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Models and Idealizations in Science: Artifactual and Fictional Approaches (Logic, Epistemology, and the Unity of Science #50)
by Juan Redmond Alejandro CassiniThis book provides both an introduction to the philosophy of scientific modeling and a contribution to the discussion and clarification of two recent philosophical conceptions of models: artifactualism and fictionalism. These can be viewed as different stances concerning the standard representationalist account of scientific models. By better understanding these two alternative views, readers will gain a deeper insight into what a model is as well as how models function in different sciences.Fictionalism has been a traditional epistemological stance related to antirealist construals of laws and theories, such as instrumentalism and inferentialism. By contrast, the more recent fictional view of models holds that scientific models must be conceived of as the same kind of entities as literary characters and places. This approach is essentially an answer to the ontological question concerning the nature of models, which in principle is not incompatible with a representationalist account of the function of models. The artifactual view of models is an approach according to which scientific models are epistemic artifacts, whose main function is not to represent the phenomena but rather to provide epistemic access to them. It can be conceived of as a non-representationalist and pragmatic account of modeling, which does not intend to focus on the ontology of models but rather on the ways they are built and used for different purposes. The different essays address questions such as the artifactual view of idealization, the use of information theory to elucidate the concepts of abstraction and idealization, the deidealization of models, the nature of scientific fictions, the structural account of representation and the ontological status of structures, the role of surrogative reasoning with models, and the use of models for explaining and predicting physical phenomena.
Models and Inferences in Science
by Emiliano Ippoliti Thomas Nickles Fabio SterpettiThe book answers long-standing questions on scientific modeling andinference across multiple perspectives and disciplines, including logic,mathematics, physics and medicine. The different chapters cover a variety ofissues, such as the role models play in scientific practice; the way scienceshapes our concept of models; ways of modeling the pursuit of scientificknowledge; the relationship between our concept of models and our concept ofscience. The book also discusses models and scientific explanations; models inthe semantic view of theories; the applicability of mathematical models to thereal world and their effectiveness; the links between models and inferences;and models as a means for acquiring new knowledge. It analyzes differentexamples of models in physics, biology, mathematics and engineering. Writtenfor researchers and graduate students, it provides a cross-disciplinaryreference guide to the notion and the use of models and inferences in science.
Models and Methods for Biological Evolution: Mathematical Models and Algorithms to Study Evolution
by Gilles Didier Stéphane GuindonBiological evolution is the phenomenon concerning how species are born, are transformed or disappear over time. Its study relies on sophisticated methods that involve both mathematical modeling of the biological processes at play and the design of efficient algorithms to fit these models to genetic and morphological data. Models and Methods for Biological Evolution outlines the main methods to study evolution and provides a broad overview illustrating the variety of formal approaches used, notably including combinatorial optimization, stochastic models and statistical inference techniques. Some of the most relevant applications of these methods are detailed, concerning, for example, the study of migratory events of ancient human populations or the progression of epidemics. This book should thus be of interest to applied mathematicians interested in central problems in biology, and to biologists eager to get a deeper understanding of widely used techniques of evolutionary data analysis.
Models and Modeling
by Myint Swe Khine Issa M. SalehThe process of developing models, known as modeling, allows scientists to visualize difficult concepts, explain complex phenomena and clarify intricate theories. In recent years, science educators have greatly increased their use of modeling in teaching, especially real-time dynamic modeling, which is central to a scientific investigation. Modeling in science teaching is being used in an array of fields, everything from primary sciences to tertiary chemistry to college physics, and it is sure to play an increasing role in the future of education. Models and Modeling: Cognitive Tools for Scientific Enquiry is a comprehensive introduction to the use of models and modeling in science education. It identifies and describes many different modeling tools and presents recent applications of modeling as a cognitive tool for scientific enquiry.
Models and Techniques in Stroke Biology
by Amit Kumar Tripathi Abhishek Kumar SinghThis book summarizes various tools and techniques used to provide insights into the cellular and molecular pathophysiology of stroke. It also presents rodent animal models to help shed light on the pathophysiology of ischemic stroke. Presenting the latest information on the different types of stroke, including embolic, filament, photothrombotic, and bilateral common carotid artery, the book also describes techniques that are used for confirmation of stroke surgery, such as laser speckle imaging (LSI) and laser Doppler flowmetry (LDF), and discusses the non-human primates that are used in stroke surgery, cerebral venous sinuous thrombosis, and neurobehavioral assessment. Lastly, it analyzes various neuroprotective agents to treat and prevent ischemic stroke, and examines the challenges and advances in treating and preventing acute ischemic stroke.
Models as Make-Believe
by Adam ToonScientists often try to understand the world by building simplified and idealised models of it. Adam Toon develops a new approach to scientific models by comparing them to the dolls and toy trucks of children's imaginative games, and offers a unified framework to solve difficult metaphysical problems and help to make sense of scientific practice.
Models for Bonding in Chemistry, 1st Edition
by Valerio MagnascoA readable little book assisting the student in understanding, in a nonmathematical way, the essentials of the different bonds occurring in chemistry. Starting with a short, self-contained,introduction, Chapter 1 presents the essential elements of the variation approach to either total or second-order molecular energies, the system of atomic units (au) necessary to simplify all mathematical expressions, and an introductory description of the electron distribution in molecules. Using mostly 2x2 Hückel secular equations, Chapter 2, by far the largest part of the book because of the many implications of the chemical bond, introduces a model of bonding in homonuclear and heteronuclear diatomics, multiple and delocalized bonds in hydrocarbons, and the stereochemistry of chemical bonds in polyatomic molecules, in a word, a model of the strong first-order interactions originating the chemical bond. In Chapter 3 the Hückel model of the linear polyene chain is used to explain the origin of band structure in the 1-dimensional crystal. Chapter 4 deals with a simple two-state model of weak interactions, introducing the reader to understand second-order electric properties of molecules and VdW bonding between closed shells. Lastly, Chapter 5 studies the structure of H-bonded dimers and the nature of the hydrogen bond, which has a strength intermediate between a VdW bond and a weak chemical bond. Besides a qualitative MO approach based on HOMO-LUMO charge transfer from an electron donor to an electron acceptor molecule, a quantitative electrostatic approach is presented yielding an electrostatic model working even at its simplest pictorial level. A list of alphabetically ordered references, author and subject indices complete the book.
Models for Research and Understanding: Exploring Dynamic Systems, Unconventional Approaches, and Applications (Simulation Foundations, Methods and Applications)
by Stanislaw RaczynskiThis introductory textbook/reference addresses the fundamental and mostly applied kinds of models. The focus is on models of dynamic systems that move and change over time. However, the work also proposes new methods of uncertainty treatment, offering supporting examples.Topics and features: Chapters suitable for textbook use in teaching modeling and simulationIncludes sections of questions and answers, helpful in didactic workProposes new methodology in addition to examining conventional approachesOffers some cognitive, more abstract models to give a wider insight on model building The book’s readership may consist of researchers working on multidisciplinary problems, as well educators and students. It may be used while teaching computer simulation, applied mathematics, system analysis and system dynamics.
Models in Ecosystem Science
by Charles D. Canham Jonathan J. Cole William K. LauenrothQuantitative models are crucial to almost every area of ecosystem science. They provide a logical structure that guides and informs empirical observations of ecosystem processes. They play a particularly crucial role in synthesizing and integrating our understanding of the immense diversity of ecosystem structure and function. Increasingly, models are being called on to predict the effects of human actions on natural ecosystems. Despite the widespread use of models, there exists intense debate within the field over a wide range of practical and philosophical issues pertaining to quantitative modeling. This book--which grew out of a gathering of leading experts at the ninth Cary Conference--explores those issues. The book opens with an overview of the status and role of modeling in ecosystem science, including perspectives on the long-running debate over the appropriate level of complexity in models. This is followed by eight chapters that address the critical issue of evaluating ecosystem models, including methods of addressing uncertainty. Next come several case studies of the role of models in environmental policy and management. A section on the future of modeling in ecosystem science focuses on increasing the use of modeling in undergraduate education and the modeling skills of professionals within the field. The benefits and limitations of predictive (versus observational) models are also considered in detail. Written by stellar contributors, this book grants access to the state of the art and science of ecosystem modeling.
Models of Biopolymers By Ring-Opening Polymerization
by Stanislaw PenczekThere are a number of methods used to synthetically prepare biopolymers, their models, and bioanalogous polymers. This work approaches the syntheses of the three major groups of biopolymers existing in nature - polypeptides, polysaccharides, and nucleic and teichoic acids - by ring-opening polymerization. Until now, this method has never been reviewed uniformly for these three groups. The majority of models prepared by ring-opening polymerization can not reach the complexity of the actual biological molecules. However, a better understanding of these biopolymers will aid in the use of such molecules in several fields of application in research and other high technologies, where they mimic functions of related biopolymers in living organisms.
Models of Calcium Signalling
by Vivien Kirk James Sneyd Geneviève Dupont Martin FalckeThis book discusses the ways in which mathematical, computational, and modelling methods can be used to help understand the dynamics of intracellular calcium. The concentration of free intracellular calcium is vital for controlling a wide range of cellular processes, and is thus of great physiological importance. However, because of the complex ways in which the calcium concentration varies, it is also of great mathematical interest. This book presents the general modelling theory as well as a large number of specific case examples, to show how mathematical modelling can interact with experimental approaches, in an interdisciplinary and multifaceted approach to the study of an important physiological control mechanism. Geneviève Dupont is FNRS Research Director at the Unit of Theoretical Chronobiology of the Université Libre de Bruxelles; Martin Falcke is head of the Mathematical Cell Physiology group at the Max Delbrück Center for Molecular Medicine, Berlin; Vivien Kirk is an Associate Professor in the Department of Mathematics at the University of Auckland, New Zeal∧ James Sneyd is a Professor in the Department of Mathematics at The University of Auckland, New Zealand.
Models of Care in Maternity Services
by Sabaratnam Arulkumaran Tahir Mahmood Charnjit Dhillon Philip OwenThis book helps all those working in maternity services to improve the quality of the care they offer. Improvement is driven by clinical effectiveness and increasing patient demands, and for each area of practice described this book outlines the service organisation needed to achieve this improvement. The goal is to help clinicians take responsibility for developing services that meet the needs of their patients as well as managing their individual medical conditions. The book demonstrates that much can be achieved within current resources and without major additional expense. Different approaches are demonstrated, but the key issue is the patient pathway. Trainees, clinicians, managers and commissioners of services will find this book of practical value. There should be a copy on the shelves of every hospital obstetric unit.
Models of Innovation: The History of an Idea
by Benoît GodinModels abound in science, technology, and society (STS) studies and in science, technology, and innovation (STI) studies. They are continually being invented, with one author developing many versions of the same model over time. At the same time, models are regularly criticized. Such is the case with the most influential model in STS-STI: the linear model of innovation.In this book, Benoît Godin examines the emergence and diffusion of the three most important conceptual models of innovation from the early twentieth century to the late 1980s: stage models, linear models, and holistic models. Godin first traces the history of the models of innovation constructed during this period, considering why these particular models came into being and what use was made of them. He then rethinks and debunks the historical narratives of models developed by theorists of innovation. Godin documents a greater diversity of thinkers and schools than in the conventional account, tracing a genealogy of models beginning with anthropologists, industrialists, and practitioners in the first half of the twentieth century to their later formalization in STS-STI. Godin suggests that a model is a conceptualization, which could be narrative, or a set of conceptualizations, or a paradigmatic perspective, often in pictorial form and reduced discursively to a simplified representation of reality. Why are so many things called models? Godin claims that model has a rhetorical function. First, a model is a symbol of "scientificity." Second, a model travels easily among scholars and policy makers. Calling a conceptualization or narrative or perspective a model facilitates its propagation.
Models of Innovation: The History of an Idea (Inside Technology)
by Benoit GodinBenoît Godin is a Professor at the Institut national de la recherche scientifique, Montreal.Models abound in science, technology, and society (STS) studies and in science, technology, and innovation (STI) studies. They are continually being invented, with one author developing many versions of the same model over time. At the same time, models are regularly criticized. Such is the case with the most influential model in STS-STI: the linear model of innovation.In this book, Benoît Godin examines the emergence and diffusion of the three most important conceptual models of innovation from the early twentieth century to the late 1980s: stage models, linear models, and holistic models. Godin first traces the history of the models of innovation constructed during this period, considering why these particular models came into being and what use was made of them. He then rethinks and debunks the historical narratives of models developed by theorists of innovation. Godin documents a greater diversity of thinkers and schools than in the conventional account, tracing a genealogy of models beginning with anthropologists, industrialists, and practitioners in the first half of the twentieth century to their later formalization in STS-STI. Godin suggests that a model is a conceptualization, which could be narrative, or a set of conceptualizations, or a paradigmatic perspective, often in pictorial form and reduced discursively to a simplified representation of reality. Why are so many things called models? Godin claims that model has a rhetorical function. First, a model is a symbol of “scientificity.” Second, a model travels easily among scholars and policy makers. Calling a conceptualization or narrative or perspective a model facilitates its propagation.
Models of Life: Dynamics and Regulation in Biological Systems
by Kim SneppenReflecting the major advances that have been made in the field over the past decade, this book provides an overview of current models of biological systems. The focus is on simple quantitative models, highlighting their role in enhancing our understanding of the strategies of gene regulation and dynamics of information transfer along signalling pathways, as well as in unravelling the interplay between function and evolution. The chapters are self-contained, each describing key methods for studying the quantitative aspects of life through the use of physical models. They focus, in particular, on connecting the dynamics of proteins and DNA with strategic decisions on the larger scale of a living cell, using E. coli and phage lambda as key examples. Encompassing fields such as quantitative molecular biology, systems biology and biophysics, this book will be a valuable tool for students from both biological and physical science backgrounds.
Models of Lung Disease: Microscopy and Structural Methods (Lung Biology In Health And Disease Ser. #47)
by Joan GilThis research-level reference provides a review of the morphological techniques that have become a primary method of anatomical study correlating structure and function in lung physiology and pathology. Detailing the evolution of anatomy as a research discipline, it explores general structural techn
Models of Madness: Psychological, social and biological approaches to schizophrenia
by John Read Loren R. Mosher Richard P. BentallIs schizophrenia an illness? Is madness preventable? This controversial, but carefully researched, book argues that what psychiatrists call "schizophrenia" is not an illness.
Models of My Life
by Herbert A. SimonIn this candid and witty autobiography, Nobel laureate Herbert A. Simon looks at his distinguished and varied career, continually asking himself whether (and how) what he learned as a scientist helps to explain other aspects of his life.A brilliant polymath in an age of increasing specialization, Simon is one of those rare scholars whose work defines fields of inquiry. Crossing disciplinary lines in half a dozen fields, Simon's story encompasses an explosion in the information sciences, the transformation of psychology by the information-processing paradigm, and the use of computer simulation for modeling the behavior of highly complex systems.Simon's theory of bounded rationality led to a Nobel Prize in economics, and his work on building machines that think -- based on the notion that human intelligence is the rule-governed manipulation of symbols -- laid conceptual foundations for the new cognitive science. Subsequently, contrasting metaphors of the maze (Simon's view) and of the mind (neural nets) have dominated the artificial intelligence debate.There is also a warm account of his successful marriage and of an unconsummated love affair, letters to his children, columns, a short story, and political and personal intrigue in academe.
Models of My Life (The\mit Press Ser.)
by Herbert A. SimonIn this candid and witty autobiography, Nobel laureate Herbert A. Simon looks at his distinguished and varied career, continually asking himself whether (and how) what he learned as a scientist helps to explain other aspects of his life.A brilliant polymath in an age of increasing specialization, Simon is one of those rare scholars whose work defines fields of inquiry. Crossing disciplinary lines in half a dozen fields, Simon's story encompasses an explosion in the information sciences, the transformation of psychology by the information-processing paradigm, and the use of computer simulation for modeling the behavior of highly complex systems.Simon's theory of bounded rationality led to a Nobel Prize in economics, and his work on building machines that think—based on the notion that human intelligence is the rule-governed manipulation of symbols—laid conceptual foundations for the new cognitive science. Subsequently, contrasting metaphors of the maze (Simon's view) and of the mind (neural nets) have dominated the artificial intelligence debate.There is also a warm account of his successful marriage and of an unconsummated love affair, letters to his children, columns, a short story, and political and personal intrigue in academe.
Models of Neurons and Perceptrons: Selected Problems and Challenges (Studies In Computational Intelligence #770)
by Andrzej BieleckiThis book describes models of the neuron and multilayer neural structures, with a particular focus on mathematical models. It also discusses electronic circuits used as models of the neuron and the synapse, and analyses the relations between the circuits and mathematical models in detail. The first part describes the biological foundations and provides a comprehensive overview of the artificial neural networks. The second part then presents mathematical foundations, reviewing elementary topics, as well as lesser-known problems such as topological conjugacy of dynamical systems and the shadowing property. The final two parts describe the models of the neuron, and the mathematical analysis of the properties of artificial multilayer neural networks. Combining biological, mathematical and electronic approaches, this multidisciplinary book it useful for the mathematicians interested in artificial neural networks and models of the neuron, for computer scientists interested in formal foundations of artificial neural networks, and for the biologists interested in mathematical and electronic models of neural structures and processes.
Models of Science Dynamics
by Peter Van Besselaar Andrea Scharnhorst Katy BörnerModels of Science Dynamics aims to capture the structure and evolution of science, the emerging arena in which scholars, science and the communication of science become themselves the basic objects of research. In order to capture the essence of phenomena as diverse as the structure of co-authorship networks or the evolution of citation diffusion patterns, such models can be represented by conceptual models based on historical and ethnographic observations, mathematical descriptions of measurable phenomena, or computational algorithms. Despite its evident importance, the mathematical modeling of science still lacks a unifying framework and a comprehensive study of the topic. This volume fills this gap, reviewing and describing major threads in the mathematical modeling of science dynamics for a wider academic and professional audience. The model classes presented cover stochastic and statistical models, system-dynamics approaches, agent-based simulations, population-dynamics models, and complex-network models. The book comprises an introduction and a foundational chapter that defines and operationalizes terminology used in the study of science, as well as a review chapter that discusses the history of mathematical approaches to modeling science from an algorithmic-historiography perspective. It concludes with a survey of remaining challenges for future science models and their relevance for science and science policy.
Models of Time and Space from Astrophysics and World Cultures: The Foundations of Astrophysical Reality from Across the Centuries (Astronomers' Universe)
by Bryan E. PenpraseModels of Time and Space from Astrophysics and World Cultures explores how our conceptions of time, space, and the physical universe have evolved across cultures throughout the centuries. Developed with a humanistic approach, this book blends historical sources, biographical profiles of exceptional scientists, and the latest discoveries in both astrophysics and particle physics. This rich read describes the incredible insights and ultimate limits of our knowledge, the physical universe, and how ideas old and new have converged, across the world, to build our current understanding of reality. From the Large Hadron Collider to the James Webb Space Telescope, we have mapped the universe from the smallest to largest scales; allowing us to gain fundamental knowledge that has transformed our understanding of the universe. The chapters herein will teach you about dark matter and dark energy, gravitational waves and other complex parts of the cosmos. Along the way, you will learn a thing or two about quantum mechanics, parallel universes, and the ultimate boundaries of the observable universe. This book cultivates insight from a variety of cultural traditions, including perspectives from both modern and ancient cultures, in order to show how our modern conceptions of space and time have arisen from the ongoing explorations within ancient world civilizations.It is a valuable, intriguing and insightful volume for those interested in the fields of historical astronomy and cultural astronomy, as well as for anyone interested in learning about the latest finds from the field of physics and astrophysics.
Models of Tree and Stand Dynamics: Theory, Formulation and Application
by Harry T. Valentine Annikki MäkeläThe book is designed to be a textbook for university students (MSc-PhD level) and a reference for researchers and practitioners. It is an introduction to dynamic modelling of forest growth based on ecological theory but aiming for practical applications for forest management under environmental change. It is largely based on the work and research findings of the authors, but it also covers a wide range of literature relevant to process-based forest modelling in general. The models presented in the book also serve as tools for research and can be elaborated further as new research findings emerge. The material in the book is arranged such that the student starts from basic concepts and formulations, then moves towards more advanced theories and methods, finally learning about parameter estimation, model testing, and practical application. Exercises with solutions and hands-on R-code are provided to help the student digest the concepts and become proficient with the methods. The book should be useful for both forest ecologists who want to become modellers, and for applied mathematicians who want to learn about forest ecology. The basic concepts and theory are formulated in the first four chapters, including a review of traditional descriptive forest models, basic concepts of carbon balance modelling applied to trees, and theories and models of tree and forest structure. Chapter 5 provides a synthesis in the form of a core model which is further elaborated and applied in the subsequent chapters. The more advanced theories and methods in Chapters 6 and 7 comprise aspects of competition through tree interactions, and eco-evolutionary modelling, including optimisation and game theory, a topical and fast developing area of ecological modelling under climate change. Chapters 8 and 9 are devoted to parameter estimation and model calibration, showing how empirical and process-based methods and related data sources can be bridged to provide reliable predictions. Chapter 10 demonstrates some practical applications and possible future development paths of the approach. The approach in this book is unique in that the models presented are based on ecological theory and research findings, yet sufficiently simple in structure to lend themselves readily to practical application, such as regional estimates of harvest potential, or satellite-based monitoring of growth. The applicability is also related to the objective of bridging empirical and process-based approaches through data assimilation methods that combine research-based ecological measurements with standard forestry data. Importantly, the ecological basis means that it is possible to build on the existing models to advance the approach as new research findings become available.
Models of the Atomic Nucleus
by Norman D. CookVery intuitive and physically precise visualization software for nuclear models Database of all nuclei and isotopes included All nuclear parameters are adjustable in a wide range Comprehensive and introductory book on nuclear models Platform invariant software (Windows, Unix, Mac)
Models, Algorithms and Technologies for Network Analysis
by Panos M. Pardalos Valery A. Kalyagin Mikhail V. BatsynThis 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.