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Simulation For The Social Scientist

Simulation For The Social Scientist Author Gilbert, Nigel
ISBN-10 9780335225125
Release 2005-02-01
Pages 295
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Social sciences -- Simulation methods. Social interaction -- Computer simulation. Social sciences -- Mathematical models. (publisher)



Simulation For The Social Scientist

Simulation For The Social Scientist Author Gilbert, Nigel
ISBN-10 0335216005
Release 2005-02-01
Pages 295
Download Link Click Here

Social sciences -- Simulation methods. Social interaction -- Computer simulation. Social sciences -- Mathematical models. (publisher)



Monte Carlo Simulation and Resampling Methods for Social Science

Monte Carlo Simulation and Resampling Methods for Social Science Author Thomas M. Carsey
ISBN-10 9781483324920
Release 2013-08-06
Pages 304
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Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, Monte Carlo Simulation and Resampling Methods for Social Science, by Thomas M. Carsey and Jeffrey J. Harden, examines abstract principles, such as bias, efficiency, and measures of uncertainty in an intuitive, visual way. Instead of thinking in the abstract about what would happen to a particular estimator "in repeated samples," the book uses simulation to actually create those repeated samples and summarize the results. The book includes basic examples appropriate for readers learning the material for the first time, as well as more advanced examples that a researcher might use to evaluate an estimator he or she was using in an actual research project. The book also covers a wide range of topics related to Monte Carlo simulation, such as resampling methods, simulations of substantive theory, simulation of quantities of interest (QI) from model results, and cross-validation. Complete R code from all examples is provided so readers can replicate every analysis presented using R.



Tools and Techniques for Social Science Simulation

Tools and Techniques for Social Science Simulation Author Ramzi Suleiman
ISBN-10 9783642517440
Release 2012-12-06
Pages 387
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The use of computer simulations to study social phenomena has grown rapidly during the last few years. Many social scientists from the fields of economics, sociology, psychology and other disciplines now use computer simulations to study a wide range of social phenomena. The availability of powerful personal computers, the development of multidisciplinary approaches and the use of artificial intelligence models have all contributed to this development. The benefits of using computer simulations in the social sciences are obvious. This holds true for the use of simulations as tools for theory building and for its implementation as a tool for sensitivity analysis and parameter optimization in application-oriented models. In both, simulation provides powerful tools for the study of complex social systems, especially for dynamic and multi-agent social systems in which mathematical tractability is often impossible. The graphical display of simulation output renders it user friendly to many social scientists that lack sufficient familiarity with the language of mathematics. The present volume aims to contribute in four directions: (1) To examine theoretical and methodological issues related to the application of simulations in the social sciences. By this we wish to promote the objective of designing a unified, user-friendly, simulation toolkit which could be applied to diverse social problems. While no claim is made that this objective has been met, the theoretical issues treated in Part 1 of this volume are a contribution towards this objective.



Introduction to Computational Social Science

Introduction to Computational Social Science Author Claudio Cioffi-Revilla
ISBN-10 9783319501314
Release 2017-06-22
Pages 618
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This textbook provides a comprehensive and reader-friendly introduction to the field of computational social science (CSS). Presenting a unified treatment, the text examines in detail the four key methodological approaches of automated social information extraction, social network analysis, social complexity theory, and social simulation modeling. This updated new edition has been enhanced with numerous review questions and exercises to test what has been learned, deepen understanding through problem-solving, and to practice writing code to implement ideas. Topics and features: contains more than a thousand questions and exercises, together with a list of acronyms and a glossary; examines the similarities and differences between computers and social systems; presents a focus on automated information extraction; discusses the measurement, scientific laws, and generative theories of social complexity in CSS; reviews the methodology of social simulations, covering both variable- and object-oriented models.



Environmental Social Science

Environmental Social Science Author Emilio F. Moran
ISBN-10 9781444358278
Release 2011-09-09
Pages 232
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Environmental Social Science offers a new synthesis of environmental studies, defining the nature of human-environment interactions and providing the foundation for a new cross-disciplinary enterprise that will make critical theories and research methods accessible across the natural and social sciences. Makes key theories and methods of the social sciences available to biologists and other environmental scientists Explains biological theories and concepts for the social sciences community working on the environment Helps bridge one of the difficult divides in collaborative work in human-environment research Includes much-needed descriptions of how to carry out research that is multinational, multiscale, multitemporal, and multidisciplinary within a complex systems theory context



Agent Based Models

Agent Based Models Author Nigel Gilbert
ISBN-10 9781412949644
Release 2008
Pages 98
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Aimed at readers with minimal experience in computer programming, this brief book provides a theoretical and methodological rationale for using ABM in the social sciences. It goes on to describe some carefully chosen examples from different disciplines, illustrating different approaches to ABM. It concludes with practical advice about how to design and create ABM, a discussion of validation procedures, and some guidelines about publishing articles based on ABM.



Numerical Issues in Statistical Computing for the Social Scientist

Numerical Issues in Statistical Computing for the Social Scientist Author Micah Altman
ISBN-10 0471236330
Release 2003-12-18
Pages 323
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At last—a social scientist's guide through the pitfalls of modern statistical computing Addressing the current deficiency in the literature on statistical methods as they apply to the social and behavioral sciences, Numerical Issues in Statistical Computing for the Social Scientist seeks to provide readers with a unique practical guidebook to the numerical methods underlying computerized statistical calculations specific to these fields. The authors demonstrate that knowledge of these numerical methods and how they are used in statistical packages is essential for making accurate inferences. With the aid of key contributors from both the social and behavioral sciences, the authors have assembled a rich set of interrelated chapters designed to guide empirical social scientists through the potential minefield of modern statistical computing. Uniquely accessible and abounding in modern-day tools, tricks, and advice, the text successfully bridges the gap between the current level of social science methodology and the more sophisticated technical coverage usually associated with the statistical field. Highlights include: A focus on problems occurring in maximum likelihood estimation Integrated examples of statistical computing (using software packages such as the SAS, Gauss, Splus, R, Stata, LIMDEP, SPSS, WinBUGS, and MATLAB®) A guide to choosing accurate statistical packages Discussions of a multitude of computationally intensive statistical approaches such as ecological inference, Markov chain Monte Carlo, and spatial regression analysis Emphasis on specific numerical problems, statistical procedures, and their applications in the field Replications and re-analysis of published social science research, using innovative numerical methods Key numerical estimation issues along with the means of avoiding common pitfalls A related Web site includes test data for use in demonstrating numerical problems, code for applying the original methods described in the book, and an online bibliography of Web resources for the statistical computation Designed as an independent research tool, a professional reference, or a classroom supplement, the book presents a well-thought-out treatment of a complex and multifaceted field.



Theory Construction and Model building Skills

Theory Construction and Model building Skills Author James Jaccard
ISBN-10 9781606233405
Release 2010-01-01
Pages 391
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Meeting a crucial need for graduate students and newly minted researchers, this innovative text provides hands-on tools for generating ideas and translating them into formal theories. It is illustrated with numerous practical examples drawn from multiple social science disciplines and research settings. The authors offer clear guidance for defining constructs, thinking through relationships and processes that link constructs, and deriving new theoretical models (or building on existing ones) based on those relationships. Step by step, they show readers how to use causal analysis, mathematical modeling, simulations, and grounded and emergent approaches to theory construction. A chapter on writing about theories contains invaluable advice on crafting effective papers and grant applications. Useful pedagogical features in every chapter include: Application exercises and concept exercises Lists of key terms and engaging topical boxes Annotated suggestions for further reading. This book is intended for graduate students in a range of disciplines, including psychology, education, sociology, health, and management, as well as social scientists pursing research careers in academic or other settings. It can serve as a primary text in graduate-level courses in theory construction or as a supplemental text in courses on research methodology, theories of a particular discipline, grant writing, or the dissertation.



Simulation and Social Theory

Simulation and Social Theory Author Sean Cubitt
ISBN-10 9780857026408
Release 2000-12-22
Pages 192
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This insightful book is the first to critically examine the ideas of some of the key thinkers of simulation. It addresses the work of Baudrillard, Debord, Virilio and Eco, clarifying their arguments by referring to the intellectual and social worlds each emerged from distilling what is important from their discussions. The book argues for a critical and selective use of the concept of simulation. Like the idea of ideology, simulation is a political theory, but it has also become a deeply pessimistic theory of the end of history and the impossibility of positive change. Through a series of reflections on the meaning of theme parks, warfare and computer modelling, Sean Cubitt demonstrates the strengths and limitations of the simulation thesis.



Generative Social Science Studies in Agent Based Computational Modeling

Generative Social Science  Studies in Agent Based Computational Modeling Author Joshua M. Epstein
ISBN-10 9781400842872
Release 2012-01-02
Pages 384
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Agent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation. This book represents a powerful consolidation of Epstein's interdisciplinary research activities in the decade since the publication of his and Robert Axtell's landmark volume, Growing Artificial Societies. Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.



Social Emergence

Social Emergence Author Robert Keith Sawyer
ISBN-10 0521844649
Release 2005-10-27
Pages 276
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Can we understand important social issues by studying individual personalities and decisions? Or are societies somehow more than the people in them? Sociologists have long believed that psychology can't explain what happens when people work together in complex modern societies. In contrast, most psychologists and economists believe that if we have an accurate theory of how individuals make choices and act on them, we can explain pretty much everything about social life. Social Emergence takes a new approach to these longstanding questions. Sawyer argues that societies are complex dynamical systems, and that the best way to resolve these debates is by developing the concept of emergence, focusing on multiple levels of analysis - individuals, interactions, and groups - and with a dynamic focus on how social group phenomena emerge from communication processes among individual members. This book makes a unique contribution not only to complex systems research but also to social theory.



Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

Introduction to Applied Bayesian Statistics and Estimation for Social Scientists Author Scott M. Lynch
ISBN-10 9780387712659
Release 2007-06-30
Pages 359
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This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.



Spatial Simulation

Spatial Simulation Author David O'Sullivan
ISBN-10 9781118527078
Release 2013-08-05
Pages 336
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A ground-up approach to explaining dynamic spatial modelling for an interdisciplinary audience. Across broad areas of the environmental and social sciences, simulation models are an important way to study systems inaccessible to scientific experimental and observational methods, and also an essential complement to those more conventional approaches. The contemporary research literature is teeming with abstract simulation models whose presentation is mathematically demanding and requires a high level of knowledge of quantitative and computational methods and approaches. Furthermore, simulation models designed to represent specific systems and phenomena are often complicated, and, as a result, difficult to reconstruct from their descriptions in the literature. This book aims to provide a practical and accessible account of dynamic spatial modelling, while also equipping readers with a sound conceptual foundation in the subject, and a useful introduction to the wide-ranging literature. Spatial Simulation: Exploring Pattern and Process is organised around the idea that a small number of spatial processes underlie the wide variety of dynamic spatial models. Its central focus on three ‘building-blocks’ of dynamic spatial models – forces of attraction and segregation, individual mobile entities, and processes of spread – guides the reader to an understanding of the basis of many of the complicated models found in the research literature. The three building block models are presented in their simplest form and are progressively elaborated and related to real world process that can be represented using them. Introductory chapters cover essential background topics, particularly the relationships between pattern, process and spatiotemporal scale. Additional chapters consider how time and space can be represented in more complicated models, and methods for the analysis and evaluation of models. Finally, the three building block models are woven together in a more elaborate example to show how a complicated model can be assembled from relatively simple components. To aid understanding, more than 50 specific models described in the book are available online at patternandprocess.org for exploration in the freely available Netlogo platform. This book encourages readers to develop intuition for the abstract types of model that are likely to be appropriate for application in any specific context. Spatial Simulation: Exploring Pattern and Process will be of interest to undergraduate and graduate students taking courses in environmental, social, ecological and geographical disciplines. Researchers and professionals who require a non-specialist introduction will also find this book an invaluable guide to dynamic spatial simulation.



Agent Based Computational Sociology

Agent Based Computational Sociology Author Flaminio Squazzoni
ISBN-10 9781119941637
Release 2012-02-27
Pages 248
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Most of the intriguing social phenomena of our time, such as international terrorism, social inequality, and urban ethnic segregation, are consequences of complex forms of agent interaction that are difficult to observe methodically and experimentally. This book looks at a new research stream that makes use of advanced computer simulation modelling techniques to spotlight agent interaction that allows us to explain the emergence of social patterns. It presents a method to pursue analytical sociology investigations that look at relevant social mechanisms in various empirical situations, such as markets, urban cities, and organisations. This book: Provides a comprehensive introduction to epistemological, theoretical and methodological features of agent-based modelling in sociology through various discussions and examples. Presents the pros and cons of using agent-based models in sociology. Explores agent-based models in combining quantitative and qualitative aspects, and micro- and macro levels of analysis. Looks at how to pose an agent-based research question, identifying the model building blocks, and how to validate simulation results. Features examples of agent-based models that look at crucial sociology issues. Supported by an accompanying website featuring data sets and code for the models included in the book. Agent-Based Computational Sociology is written in a common sociological language and features examples of models that look at all the traditional explanatory challenges of sociology. Researchers and graduate students involved in the field of agent-based modelling and computer simulation in areas such as social sciences, cognitive sciences and computer sciences will benefit from this book.



What the Future Holds

What the Future Holds Author Richard N. Cooper
ISBN-10 0262532042
Release 2003
Pages 285
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Social scientists from various disciplines discuss and offer predictions about the future.



Principles of Modeling and Simulation

Principles of Modeling and Simulation Author John A. Sokolowski
ISBN-10 9781118210949
Release 2011-09-20
Pages 280
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Explores wide-ranging applications of modeling and simulation techniques that allow readers to conduct research and ask "What if??" Principles of Modeling and Simulation: A Multidisciplinary Approach is the first book to provide an introduction to modeling and simulation techniques across diverse areas of study. Numerous researchers from the fields of social science, engineering, computer science, and business have collaborated on this work to explore the multifaceted uses of computational modeling while illustrating their applications in common spreadsheets. The book is organized into three succinct parts: Principles of Modeling and Simulation provides a brief history of modeling and simulation, outlines its many functions, and explores the advantages and disadvantages of using models in problem solving. Two major reasons to employ modeling and simulation are illustrated through the study of a specific problem in conjunction with the use of related applications, thus gaining insight into complex concepts. Theoretical Underpinnings examines various modeling techniques and introduces readers to two significant simulation concepts: discrete event simulation and simulation of continuous systems. This section details the two primary methods in which humans interface with simulations, and it also distinguishes the meaning, importance, and significance of verification and validation. Practical Domains delves into specific topics related to transportation, business, medicine, social science, and enterprise decision support. The challenges of modeling and simulation are discussed, along with advanced applied principles of modeling and simulation such as representation techniques, integration into the application infrastructure, and emerging technologies. With its accessible style and wealth of real-world examples, Principles of Modeling and Simulation: A Multidisciplinary Approach is a valuable book for modeling and simulation courses at the upper-undergraduate and graduate levels. It is also an indispensable reference for researchers and practitioners working in statistics, mathematics, engineering, computer science, economics, and the social sciences who would like to further develop their understanding and knowledge of the field.