Simulation for the Social ScientistGilbert (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 |
Contents
Multilevel simulation models | 6 |
Simulation as a method | 14 |
System dynamics and world models | 38 |
Copyright | |
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activations actors agents ALLBUS applied approach array artificial intelligence artificial neural networks artificial societies attributes backpropagation basic-agent behaviour birth calculated cellular automata cent Chapter check-in counter chromosome complex defMeth defun depends described develop differential equations discrete event simulation distributed artificial intelligence distribution dotimes doves environment error evaluated example Figure genes genetic algorithm grid grid-size hawks hawks and doves household income individual initial interactions language large number law-abiders Lisp macro means microanalytical simulation models microdata microdata file microsimulation MIMOSE MSMs multi-agent models multilevel modelling neighbours neural networks object old-grid parameters passengers pollution population predict probability proportion queue Queuing models random number retrodiction rule sample setf aref setq slot-value social sciences social simulation stationary statistical step strategy sugar Swarm system dynamics table function target system theory tion units values variables weights world model WORLD3