Models and Modeling in the Sciences
Models and Modeling in the Sciences

Author: Stephen M. Downes

Publisher: Routledge

Published: 2020-07-09

Total Pages: 115

ISBN-13: 1317298063

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Biologists, climate scientists, and economists all rely on models to move their work forward. In this book, Stephen M. Downes explores the use of models in these and other fields to introduce readers to the various philosophical issues that arise in scientific modeling. Readers learn that paying attention to models plays a crucial role in appraising scientific work. This book first presents a wide range of models from a number of different scientific disciplines. After assembling some illustrative examples, Downes demonstrates how models shed light on many perennial issues in philosophy of science and in philosophy in general. Reviewing the range of views on how models represent their targets introduces readers to the key issues in debates on representation, not only in science but in the arts as well. Also, standard epistemological questions are cast in new and interesting ways when readers confront the question, "What makes for a good (or bad) model?" All examples from the sciences and positions in the philosophy of science are presented in an accessible manner. The book is suitable for undergraduates with minimal experience in philosophy and an introductory undergraduate experience in science. Key features: The book serves as a highly accessible philosophical introduction to models and modeling in the sciences, presenting all philosophical and scientific issues in a nontechnical manner. Students and other readers learn to practice philosophy of science by starting with clear examples taken directly from the sciences. While not comprehensive, this book introduces the reader to a wide range of views on key issues in the philosophy of science.

Models and Modeling
Models and Modeling

Author: Myint Swe Khine

Publisher: Springer Science & Business Media

Published: 2011-03-01

Total Pages: 289

ISBN-13: 9400704496

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The 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.

Towards a Competence-Based View on Models and Modeling in Science Education
Towards a Competence-Based View on Models and Modeling in Science Education

Author: Annette Upmeier zu Belzen

Publisher: Springer Nature

Published: 2020-01-01

Total Pages: 317

ISBN-13: 3030302555

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The book takes a closer look at the theoretical and empirical basis for a competence-based view of models and modeling in science learning and science education research. Current thinking about models and modeling is reflected. The focus lies on the development of modeling competence in science education, and on philosophical aspects, including perspectives on nature of science. The book explores, interprets, and discusses models and modeling from the perspective of different theoretical frameworks and empirical results. The extent to which these frameworks can be integrated into a competence-based approach for science education is discussed. In addition, the book provides practical guidance by outlining evidence-based approaches to diagnosing and promoting modeling competence. The aim is to convey a strong understanding of models and modeling for professions such as teacher educators, science education researchers, teachers, and scientists. Different methods for the diagnosis and assessment of modeling competence are presented and discussed with regard to their potential and limitations. The book provides evidence-based ideas about how teachers can be supported in teaching with models and modeling implementing a competence-based approach and, thus, how students can develop their modeling competence. Based on the findings, research challenges for the future are identified.

Modelling-based Teaching in Science Education
Modelling-based Teaching in Science Education

Author: John K. Gilbert

Publisher: Springer

Published: 2016-05-30

Total Pages: 279

ISBN-13: 3319290398

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This book argues that modelling should be a component of all school curricula that aspire to provide ‘authentic science education for all’. The literature on modelling is reviewed and a ‘model of modelling’ is proposed. The conditions for the successful implementation of the ‘model of modelling’ in classrooms are explored and illustrated from practical experience. The roles of argumentation, visualisation, and analogical reasoning, in successful modelling-based teaching are reviewed. The contribution of such teaching to both the learning of key scientific concepts and an understanding of the nature of science are established. Approaches to the design of curricula that facilitate the progressive grasp of the knowledge and skills entailed in modelling are outlined. Recognising that the approach will both represent a substantial change from the ‘content-transmission’ approach to science teaching and be in accordance with current best-practice in science education, the design of suitable approaches to teacher education are discussed. Finally, the challenges that modelling-based education pose to science education researchers, advanced students of science education and curriculum design, teacher educators, public examiners, and textbook designers, are all outlined.

Similarity and Modeling in Science and Engineering
Similarity and Modeling in Science and Engineering

Author: Josef Kuneš

Publisher: Springer Science & Business Media

Published: 2012-04-07

Total Pages: 451

ISBN-13: 1907343776

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The present text sets itself in relief to other titles on the subject in that it addresses the means and methodologies versus a narrow specific-task oriented approach. Concepts and their developments which evolved to meet the changing needs of applications are addressed. This approach provides the reader with a general tool-box to apply to their specific needs. Two important tools are presented: dimensional analysis and the similarity analysis methods. The fundamental point of view, enabling one to sort all models, is that of information flux between a model and an original expressed by the similarity and abstraction Each chapter includes original examples and applications. In this respect, the models can be divided into several groups. The following models are dealt with separately by chapter; mathematical and physical models, physical analogues, deterministic, stochastic, and cybernetic computer models. The mathematical models are divided into asymptotic and phenomenological models. The phenomenological models, which can also be called experimental, are usually the result of an experiment on an complex object or process. The variable dimensionless quantities contain information about the real state of boundary conditions, parameter (non-linearity) changes, and other factors. With satisfactory measurement accuracy and experimental strategy, such models are highly credible and can be used, for example in control systems.

Model Based Inference in the Life Sciences
Model Based Inference in the Life Sciences

Author: David R. Anderson

Publisher: Springer Science & Business Media

Published: 2007-12-22

Total Pages: 203

ISBN-13: 0387740759

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This textbook introduces a science philosophy called "information theoretic" based on Kullback-Leibler information theory. It focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation.

Modeling Theory in Science Education
Modeling Theory in Science Education

Author: Ibrahim A. Halloun

Publisher: Springer Science & Business Media

Published: 2007-01-25

Total Pages: 262

ISBN-13: 1402021402

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This book is the culmination of over twenty years of work toward a pedagogical theory that promotes experiential learning of model-laden theory and inquiry in science. The book focuses as much on course content as on instruction and learning methodology, presenting practical aspects that have repeatedly demonstrated their value in fostering meaningful and equitable learning of physics and other science courses at the secondary school and college levels.

Formal Modeling in Social Science
Formal Modeling in Social Science

Author: Carol Mershon

Publisher: University of Michigan Press

Published: 2019-09-03

Total Pages: 257

ISBN-13: 0472054236

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A formal model in the social sciences builds explanations when it structures the reasoning underlying a theoretical argument, opens venues for controlled experimentation, and can lead to hypotheses. Yet more importantly, models evaluate theory, build theory, and enhance conjectures. Formal Modeling in Social Science addresses the varied helpful roles of formal models and goes further to take up more fundamental considerations of epistemology and methodology. The authors integrate the exposition of the epistemology and the methodology of modeling and argue that these two reinforce each other. They illustrate the process of designing an original model suited to the puzzle at hand, using multiple methods in diverse substantive areas of inquiry. The authors also emphasize the crucial, though underappreciated, role of a narrative in the progression from theory to model. Transparency of assumptions and steps in a model means that any analyst will reach equivalent predictions whenever she replicates the argument. Hence, models enable theoretical replication, essential in the accumulation of knowledge. Formal Modeling in Social Science speaks to scholars in different career stages and disciplines and with varying expertise in modeling.

Modeling Life
Modeling Life

Author: Alan Garfinkel

Publisher: Springer

Published: 2017-09-06

Total Pages: 456

ISBN-13: 3319597310

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This book develops the mathematical tools essential for students in the life sciences to describe interacting systems and predict their behavior. From predator-prey populations in an ecosystem, to hormone regulation within the body, the natural world abounds in dynamical systems that affect us profoundly. Complex feedback relations and counter-intuitive responses are common in nature; this book develops the quantitative skills needed to explore these interactions. Differential equations are the natural mathematical tool for quantifying change, and are the driving force throughout this book. The use of Euler’s method makes nonlinear examples tractable and accessible to a broad spectrum of early-stage undergraduates, thus providing a practical alternative to the procedural approach of a traditional Calculus curriculum. Tools are developed within numerous, relevant examples, with an emphasis on the construction, evaluation, and interpretation of mathematical models throughout. Encountering these concepts in context, students learn not only quantitative techniques, but how to bridge between biological and mathematical ways of thinking. Examples range broadly, exploring the dynamics of neurons and the immune system, through to population dynamics and the Google PageRank algorithm. Each scenario relies only on an interest in the natural world; no biological expertise is assumed of student or instructor. Building on a single prerequisite of Precalculus, the book suits a two-quarter sequence for first or second year undergraduates, and meets the mathematical requirements of medical school entry. The later material provides opportunities for more advanced students in both mathematics and life sciences to revisit theoretical knowledge in a rich, real-world framework. In all cases, the focus is clear: how does the math help us understand the science?

Mathematical Modeling in Science and Engineering
Mathematical Modeling in Science and Engineering

Author: Ismael Herrera

Publisher: John Wiley & Sons

Published: 2012-03-19

Total Pages: 259

ISBN-13: 1118207203

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A powerful, unified approach to mathematical and computational modeling in science and engineering Mathematical and computational modeling makes it possible to predict the behavior of a broad range of systems across a broad range of disciplines. This text guides students and professionals through the axiomatic approach, a powerful method that will enable them to easily master the principle types of mathematical and computational models used in engineering and science. Readers will discover that this axiomatic approach not only enables them to systematically construct effective models, it also enables them to apply these models to any macroscopic physical system. Mathematical Modeling in Science and Engineering focuses on models in which the processes to be modeled are expressed as systems of partial differential equations. It begins with an introductory discussion of the axiomatic formulation of basic models, setting the foundation for further topics such as: Mechanics of classical and non-classical continuous systems Solute transport by a free fluid Flow of a fluid in a porous medium Multiphase systems Enhanced oil recovery Fluid mechanics Throughout the text, diagrams are provided to help readers visualize and better understand complex mathematical concepts. A set of exercises at the end of each chapter enables readers to put their new modeling skills into practice. There is also a bibliography in each chapter to facilitate further investigation of individual topics. Mathematical Modeling in Science and Engineering is ideal for both students and professionals across the many disciplines of science and engineering that depend on mathematical and computational modeling to predict and understand complex systems.