Computational and Mathematical Modeling in the Social Sciences
Mathematical models in the social sciences have become increasingly sophisticated and widespread in the last decade. This period has also seen many critiques, most lamenting the sacrifices incurred in pursuit of mathematical rigor. If, as critics argue, our ability to understand the world has not improved during the mathematization of the social sciences, we might want to adopt a different paradigm. This book examines the three main fields of mathematical modeling - game theory, statistics, and computational methods - and proposes a new framework for modeling. Unlike previous treatments which view each field separately, the treatment provides a framework that spans and incorporates the different methodological approaches. The goal is to arrive at a new vision of modeling that allows researchers to solve more complex problems in the social sciences. Additionally, a special emphasis is placed upon the role of computational modeling in the social sciences.
- Most succinct, up to date presentation of computational modeling for broad social science audience
- Compares and contrasts computational models with those of game theory and statistics
- Designed as coursebook with accessible narrative; surprisingly little math in actual text
Reviews & endorsements
' … we have in this slim volume, full of good ideas, deep insights and practical advice for how to do sound methodological work and how not to do it. Given these strengths, this book should earn a cultish following among graduate students who possess a methodological bent.' Public Choice
Product details
November 2005Paperback
9780521619134
220 pages
228 × 153 × 13 mm
0.308kg
20 b/w illus. 7 tables
Available
Table of Contents
- 1. Not all fun and games: challenges in mathematical modeling
- 2. Looking for car keys without any street lights
- 3. From curses to complexity: the justification for computational modeling
- 4. Why everything should look like a nail: deriving parsimonious encodings for complex games
- 5. KKV redux: deriving and testing logical implications.