Search results for ""Author Nigel Gilbert""

Agent-based simulation has become increasingly popular as a modeling approach in the social sciences because it enables researchers to build models where individual entities and their interactions are directly represented. The Second Edition of Nigel Gilbert′s Agent-Based Models introduces this technique; considers a range of methodological and theoretical issues; shows how to design an agent-based model, with a simple example; offers some practical advice about developing, verifying and validating agent-based models; and finally discusses how to plan an agent-based modelling project, publish the results and apply agent-based modeling to formulate and evaluate social and economic policies.

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This book brings together computer models and simulation approaches that allow the investigation of a wide range of innovation related issues, and hence will be of interest for academics and researchers from a variety of innovation related disciplines.'- Mercedes Bleda, Journal of Artificial Societies and Social SimulationChristopher Watts and Nigel Gilbert explore the generation, diffusion and impact of innovations, which can now be studied using computer simulations.Agent-based simulation models can be used to explain the innovation that emerges from interactions among complex, adaptive, diverse networks of firms, people, technologies, practices and resources. This book provides a critical review of recent advances in agent-based modeling and other forms of the simulation of innovation. Elements explored include: diffusion of innovations, social networks, organizational learning, science models, adopting and adapting, and technological evolution and innovation networks. Many of the models featured in the book can be downloaded from the book's accompanying website.Bringing together simulation models from several innovation-related fields, this book will prove a fascinating read for academics and researchers in a wide range of disciplines, including: innovation studies, evolutionary economics, complexity science, organization studies, social networks, and science and technology studies. Scholars and researchers in the areas of computer science, operational research and management science will also be interested in the uses of simulation models to improve the understanding of organization.

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What can computer simulation contribute to the social sciences? Which of the many approaches to simulation would be best for my social science project? How do I design, carry out and analyse the results from a computer simulation? Interest in social simulation has been growing rapidly worldwide as a result of increasingly powerful hardware and software and also a rising interest in the application of ideas of complexity, evolution, adaptation and chaos in the social sciences. Simulation for the Social Scientist is a practical textbook on the techniques of building computer simulations to assist understanding of social and economic issues and problems. This authoritative book details all the common approaches to social simulation, to provide social scientists with an appreciation of the literature and allow those with some programming skills to create their own simulations. New for this edition: A new chapter on designing multi-agent systems, to support the fact that multi-agent modelling has become the most common approach to simulation New examples and guides to current software Updated throughout to take new approaches into account The book is an essential tool for social scientists in a wide range of fields, particularly sociology, economics, anthropology, geography, organizational theory, political science, social policy, cognitive psychology and cognitive science. It will also appeal to computer scientists interested in distributed artificial intelligence, multi-agent systems and agent technologies.

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`This book is highly recommended for libraries and departments to adopt. If I had to teach a statistics class for sociology students this would be a book I would surely choose. The book achieves two very important goals: it teaches students a software package and trains them in the statistical analysis of sociological data′ - Journal of Applied Statistics This fully revised, expanded and updated Second Edition of the best-selling textbook by Jane Fielding and Nigel Gilbert provides a comprehensive yet accessible guide to quantitative data analysis. Designed to help take the fear out of the use of numbers in social research, this textbook introduces students to statistics as a powerful means of revealing patterns in human behaviour. The textbook covers everything typically included in an introductory course on social statistics for students in the social sciences and the authors have taken the opportunity of this Second Edition to bring the data sources as current as possible. The book is full of up-to-date examples and useful and clear illustrations using the latest SPSS software. While maintaining the student-friendly elements of the first, such as chapter summaries, exercises at the end of each chapter, and a glossary of key terms, new features to this edition include: - Updated examples and references SPSS coverage and screen-shots now incorporate the current version 14.0 and are used to demonstrate the latest social statistics datasets; - Additions to content include a brand new section on developing a coding frame and an additional discussion of weighting counts as a means of analyzing published statistics; - Enhanced design aids navigation which is further simplified by the addition of core objectives for each chapter and bullet-pointed chapter summaries; - The updated Website at http:/www.soc.surrey.ac.uk/uss/index.html reflects changes made to the text and provides updated datasets; A valuable and practical guide for students dealing with the large amounts of data that are typically collected in social surveys, the Second Edition of Understanding Social Statistics is an essential textbook for courses on statistics and quantitative research across the social sciences.

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Agent-based modelling in economics Lynne Hamill and Nigel Gilbert, Centre for Research in Social Simulation (CRESS), University of Surrey, UK New methods of economic modelling have been sought as a result of the global economic downturn in 2008.This unique book highlights the benefits of an agent-based modelling (ABM) approach. It demonstrates how ABM can easily handle complexity: heterogeneous people, households and firms interacting dynamically. Unlike traditional methods, ABM does not require people or firms to optimise or economic systems to reach equilibrium. ABM offers a way to link micro foundations directly to the macro situation. Key features: Introduces the concept of agent-based modelling and shows how it differs from existing approaches. Provides a theoretical and methodological rationale for using ABM in economics, along with practical advice on how to design and create the models. Each chapter starts with a short summary of the relevant economic theory and then shows how to apply ABM. Explores both topics covered in basic economics textbooks and current important policy themes; unemployment, exchange rates, banking and environmental issues. Describes the models in pseudocode, enabling the reader to develop programs in their chosen language. Supported by a website featuring the NetLogo models described in the book. Agent-based Modelling in Economics provides students and researchers with the skills to design, implement, and analyze agent-based models. Third year undergraduate, master and doctoral students, faculty and professional economists will find this book an invaluable resource.

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This book brings together computer models and simulation approaches that allow the investigation of a wide range of innovation related issues, and hence will be of interest for academics and researchers from a variety of innovation related disciplines.'- Mercedes Bleda, Journal of Artificial Societies and Social SimulationChristopher Watts and Nigel Gilbert explore the generation, diffusion and impact of innovations, which can now be studied using computer simulations.Agent-based simulation models can be used to explain the innovation that emerges from interactions among complex, adaptive, diverse networks of firms, people, technologies, practices and resources. This book provides a critical review of recent advances in agent-based modeling and other forms of the simulation of innovation. Elements explored include: diffusion of innovations, social networks, organizational learning, science models, adopting and adapting, and technological evolution and innovation networks. Many of the models featured in the book can be downloaded from the book's accompanying website.Bringing together simulation models from several innovation-related fields, this book will prove a fascinating read for academics and researchers in a wide range of disciplines, including: innovation studies, evolutionary economics, complexity science, organization studies, social networks, and science and technology studies. Scholars and researchers in the areas of computer science, operational research and management science will also be interested in the uses of simulation models to improve the understanding of organization.

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