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Social sciences -- Simulation methods. Social interaction -- Computer simulation. Social sciences -- Mathematical models. (publisher)
Gilbert (sociology, U. of Surrey) and Troitzsch (social science informatics, U. of Koblenz-Landau, Germany) offer a practical textbook on techniques for building simulations to assist the understanding of social and economics issues. They explain what computer simulation can contribute to the social sciences, which of the many approaches to simulation would be best for a particular research project, and how to design and carry out a simulation and analyze the results. Computer scientists might also benefit from reading what functions social scientists need and what problems they have with existing packages. US distribution by Taylor and Francis. Annotation copyrighted by Book News, Inc., Portland, OR
Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, this book 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.
Model building in the social sciences can increasingly rely on well elaborated formal theories. At the same time inexpensive large computational capacities are now available. Both make computer-based model building and simulation possible in social science, whose central aim is in particular an understanding of social dynamics. Such social dynamics refer to public opinion formation, partner choice, strategy decisions in social dilemma situations and much more. In the context of such modelling approaches, novel problems in philosophy of science arise which must be analysed - the main aim of this book. Interest in social simulation has recently been growing rapidly world- wide, mainly as a result of the increasing availability of powerful personal computers. The field has also been greatly influenced by developments in cellular automata theory (from mathematics) and in distributed artificial intelligence which provided tools readily applicable to social simulation. This book presents a number of modelling and simulation approaches and their relations to problems in philosophy of science. It addresses sociologists and other social scientists interested in formal modelling, mathematical sociology, and computer simulation as well as computer scientists interested in social science applications, and philosophers of social science.
Computer simulation was first pioneered as a scientific tool in meteorology and nuclear physics in the period following World War II, but it has grown rapidly to become indispensible in a wide variety of scientific disciplines, including astrophysics, high-energy physics, climate science, engineering, ecology, and economics. Digital computer simulation helps study phenomena of great complexity, but how much do we know about the limits and possibilities of this new scientific practice? How do simulations compare to traditional experiments? And are they reliable? Eric Winsberg seeks to answer these questions in Science in the Age of Computer Simulation. Scrutinizing these issue with a philosophical lens, Winsberg explores the impact of simulation on such issues as the nature of scientific evidence; the role of values in science; the nature and role of fictions in science; and the relationship between simulation and experiment, theories and data, and theories at different levels of description. Science in the Age of Computer Simulation will transform many of the core issues in philosophy of science, as well as our basic understanding of the role of the digital computer in the sciences.
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.
An exploration of the implications of developments in artificial intelligence for social scientific research, which builds on the theoretical and methodological insights provided by "Simulating societies".; This book is intended for worldwide library market for social science subjects such as sociology, political science, geography, archaeology/anthropology, and significant appeal within computer science, particularly artificial intelligence. Also personal reference for researchers.
Aimed at researchers across the social sciences, this book explains the logic behind the Monte Carlo simulation method and demonstrates its uses for social and behavioural research.