Analysis of Industrial Cluster Agent Relations Using Fuzzy Subrelations (Antoni Vidal-Sune, Albert Fonts-Ribas)
• Neural Networks: Evolutionary Neural Network Classifiers for Monitoring Research, Development and Innovation Performance in European Union Member States (Monica de la Paz-Marin, Pilar Campoy-Munoz, Cesar Hervas-Martinez)
• Memory in Financial Time Series: From Investor Behavior to Artificial Neural Networks (M Teresa Sorrosal-Forradellas, M Gloria Barbera-Marine, Lisana B Martinez, Maria-Jose Garbajosa-Cabello)
• Capital Structure in the Spanish Chemical Industry. How Does the Financial Crisis Affect? (Xavier Camara-Turull, Xavier Borras Balsells, M Teresa Sorrosal-Forradellas, M Angeles Fernandez-Izquierdo)
• Using Neural Networks to Model Sovereign Credit Ratings: Application to the European Union (Raul Leon, Maria Jesus Munoz)
• The Effect of Changes of the Hurst Exponent in Return Predictability: The Case of the Dutch Market (Laura Lanzarini, Aurelio Fernandez Bariviera, M Belen Guercio, Cristina Tomas-Monterde)
• Knowledge Management and Innovation: Organizational Learning in Complex Environments: Exploration and Exploitation in a NK Landscape (Bocanet Anca, Iandoli Luca, Cristina Ponsiglione, Giuseppe Zollo)
• Variables Fostering Knowledge Management Classical Theory vs. Uncertainty Theory (Gerardo Gabriel Alfaro Calderon, Federico Gonzalez Santoyo, Juan J Flores, Victor Gerardo Alfaro Garcia)
• Multi-Objective Fuzzy Models in the Selection of Innovations in the Agricultural Area (Luis M Rivera Vilas, Natalia Lopez-Mosquera Garcia, Mercedes Sanchez Garciaxiv).
• (source: Nielsen Book Data)

This book contains a selection of the papers presented at the XVII SIGEF Congress. It presents fuzzy logic, neural networks and other intelligent techniques applied to economic and business problems. This book is very useful for researchers and graduate students aiming to introduce themselves to the field of quantitative techniques for overcoming uncertain environments. The contributors are experienced scholars of different countries who offer real world applications of these mathematical techniques.
(source: Nielsen Book Data)

• Preface Symbols and Abbreviations
• Chapter 1 Uncertainty 13
• Chapter 2 Decision Tree with Uncertainty
• Chapter 3 Clustering under Uncertainty Environment 77
• Chapter 4 Active Learning with Uncertainty 121
• Chapter 5 Ensemble learning with Uncertainty 173 Index.
• (source: Nielsen Book Data)

Learning with uncertainty covers a broad range of scenarios in machine learning, this book mainly focuses on: (1) Decision tree learning with uncertainty, (2) Clustering under uncertainty environment, (3) Active learning based on uncertainty criterion, and (4) Ensemble learning in a framework of uncertainty. The book starts with the introduction to uncertainty including randomness, roughness, fuzziness and non-specificity and then comprehensively discusses a number of key issues in learning with uncertainty, such as uncertainty representation in learning, the influence of uncertainty on the performance of learning system, the heuristic design with uncertainty, etc. Most contents of the book are our research results in recent decades. The purpose of this book is to help the readers to understand the impact of uncertainty on learning processes. It comes with many examples to facilitate understanding. The book can be used as reference book or textbook for researcher fellows, senior undergraduates and postgraduates majored in computer science and technology, applied mathematics, automation, electrical engineering, etc.
(source: Nielsen Book Data)

• Preface.
• 1. Introduction.
• 2. Preliminaries on Axiomatic Probability Theory.
• 3. Probabilistic Model of Decision Making Under Uncertainty.
• 4. Basic Elements of Dempster-Shafer Theory.
• 5. Elementary Properties of Belief Functions.
• 6. Probabilistic Analysis of Dempster Combination Rule.
• 7. Nonspecificity Degrees of Basic Probability Assignments.
• 8. Belief Functions Induced by Partial Compatibility Relations.
• 9. Belief Functions over Infinite State Spaces.
• 10. Boolean Combinations of Set-Valued Random Variables.
• 11. Belief Functions with Signed and Nonstandard Values.
• 12. Jordan Decomposition of Signed Belief Functions.
• 13. Monte-Carlo Estimations for Belief Functions.
• 14. Boolean-Valued and Boolean-Like Processed Belief Functions.
• 15. References.
• 16. Index.
• (source: Nielsen Book Data)

This volume is a highly theoretical and mathematical study analyzing the notion and theory of belief functions, also known as the Dempster-Shafer theory, from the point of view of the classical Kolmogorov axiomatic probability theory. In other terms, the theory of belief functions is taken as an interesting, non-traditional application of probability theory, and the standard methodology of probability theory, and measure theory in general, is applied in order to arrive at some new and perhaps interesting generalizations and results not accessible within the classical combinatorial framework of the theory of belief functions (Dempster-Shafer theory) over finite spaces. The relation to great systems and their theory seems to be very close and should become clear from the first two chapters of the book.
(source: Nielsen Book Data)

• 1 Introduction
• 1.1 Uncertainty in the World Around
• 1.2 Classical Probability Theory
• Uncertainty as Randomness
• 1.3 Dempster-Shafer Approach to Uncertainty Processing
• 1.4 Relations to the Theory of General Systems
• 2 Preliminaries on Axiomatic Probability Theory
• 2.1 Probability Spaces and Random Variables
• 2.2 Some Intuition and Motivation Behind
• 2.3 Conditional Probabilities and Stochastic Independence
• 2.4 The Most Simple Setting of the Strong Law of Large Numbers
• 3 Probabilistic Model of Decision Making under Uncertainty
• 3.1 An Intuitive Background to Decision Making Theory
• 3.2 Decision Making under Uncertainty
• 3.3 Statistical Decision Functions
• 3.4 The Bayesian and the Minimax Principles
• 3.5 An Example and Some Problems Involved
• 4 Basic Elements of Dempster-Shafer Theory
• 4.1 From Intuition to Compatibility Relations
• 4.2 From Compatibility Relations to Belief Functions
• 4.3 Some Remarks and Comments
• 4.4 Semantical Consistence and Correctness of Belief Functions
• 5 Elementary Properties of Belief Functions
• 5.1 Plausibility Functions
• 5.2 Basic Probability Assignments and Belief Functions
• 5.3 Super-Additivity of Belief Functions
• 5.4 Some Particular Cases of Belief Functions
• 5.5 Belief Functions and the Case of Total Ignorance
• 6 Probabilistic Analysis of Dempster Combination Rule
• 6.1 Knowledge Acquisition as Dynamical Process
• 6.2 Combining Compatibility Relations
• 6.3 Towards Dempster Combination Rule
• 6.4 Elementary Properties of Dempster Combination Rule
• 6.5 Dual Combination Rule
• 7 Nonspecificity Degrees of Basic Probability Assignments
• 7.1 The Most Simple Case of Nonspecificity Degrees
• 7.2 Nonspecificity Degrees of Dempster Products
• 7.3 Quasi-Deconditioning
• 7.4 The Case of Dual Combination Rule
• 8 Belief Functions Induced by Partial Compatibility Relations
• 8.1 Compatibility Relations over Sets of States and Sets of Empirical Values
• 8.2 Partial Generalized Compatibility Relations
• 8.3 Belief Functions Defined by Partial Generalized Compatibility Relations
• 8.4 Partial Generalized Compatibility Relations with the Same Compatibility Relation
• 8.5 Approximations of Belief Functions by the Partial Generalized Ones
• 9 Belief Functions over Infinite State Spaces
• 9.1 Towards Infinite Basic Spaces
• 9.2 Definability of Degrees of Belief for Subsets of Infinite Spaces
• 9.3 Extensions of Degrees of Belief to Non-Regular Subsets
• 9.4 Elementary Properties of Extended Belief Functions
• 9.5 Bounds of Application of Extended Belief Functions
• 9.6 Survey of Approximations of Degrees of Belief over Infinite Spaces
• 10 Boolean Combinations of Set-Valued Random Variables
• 10.1 Combining Set-Valued Random Variables
• 10.2 Belief Functions Defined by Unions of Set-Valued Random Variables
• 10.3 Belief Functions Defined by Intersections of Set-Valued Random Variables
• 11 Belief Functions with Signed and Nonstandard Values
• 11.1 The Inversion Problem for Degrees of Belief and Belief Functions
• 11.2 Signed Measures
• 11.3 Degrees of Belief Are Leaving the Unit Interval of Reals
• 11.4 Dempster Combination Rule for Basic Signed Measure Assignments
• 11.5 Inversion Rule for Basic Signed Measure Assignments
• 11.6 Almost Invertibility of Basic Signed Measure Assignments
• 11.7 Degrees of Belief with Nonstandard Values
• 11.8 An Abstract Algebraic Approach to the Inversion Problem
• 12 Jordan Decomposition of Signed Belief Functions
• 12.1 Hahn Decomposition Theorem for Signed Measures
• 12.2 Jordan Decomposition of Signed Belief Functions
• 12.3 Generalizing Conditioned Belief Functions
• 13 Monte-Carlo Estimations for Belief Functions
• 13.1 Strong Law of Large Numbers Applied to Belief Functions
• 13.2 Towards Monte-Carlo Algorithms for Belief Functions
• 13.3 Asymptotic Properties of Monte-Carlo Estimations of Belief Functions
• 13.4 Chebyshev Inequality for Monte-Carlo Estimations of Belief Functions
• 14 Boolean-Valued and Boolean-Like Processed Belief Functions
• 14.1 Intuition, Motivation and Preliminaries on Boolean Algebras
• 14.2 Boolean-Valued Probability Measures
• 14.3 Boolean-Valued Belief and Plausibility Functions
• 14.4 Boolean-Like Structure over The Unit Interval of Real Numbers
• 14.5 Probability Measures with Values in Boolean-Like Structured Unit Interval of Real Numbers
• 14.6 Basic Nonstandard Probability Assignments
• 15 References
• 16 Index.

This volume is a highly theoretical and mathematical study analyzing the notion and theory of belief functions, also known as the Dempster-Shafer theory, from the point of view of the classical Kolmogorov axiomatic probability theory. In other terms, the theory of belief functions is taken as an interesting, non-traditional application of probability theory, and the standard methodology of probability theory, and measure theory in general, is applied in order to arrive at some new and perhaps interesting generalizations and results not accessible within the classical combinatorial framework of the theory of belief functions (Dempster-Shafer theory) over finite spaces. The relation to great systems and their theory seems to be very close and should become clear from the first two chapters of the book.

• Fuzzy Maximal Subsets Fuzzy Set Theory Fuzzy Maximal Sets
• Fuzzy Choice Functions Basic Properties Consistency Conditions M-Rationality and G-Rationality Full Rationality of Fuzzy Choice Functions on Base Domain Quasi-Transitive Rationality of Fuzzy Choice Functions Full Rationality and Congruence Axioms of Fuzzy Choice Functions
• Factorization of Fuzzy Preference Relations Basic Definitions and Results Quasi-Subtraction Factorizations Intuitionistic Fuzzy Relations Intuitionistic Fuzzy Preference Relations and Their Factorization
• Fuzzy Non-Arrow Results Nondictatorial Fuzzy Aggregation Rules Auxiliary Functions Arrow's Theorem and Max-Star Transitivity
• Fuzzy Arrow's Theorem Dictatorial Fuzzy Preference Aggregation Rules Representation Rules, Veto Players, Oligarchies, and Collegiums Decisive Sets, Filters, and Fuzzy Arrow's Theorem Fuzzy Preferences and Social Choice Fuzzy Preferences and Arrow-Type Problems The Structure of Fuzzy Preferences: Social Choice Implications
• Single Peaked Fuzzy Preferences: Black's Median Voter Theorem Fuzzy Aggregation Preference Rules Fuzzy Voting Rules Single-Peaked Fuzzy Profiles The Core in Fuzzy Spatial Models
• Rationality Fuzzy Preference and Preference-Based Choice Functions Fuzzy Choice Functions, Revealed Preference and Rationality Arrow-Type Results under Intuitionistic Fuzzy Preferences Fuzzy Preference Profiles and Fuzzy Aggregation Rules IIA3 and Nondictatorial Fuzzy Aggregation Rules IIA2, IIA4, and Fuzzy Arrow's Theorem Representation Rules, Veto Players, and Oligarchies Fuzzy Preference and Arrowian Results Intuitionistic Arrow's Theorem and Gibbard's Oligarchy Theorem
• Manipulability of Fuzzy Social Choice Functions Preliminaries Fuzzy Social Choice Functions Impossibility Results Non-Manipulable Partitioning Application
• Similarity of Fuzzy Choice Functions Fuzzy Choice Functions Similarity of Fuzzy Choice Functions Arrow Index of a Fuzzy Choice Function
• Index
• Exercises and References appear at the end of each chapter.
• (source: Nielsen Book Data)

Fuzzy social choice theory is useful for modeling the uncertainty and imprecision prevalent in social life yet it has been scarcely applied and studied in the social sciences. Filling this gap, Application of Fuzzy Logic to Social Choice Theory provides a comprehensive study of fuzzy social choice theory. The book explains the concept of a fuzzy maximal subset of a set of alternatives, fuzzy choice functions, the factorization of a fuzzy preference relation into the "union" (conorm) of a strict fuzzy relation and an indifference operator, fuzzy non-Arrowian results, fuzzy versions of Arrow's theorem, and Black's median voter theorem for fuzzy preferences. It examines how unambiguous and exact choices are generated by fuzzy preferences and whether exact choices induced by fuzzy preferences satisfy certain plausible rationality relations. The authors also extend known Arrowian results involving fuzzy set theory to results involving intuitionistic fuzzy sets as well as the Gibbard-Satterthwaite theorem to the case of fuzzy weak preference relations. The final chapter discusses Georgescu's degree of similarity of two fuzzy choice functions.
(source: Nielsen Book Data)

• About the Authors. Foreword. Preface.
• 1 Decision-Making in System Project, Planning, Operation, and Control: Motivation, Objectives, and Basic Concepts. 1.1 Decision-Making and its Support. 1.2 Optimization and Decision-Making Problems. 1.3 Multicriteria Decision-Making. 1.4 Group Decision-Making. 1.5 Fuzzy Sets and their Role in Decision-Making Processes. 1.6 Conclusions. References.
• 2 Notions and Concepts of Fuzzy Sets: An Introduction. 2.1 Sets and Fuzzy Sets: A Fundamental Departure from the Principle of Dichotomy. 2.2 Interpretation of Fuzzy Sets. 2.3 Membership Functions and Classes of Fuzzy Sets. 2.4 Fuzzy Numbers and Intervals. 2.5 Linguistic Variables. 2.6 A Generic Characterization of Fuzzy Sets: Some Fundamental Descriptors. 2.7 Geometric Interpretation of Sets and Fuzzy Sets. 2.8 Fuzzy Sets and the Family of alpha-cuts. 2.9 Operations on Fuzzy Sets. 2.11 Conclusions. References.
• 3 Selected Design and Processing Aspects of Fuzzy Sets. 3.1 The Development of Fuzzy Sets: Elicitation of Membership Functions. 3.2 Aggregation Operations. 3.3 Transformations of Fuzzy Sets. 3.4 Conclusions. References.
• 4 Continuous Models ofMulticriteria Decision-Making and their Analysis. 4.1 Continuous Models (X, M Models) of Multicriteria Decision-Making. 4.2 Pareto-Optimal Solutions. 4.3 Approaches to the Use of DM Information. 4.4 Methods of Multiobjective Decision-Making. 4.5 Bellman Zadeh Approach and its Application to Multicriteria Decision-Making. 4.6 Multicriteria Resource Allocation. 4.7 Adaptive Interactive Decision-Making System for Multicriteria Resource Allocation. 4.8 Application of the Bellman Zadeh Approach to Multicriteria Problems. 4.9 Conclusions. References.
• 5 Introduction to Preference Modeling with Binary Fuzzy Relations. 5.1 Binary Fuzzy Relations and their Fundamental Properties. 5.2 Preference Modeling with Binary Fuzzy Relations. 5.3 Preference Structure of Binary Fuzzy Preference Relations. 5.4 A Method for Constructing a Fuzzy Preference Structure. 5.5 Consistency of Fuzzy Preference Relations. 5.6 Conclusions. References.
• 6 Construction of Fuzzy Preference Relations. 6.1 Preference Formats. 6.2 Ordering of Fuzzy Quantities and the Construction of Fuzzy Preference Relations. 6.3 Transformation Functions and their Use for Converting Different Preference Formats into Fuzzy Preference Relations. 6.4 A Method for Repairing Inconsistent Judgments. 6.5 Conclusions. References.
• 7 Discrete Models of Multicriteria Decision-Making and their Analysis. 7.1 Optimization Problems with Fuzzy Coefficients and their Analysis. 7.2 Discrete Models (X, R Models) of Multiattribute Decision-Making. 7.3 Basic Techniques of Analysis of X, R Models. 7.4 Interactive Decision-Making System for Multicriteria Analysis of Alternatives in a Fuzzy Environment. 7.5 Multicriteria Analysis of Alternatives with Fuzzy Ordering of Criteria. 7.6 Multicriteria Analysis of Alternatives with the Concept of Fuzzy Majority. 7.7 Multicriteria Analysis of Alternatives Based on an Outranking Approach (Fuzzy Promethee). 7.8 Application Examples. 7.9 Conclusions. References.
• 8 Generalization of a Classic Approach to Dealing with Uncertainty of Information for Multicriteria Decision Problems. 8.1 Classic Approach to Dealing with Uncertainty of Information. 8.2 Choice Criteria. 8.3 Generalization of the Classic Approach. 8.4 Modification of the Choice Criteria. 8.5 General Scheme of Multicriteria Decision-Making under Uncertainty. 8.6 Application Example. 8.7 Conclusions. References.
• 9 Group Decision-Making: Fuzzy Models. 9.1 Group Decision-Making Problem and its Characteristics. 9.2 Strategies for the Analysis of Group Decision-Making Problems: Multiperson and Multiattribute Aggregation Modes. 9.3 The Different Levels of Influence of Each Expert in the Construction of the Collective Opinion. 9.4 Aggregation Operators for Constructing Collective Opinions on the Basis of Fuzzy Models and their Properties. 9.5 Consistency of Pairwise Judgments in Group Decision-Making. 9.6 Fuzzy Group Decision-Making Methods. 9.7 Conclusions. References.
• 10 Use of Consensus Schemes in Group Decision-Making. 10.1 Consensus in Group Decision-Making. 10.2 Consensus Schemes: Definition and Motivation. 10.3 Fuzzy Concordance and Fuzzy Consensus Measures. 10.4 Moderator Interventions. 10.5 Optimal Consensus in a Fuzzy Environment. 10.6 Consensus Schemes in Fuzzy Environment. 10.7 An Application Related to the Balanced Scorecard Methodology. 10.8 Conclusions. References. Index.
• (source: Nielsen Book Data)

Fuzzy Multicriteria Decision-Making: Models, Algorithms and Applications addresses theoretical and practical gaps in considering uncertainty and multicriteria factors encountered in the design, planning, and control of complex systems. Including all prerequisite knowledge and augmenting some parts with a step-by-step explanation of more advanced concepts, the authors provide a systematic and comprehensive presentation of the concepts, design methodology, and detailed algorithms. These are supported by many numeric illustrations and a number of application scenarios to motivate the reader and make some abstract concepts more tangible. Fuzzy Multicriteria Decision-Making: Models, Algorithms and Applications will appeal to a wide audience of researchers and practitioners in disciplines where decision-making is paramount, including various branches of engineering, operations research, economics and management; it will also be of interest to graduate students and senior undergraduate students in courses such as decision making, management, risk management, operations research, numerical methods, and knowledge-based systems.
(source: Nielsen Book Data)