Tools and Techniques for Social Science Simulation
Ramzi Suleiman, Klaus G. Troitzsch, Nigel Gilbert
Physica-Verlag HD, Feb 11, 2000 - Social Science - 387 pages
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.
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Towards a Simulation Toolkit
Simulating the Role of Nonliniarity and Discreteness
Questions in the Methodology of Artificial Societies
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action adaptive agents algorithms application artificial societies assumptions Bedrock behaviour Berlin Castelfranchi cells cellular automata cluster Cognition complex component computer simulation continuing diversity correspondence-function decision defined described discrete distribution Doran environment equations evolution evolutionary game theory example Figure genetic algorithms Gilbert goals grid Hegselmann hybrid hypothesis implemented individual initial input interaction iteration Latane learning rules linear mathematical matrix means meta parameters methods Microsimulation migration multi-agent systems multilevel Modelling neediness classes neighbourhood neighbours neural networks Nowak opinion dynamics optimisation optimization output partnerships patterns payoff PECS Physis players population possible problem programming proposition H protocol rational actors reference model regression represented risk dominance sensitivity analysis Simulating Societies simulation models simulation run SMASS social sciences social simulation social systems specific Springer SPSS statistical strategy structure support game tion toolkit Troitzsch ubiquitous agreement units updating values variables