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Modelling Nature: An Opinionated Introduction to Scientific Representation (Synthese Library #427)
by Roman Frigg James NguyenThis monograph offers a critical introduction to current theories of how scientific models represent their target systems. Representation is important because it allows scientists to study a model to discover features of reality. The authors provide a map of the conceptual landscape surrounding the issue of scientific representation, arguing that it consists of multiple intertwined problems. They provide an encyclopaedic overview of existing attempts to answer these questions, and they assess their strengths and weaknesses. The book also presents a comprehensive statement of their alternative proposal, the DEKI account of representation, which they have developed over the last few years. They show how the account works in the case of material as well as non-material models; how it accommodates the use of mathematics in scientific modelling; and how it sheds light on the relation between representation in science and art. The issue of representation has generated a sizeable literature, which has been growing fast in particular over the last decade. This makes it hard for novices to get a handle on the topic because so far there is no book-length introduction that would guide them through the discussion. Likewise, researchers may require a comprehensive review that they can refer to for critical evaluations. This book meets the needs of both groups.
Modelling Non-Markovian Quantum Systems Using Tensor Networks (Springer Theses)
by Aidan StrathearnThis thesis presents a revolutionary technique for modelling the dynamics of a quantum system that is strongly coupled to its immediate environment. This is a challenging but timely problem. In particular it is relevant for modelling decoherence in devices such as quantum information processors, and how quantum information moves between spatially separated parts of a quantum system.The key feature of this work is a novel way to represent the dynamics of general open quantum systems as tensor networks, a result which has connections with the Feynman operator calculus and process tensor approaches to quantum mechanics. The tensor network methodology developed here has proven to be extremely powerful: For many situations it may be the most efficient way of calculating open quantum dynamics. This work is abounds with new ideas and invention, and is likely to have a very significant impact on future generations of physicists.
The Modelling of Radiation Damage in Metals Using Ehrenfest Dynamics
by Christopher RaceAtomistic simulations of metals under irradiation are indispensable for understanding damage processes at time- and length-scales beyond the reach of experiment. Previously, such simulations have largely ignored the effect of electronic excitations on the atomic dynamics, even though energy exchange between atoms and electrons can have significant effects on the extent and nature of radiation damage. This thesis presents the results of time-dependent tight-binding simulations of radiation damage, in which the evolution of a coupled system of energetic classical ions and quantum mechanical electrons is correctly described. The effects of electronic excitations in collision cascades and ion channeling are explored and a new model is presented, which makes possible the accurate reproduction of non-adiabatic electronic forces in large-scale classical molecular dynamics simulations of metals.
Modelling Operational Risk Using Bayesian Inference
by Pavel V. ShevchenkoThe management of operational risk in the banking industry has undergone explosive changes over the last decade due to substantial changes in the operational environment. Globalization, deregulation, the use of complex financial products, and changes in information technology have resulted in exposure to new risks which are very different from market and credit risks. In response, the Basel Committee on Banking Supervision has developed a new regulatory framework for capital measurement and standards for the banking sector. This has formally defined operational risk and introduced corresponding capital requirements. Many banks are undertaking quantitative modelling of operational risk using the Loss Distribution Approach (LDA) based on statistical quantification of the frequency and severity of operational risk losses. There are a number of unresolved methodological challenges in the LDA implementation. Overall, the area of quantitative operational risk is very new and different methods are under hot debate. This book is devoted to quantitative issues in LDA. In particular, the use of Bayesian inference is the main focus. Though it is very new in this area, the Bayesian approach is well suited for modelling operational risk, as it allows for a consistent and convenient statistical framework for quantifying the uncertainties involved. It also allows for the combination of expert opinion with historical internal and external data in estimation procedures. These are critical, especially for low-frequency/high-impact operational risks. This book is aimed at practitioners in risk management, academic researchers in financial mathematics, banking industry regulators and advanced graduate students in the area. It is a must-read for anyone who works, teaches or does research in the area of financial risk.
Modelling our Changing World (Palgrave Texts in Econometrics)
by Jennifer L. Castle David F. HendryThis open access book focuses on the concepts, tools and techniques needed to successfully model ever-changing time-series data. It emphasizes the need for general models to account for the complexities of the modern world and how these can be applied to a range of issues facing Earth, from modelling volcanic eruptions, carbon dioxide emissions and global temperatures, to modelling unemployment rates, wage inflation and population growth.Except where otherwise noted, this book is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0.
Modelling, Simulation and Applications of Complex Systems: CoSMoS 2019, Penang, Malaysia, April 8-11, 2019 (Springer Proceedings in Mathematics & Statistics #359)
by Mohd Hafiz Mohd Md Yushalify Misro Syakila Ahmad Doanh Nguyen NgocThis book discusses the latest progresses and developments on complex systems research and intends to give an exposure to prospective readers about the theoretical and practical aspects of mathematical modelling, numerical simulation and agent-based modelling frameworks. The main purpose of this book is to emphasize a unified approach to complex systems analysis, which goes beyond to examine complicated phenomena of numerous real-life systems; this is done by investigating a huge number of components that interact with each other at different (microscopic and macroscopic) scales; new insights and emergent collective behaviours can evolve from the interactions between individual components and also with their environments. These tools and concepts permit us to better understand the patterns of various real-life systems and help us to comprehend the mechanisms behind which distinct factors shaping some complex systems phenomena being influenced.This book is published in conjunction with the International Workshop on Complex Systems Modelling & Simulation 2019 (CoSMoS 2019): IoT & Big Data Integration. This international event was held at the Universiti Sains Malaysia Main Campus, Penang, Malaysia, from 8 to 11 April 2019. This book appeals to readers interested in complex systems research and other related areas such as mathematical modelling, numerical simulation and agent-based modelling frameworks.
Modelling, Simulation and Control of Non-linear Dynamical Systems: An Intelligent Approach Using Soft Computing and Fractal Theory
by Oscar Castillo Patricia MelinThese authors use soft computing techniques and fractal theory in this new approach to mathematical modeling, simulation and control of complexion-linear dynamical systems. First, a new fuzzy-fractal approach to automated mathematical modeling of non-linear dynamical systems is presented. It is illustrated with examples on the PROLOG programming la
Modelling, Simulation and Optimization of Industrial Fixed Bed Catalytic Reactors
by S.S.E.H. ElnashaieIn the last two decades impressive advances have been made toward the understanding and quantitative description of the kinetics. Despite these advances, however, the use of mathematical modelling of gas-solid catalytic reactors in industry is still limited. By consolidating progress in the understanding of catalytic processes, this book applies these fundamental advances to the development of models for design, simulation and optimization of industrial reactors. Paying particular attention to the verification of the developed models against industrial data, these models are used to optimize the performance of many practical reactor cases. Using a systems approach for the development of the different components and the resulting overall models, the book is easy to read and gives an insight into the behaviour of these complex industrial systems. In addition, the practical relevance of bifurcation, instability and chaos to industrial reactors is briefly discussed.
Modelling Supply Chain Dynamics
by Jose M. FraminanThis book discusses supply chain management, focusing on developments within modelling the dynamic behaviour of the supply chain. Aimed at postgraduate students, researchers and practitioners, this book provides an in-depth knowledge of the dynamics of supply chains. Business trends such as the globalisation process and the increase of competition across many industrial sectors have forced companies to concentrate on their core competences and to outsource those activities in which they do not excel. As a consequence, companies no longer produce and distribute their goods in isolation, but being part of a supply chain or supply network, i.e. a set of interrelated companies who ultimately deliver the goods and services to the final customer. Despite the prevalence of supply chains as the primary form of production and distribution, their performance can be seriously hampered by the complex dynamics resulting from the collaboration and coordination (or lack thereof) among their members.This book provides the reader with modelling tools to understand, analyse and improve the dynamic behaviour of supply chains. It assembles seminal works on supply chain models and recent developments on the topic in order to provide a comprehensive, unified vision of the field for researchers and practitioners who wish to grasp the challenges of supply chain management. Aside presenting the main elements, equations and performance indicators governing the dynamics of a supply chain, and the book addresses issues such as the effect of timely and accurately sharing the information across members, the influence of restrictions on the productive capacities of their members, or the impact of the variability of the lead times, among others. Furthermore, more complex supply chain structures such as non-serial supply networks or closed-loop supply chains are modelled and discussed. Relevant managerial insights regarding the causes of supply chain underperformance, as well as avenues to improve their efficiency can be extracted from the resulting models.
Modelling Survival Data in Medical Research (Chapman & Hall/CRC Texts in Statistical Science)
by David CollettModelling Survival Data in Medical Research, Fourth Edition, describes the analysis of survival data, illustrated using a wide range of examples from biomedical research. Written in a non-technical style, it concentrates on how the techniques are used in practice. Starting with standard methods for summarising survival data, Cox regression and parametric modelling, the book covers many more advanced techniques, including interval-censoring, frailty modelling, competing risks, analysis of multiple events, and dependent censoring. This new edition contains chapters on Bayesian survival analysis and use of the R software. Earlier chapters have been extensively revised and expanded to add new material on several topics. These include methods for assessing the predictive ability of a model, joint models for longitudinal and survival data, and modern methods for the analysis of interval-censored survival data. Features: Presents an accessible account of a wide range of statistical methods for analysing survival data Contains practical guidance on modelling survival data from the author’s many years of experience in teaching and consultancy Shows how Bayesian methods can be used to analyse survival data Includes details on how R can be used to carry out all the methods described, with guidance on the interpretation of the resulting output Contains many real data examples and additional data sets that can be used for coursework All data sets used are available in electronic format from the publisher’s website Modelling Survival Data in Medical Research, Fourth Edition, is an invaluable resource for statisticians in the pharmaceutical industry and biomedical research centres, research scientists and clinicians who are analysing their own data, and students following undergraduate or postgraduate courses in survival analysis.
Modelling Trends and Cycles in Economic Time Series (Palgrave Texts in Econometrics)
by Terence C. MillsModelling trends and cycles in economic time series has a long history, with the use of linear trends and moving averages forming the basic tool kit of economists until the 1970s. Several developments in econometrics then led to an overhaul of the techniques used to extract trends and cycles from time series. In this second edition, Terence Mills expands on the research in the area of trends and cycles over the last (almost) two decades, to highlight to students and researchers the variety of techniques and the considerations that underpin their choice for modelling trends and cycles.
Modelling Under Risk and Uncertainty
by Etienne De RocquignyModelling has permeated virtually all areas of industrial, environmental, economic, bio-medical or civil engineering: yet the use of models for decision-making raises a number of issues to which this book is dedicated:How uncertain is my model ? Is it truly valuable to support decision-making ? What kind of decision can be truly supported and how can I handle residual uncertainty ? How much refined should the mathematical description be, given the true data limitations ? Could the uncertainty be reduced through more data, increased modeling investment or computational budget ? Should it be reduced now or later ? How robust is the analysis or the computational methods involved ? Should / could those methods be more robust ? Does it make sense to handle uncertainty, risk, lack of knowledge, variability or errors altogether ? How reasonable is the choice of probabilistic modeling for rare events ? How rare are the events to be considered ? How far does it make sense to handle extreme events and elaborate confidence figures ? Can I take advantage of expert / phenomenological knowledge to tighten the probabilistic figures ? Are there connex domains that could provide models or inspiration for my problem ?Written by a leader at the crossroads of industry, academia and engineering, and based on decades of multi-disciplinary field experience, Modelling Under Risk and Uncertainty gives a self-consistent introduction to the methods involved by any type of modeling development acknowledging the inevitable uncertainty and associated risks. It goes beyond the "black-box" view that some analysts, modelers, risk experts or statisticians develop on the underlying phenomenology of the environmental or industrial processes, without valuing enough their physical properties and inner modelling potential nor challenging the practical plausibility of mathematical hypotheses; conversely it is also to attract environmental or engineering modellers to better handle model confidence issues through finer statistical and risk analysis material taking advantage of advanced scientific computing, to face new regulations departing from deterministic design or support robust decision-making.Modelling Under Risk and Uncertainty:Addresses a concern of growing interest for large industries, environmentalists or analysts: robust modeling for decision-making in complex systems.Gives new insights into the peculiar mathematical and computational challenges generated by recent industrial safety or environmental control analysis for rare events. Implements decision theory choices differentiating or aggregating the dimensions of risk/aleatory and epistemic uncertainty through a consistent multi-disciplinary set of statistical estimation, physical modelling, robust computation and risk analysis.Provides an original review of the advanced inverse probabilistic approaches for model identification, calibration or data assimilation, key to digest fast-growing multi-physical data acquisition.Illustrated with one favourite pedagogical example crossing natural risk, engineering and economics, developed throughout the book to facilitate the reading and understanding.Supports Master/PhD-level course as well as advanced tutorials for professional trainingAnalysts and researchers in numerical modeling, applied statistics, scientific computing, reliability, advanced engineering, natural risk or environmental science will benefit from this book.
Modelling with Ordinary Differential Equations: A Comprehensive Approach (Chapman & Hall/CRC Numerical Analysis and Scientific Computing Series)
by Alfio BorzìModelling with Ordinary Differential Equations: A Comprehensive Approach aims to provide a broad and self-contained introduction to the mathematical tools necessary to investigate and apply ODE models. The book starts by establishing the existence of solutions in various settings and analysing their stability properties. The next step is to illustrate modelling issues arising in the calculus of variation and optimal control theory that are of interest in many applications. This discussion is continued with an introduction to inverse problems governed by ODE models and to differential games. The book is completed with an illustration of stochastic differential equations and the development of neural networks to solve ODE systems. Many numerical methods are presented to solve the classes of problems discussed in this book. Features: Provides insight into rigorous mathematical issues concerning various topics, while discussing many different models of interest in different disciplines (biology, chemistry, economics, medicine, physics, social sciences, etc.) Suitable for undergraduate and graduate students and as an introduction for researchers in engineering and the sciences Accompanied by codes which allow the reader to apply the numerical methods discussed in this book in those cases where analytical solutions are not available
Modelling with Ordinary Differential Equations
by T.P. DreyerModelling with Ordinary Differential Equations integrates standard material from an elementary course on ordinary differential equations with the skills of mathematical modeling in a number of diverse real-world situations. Each situation highlights a different aspect of the theory or modeling. Carefully selected exercises and projects present excellent opportunities for tutorial sessions and self-study.This text/reference addresses common types of first order ordinary differential equations and the basic theory of linear second order equations with constant coefficients. It also explores the elementary theory of systems of differential equations, Laplace transforms, and numerical solutions. Theorems on the existence and uniqueness of solutions are a central feature. Topics such as curve fitting, time-delay equations, and phase plane diagrams are introduced. The book includes algorithms for computer programs as an integral part of the answer-finding process. Professionals and students in the social and biological sciences, as well as those in physics and mathematics will find this text/reference indispensable for self-study.
Modelling with the Master Equation: Solution Methods and Applications in Social and Natural Sciences
by Günter HaagThis book presents the theory and practical applications of the Master equation approach, which provides a powerful general framework for model building in a variety of disciplines. The aim of the book is to not only highlight different mathematical solution methods, but also reveal their potential by means of practical examples.Part I of the book, which can be used as a toolbox, introduces selected statistical fundamentals and solution methods for the Master equation. In Part II and Part III, the Master equation approach is applied to important applications in the natural and social sciences.The case studies presented mainly hail from the social sciences, including urban and regional dynamics, population dynamics, dynamic decision theory, opinion formation and traffic dynamics; however, some applications from physics and chemistry are treated as well, underlining the interdisciplinary modelling potential of the Master equation approach. Drawing upon the author’s extensive teaching and research experience and consulting work, the book offers a valuable guide for researchers, graduate students and professionals alike.
Modellreduktion: Eine systemtheoretisch orientierte Einführung (Springer Studium Mathematik (Master))
by Peter Benner Heike FaßbenderDieses Lehrbuch führt konsequent algorithmisch orientiert in die Modellreduktion linearer zeitinvarianter Systeme ein; der Fokus liegt hierbei auf systemtheoretischen Methoden. Insbesondere werden modales und balanciertes Abschneiden eingehend behandelt. Darüber hinaus werden Methoden des Momentenabgleichs, basierend auf Krylovraumverfahren und rationaler Interpolation, diskutiert. Dabei werden alle notwendigen Grundlagen sowohl aus der Systemtheorie als auch aus der numerischen linearen Algebra vorgestellt. Die Illustration der in diesem Buch vorgestellten Verfahren der Modellreduktion, sowie einiger der notwendigen, verwendeten Konzepte aus unterschiedlichen mathematischen Bereichen, erfolgt anhand einer Reihe von numerischen Beispielen. Dazu werden die mathematische Software MATLAB® und einige frei verfügbare Software-Pakete eingesetzt, so dass alle Beispiele nachvollzogen werden können.
Models, Algorithms and Technologies for Network Analysis
by Mikhail V. Batsyn Valery A. Kalyagin Panos M. PardalosThis volume compiles the major results of conference participants from the "Third International Conference in Network Analysis" held at the Higher School of Economics, Nizhny Novgorod in May 2013, with the aim to initiate further joint research among different groups. The contributions in this book cover a broad range of topics relevant to the theory and practice of network analysis, including the reliability of complex networks, software, theory, methodology, and applications. Network analysis has become a major research topic over the last several years. The broad range of applications that can be described and analyzed by means of a network has brought together researchers, practitioners from numerous fields such as operations research, computer science, transportation, energy, biomedicine, computational neuroscience and social sciences. In addition, new approaches and computer environments such as parallel computing, grid computing, cloud computing, and quantum computing have helped to solve large scale network optimization problems.
Models, Algorithms, and Technologies for Network Analysis
by Boris I. Goldengorin Valery A. Kalyagin Panos M. PardalosThis volume contains two types of papers--a selection of contributions from the "Second International Conference in Network Analysis" held in Nizhny Novgorod on May 7-9, 2012, and papers submitted to an "open call for papers" reflecting the activities of LATNA at the Higher School for Economics. This volume contains many new results in modeling and powerful algorithmic solutions applied to problems in * vehicle routing * single machine scheduling * modern financial markets * cell formation in group technology * brain activities of left- and right-handers * speeding up algorithms for the maximum clique problem * analysis and applications of different measures in clustering The broad range of applications that can be described and analyzed by means of a network brings together researchers, practitioners, and other scientific communities from numerous fields such as Operations Research, Computer Science, Transportation, Energy, Social Sciences, and more. The contributions not only come from different fields, but also cover a broad range of topics relevant to the theory and practice of network analysis. Researchers, students, and engineers from various disciplines will benefit from the state-of-the-art in models, algorithms, technologies, and techniques presented.
Models, Algorithms, and Technologies for Network Analysis
by Boris Goldengorin Valery A. Kalyagin Panos M. PardalosNetwork Analysis has become a major research topic over the last several years. The broad range of applications that can be described and analyzed by means of a network is bringing together researchers, practitioners and other scientific communities from numerous fields such as Operations Research, Computer Science, Transportation, Energy, Social Sciences, and more. The remarkable diversity of fields that take advantage of Network Analysis makes the endeavor of gathering up-to-date material in a single compilation a useful, yet very difficult, task. The purpose of these proceedings is to overcome this difficulty by collecting the major results found by the participants of the "First International Conference in Network Analysis," held at The University of Florida, Gainesville, USA, from the 14th to the 16th of December 2011. The contributions of this conference not only come from different fields, but also cover a broad range of topics relevant to the theory and practice of network analysis, including the reliability of complex networks, software, theory, methodology and applications.
Models, Algorithms, and Technologies for Network Analysis
by Valery A. Kalyagin Alexey I. Nikolaev Panos M. Pardalos Oleg A. ProkopyevThis valuable source for graduate students and researchers provides a comprehensive introduction to current theories and applications in optimization methods and network models. Contributions to this book are focused on new efficient algorithms and rigorous mathematical theories, which can be used to optimize and analyze mathematical graph structures with massive size and high density induced by natural or artificial complex networks. Applications to social networks, power transmission grids, telecommunication networks, stock market networks, and human brain networks are presented. Chapters in this book cover the following topics: Linear max min fairness Heuristic approaches for high-quality solutions Efficient approaches for complex multi-criteria optimization problems Comparison of heuristic algorithms New heuristic iterative local search Power in network structures Clustering nodes in random graphs Power transmission grid structure Network decomposition problems Homogeneity hypothesis testing Network analysis of international migration Social networks with node attributes Testing hypothesis on degree distribution in the market graphs Machine learning applications to human brain network studies This proceeding is a result of The 6th International Conference on Network Analysis held at the Higher School of Economics, Nizhny Novgorod in May 2016. The conference brought together scientists and engineers from industry, government, and academia to discuss the links between network analysis and a variety of fields.
Models and Methods for Interval-Valued Cooperative Games in Economic Management
by Deng-Feng LiThis book proposes several commonly used interval-valuedsolution concepts of interval-valued cooperative games with transferable utility. It thoroughly investigates these solutions,thereby establishing the properties, models, methods, and applications. Thefirst chapter proposes the interval-valued least square solutions and quadraticprogramming models, methods, and properties. Next, the satisfactory-degree-basednon-linear programming models for computing interval-valued cores andcorresponding bisection algorithm are explained. Finally, the book explores severalsimplification methods of interval-valued solutions: the interval-valued equal division and equalsurplus division values; the interval-valued Shapley, egalitarian Shapley, and discountedShapley values; the interval-valued solidarity and generalized solidarity values;and the interval-valued Banzhaf value. This book is designed for individuals fromdifferent fields and disciplines, such as decision science, game theory, managementscience, operations research, fuzzy sets or fuzzy mathematics, appliedmathematics, industrial engineering, finance, applied economics, expert system,and social economy as well as artificial intelligence. Moreover, it is suitablefor teachers, postgraduates, and researchers from different disciplines:decision analysis, management, operations research, fuzzy mathematics, fuzzysystem analysis, applied mathematics, systems engineering, project management,supply chain management, industrial engineering, applied economics, and hydrologyand water resources.
Models and Modeling Perspectives: A Special Double Issue of mathematical Thinking and Learning
by Richard LeshThis special issue of Mathematical Thinking and Learning describes models and modeling perspectives toward mathematics problem solving, learning, and teaching. The concern is not only the mature forms of models and modeling in communities of scientists and mathematicians, but also the need to initiate students in these forms of thought. The contributions of this issue suggest a variety of ways that students (children through adults) can be introduced to highly productive forms of modeling practices. Collectively, they illustrate how modeling activities often lead to remarkable mathematical achievements by students formerly judged to be too young or too lacking in ability for such sophisticated and powerful forms of mathematical thinking. The papers also illustrate how modeling activities often create productive interdisciplinary niches for mathematical thinking, learning, and problem solving that involve simulations of similar situations that occur when mathematics is useful beyond school.
Models.Behaving.Badly.
by Emanuel DermanEmanuel Derman was a quantitative analyst (Quant) at Goldman Sachs, one of the financial engineers whose mathematical models usurped traders' intuition on Wall Street. The reliance traders put on such quantitative analysis was catastrophic for the economy, setting off the series of financial crises that began to erupt in 2007 with the mortgage crisis and from which we're still recovering. Here Derman looks at why people--bankers in particular--still put so much faith in these models, and why it's a terrible mistake to do so.Though financial models imitate the style of physics by using the language of mathematics, ultimately they deal with human beings. Their similarity confuses the fundamental difference between the aims and possible achievements of the phsyics world and that of the financial world. When we make a model involving human beings, we are trying to force the ugly stepsister's foot into Cinderella's pretty glass slipper. It doesn't fit without cutting off some of the essential parts. Physicists and economists have been too enthusiastic to recognize the limits of their equations in the sphere of human behavior--which of course is what economics is all about. Models.Behaving.Badly includes a personal account Derman's childhood encounter with failed models--the utopia of the kibbutz, his experience as a physicist on Wall Street, and a look at the models quants generated: the benefits they brought and the problems they caused. Derman takes a close look at what a model is, and then he highlights the differences between the success of modeling in physics and its relative failure in economics. Describing the collapse of the subprime mortgage CDO market in 2007, Derman urges us to stop relying on these models where possible, and offers suggestions for mending these models where they might still do some good. This is a fascinating, lyrical, and very human look behind the curtain at the intersection between mathematics and human nature.
Models for Concurrency
by Uri AbrahamConcurrent systems are generally understood in terms of behavioral notions. Models for Concurrency analyzes the subject in terms of events and their temporal relationship rather than on global states. It presents a comprehensive analysis of model theory applied to concurrent protocols, and seeks to provide a theory of concurrency that is both intuitively appealing and rigorously based on mathematical foundations. The book is divided into three main sections. The first introduces the required concepts from model theory, details the structures that are used to model concurrency, gives an in-depth description and explanation of the semantics of a simple language that allows concurrent execution of sequential programs, and deals with the question of resolving executions into higher-level and lower-level granularities. The second and third sections apply the theory developed to practical examples, and an exposition of the producer/consumer problem with details of two solutions is given. The author also deals with message passing, as opposed to shared memory.
Models for Cooperative Games with Fuzzy Relations among the Agents: Fuzzy Communication, Proximity Relation and Fuzzy Permission (Studies in Fuzziness and Soft Computing #355)
by Andrés Jiménez-LosadaThis book offers a comprehensive introduction to cooperative game theory and a practice-oriented reference guide to new models and tools for studying bilateral fuzzy relations among several agents or players. It introduces the reader to several fuzzy models, each of which is first analyzed in the context of classical games (crisp games) and subsequently in the context of fuzzy games. Special emphasis is given to the value of Shapley, which is presented for the first time in the context of fuzzy games.Students and researchers will find here a self-contained reference guide to cooperative fuzzy games, characterized by a wealth of examples, descriptions of a wide range of possible situations, step-by-step explanations of the basic mathematical concepts involved, and easy-to-follow information on axioms and properties.