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Informationen zum Autor Nigel Gilbert. University of Surrey Klappentext 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 simulationNew examples and guides to current softwareUpdated throughout to take new approaches into accountThe 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. Zusammenfassung Details 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. This book presents the techniques of building computer simulations to assist understanding of social and economic issues and problems. Inhaltsverzeichnis 1: Simulation and social science 2: Simulation as a method 3: Systems dynamics and world models 4: Microanalytical simulation models 5: Queuing models 6: Multilevel simulation models 7: Cellular automata 8: Multi-agent models 9: Developing multi-agent systems 10: Learning and evolutionary models Appendix A (websites) Appendix B (Linear stability analysis of the hawk-dove-lawabider model) Appendix C (Random number generators)...
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Nigel Gilbert. University of Surrey
