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Other titles in the Quantitative Applications in the Social Sciences series:
Computational Modeling (96 Edition)by Charles S. Taber
Synopses & ReviewsPlease note that used books may not include additional media (study guides, CDs, DVDs, solutions manuals, etc.) as described in the publisher comments.
Computational modelling allows researchers to combine the rich detail of qualitative research with the rigour of quantitative and formal research, as well as to represent complex structures and processes within a theoretical model.
After an introduction to modelling, the authors discuss the role of computational methods in the social sciences. They treat computational methods, including dynamic simulation, knowledge-based models and machine learning, as a single broad class of research tools and develop a framework for incorporating them within established traditions of social science research. They provide a concise description of each method and a variety of social science illustrations, including four detailed examples.
Table of Contents
Introduction: Beyond platforms and on-ramps. Models and computational models. Why model computationally? Where is computational modeling likely to be most useful? Drawbacks of computational modeling. General stages in a computational modeling project. An overview of the following chapters — Dynamic simulation models: Dynamic simulation. Cellular automata — Knowledge-based systems: Semantic networks. Frame systems. Rule-based expert systems. Hybrid systems. — Models of machine learning: Connectionist models. ID3. Genetic algorithms — Evaluating computational models: Truth. Beauty. Justice. Conclusion.
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