By Charles S. Taber, Richard J. Timpone
Computational modelling permits researchers to mix the wealthy aspect of qualitative study with the rigour of quantitative and formal examine, in addition to to symbolize advanced constructions and tactics inside a theoretical version. After an advent to modelling, the authors talk about the function of computational tools within the social sciences. They deal with computational equipment, together with dynamic simulation, knowledge-based types and desktop studying, as a unmarried large classification of study instruments and enhance a framework for incorporating them inside of demonstrated traditions of social technological know-how learn. they supply a concise description of every process and various social technological know-how illustrations, together with 4 distinct examples.
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Extra info for Computational modeling, Issue 113
Sometimes, as with most of the examples in this book, the observation is more complicated, perhaps taking the form of dissatisfaction with existing theoretical explanations of some complex phenomenon. The issue then becomes how we can fashion existing theoretical ideas and novel hypotheses into a more satistying explanation. Obviously, theory development cannot occur in a vacuum. One needs a solid grasp of existing substantive knowledge and theory about the research domain. Too often, researchers become committed to particular analytic tools, such as computational modeling, and lose sight of the substantive questions.
OR (2) Taber, C. , & Timpone, R. J. (1996) Computational Modeling (Sage University Paper series on Quantitative Applications in the Social Sciences, series no. 07-113). Thousand Oaks, CA: Sage. Page iii Contents Acknowledgments v Series Editor's Introduction vii 1. Introduction 1 Beyond Platforms and On-Ramps 1 Models and Computational Models 2 Why Model Computationally? 3 Where Is Computational Modeling Likely to Be Most Useful? 8 Drawbacks of Computational Modeling 11 General Stages in a Computational Modeling Project 13 An Overview of the Following Chapters 17 2.
Ideally, each cycle through these analytic stages will provide additional understanding, as our theories become richer and our models more precise. , Abelson & Carroll, 1965; McPhee, 1963). Over the past two decades, at an accelerating pace, the tools of computational modeling have become more sophisticated and diverse. Chapters 2 through 4 provide an overview of current methods, from traditional computer simulation through the more recent developments in cognitive science and machine learning.