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 perfection. 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.
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agents alliance game allow approach argue argument candidate Chapter choice choose Combinatorial game theory complex component games component utility function computational model conflict currency game curse of dimensionality data set deductive models dependent derive difficult dyad electorate empirical models empirical referent encoding equivalence classes evaluation example existing Fearon's feature space FEDERAL SPENDING Figure Friedman game theoretic models game theory genetic algorithm given goal graph Hinich ideal point idiosyncratic utility functions independent variables issue space landscape large number linear logical implications machine chess methodology methods Nash equilibrium neighborhood neural networks nonseparable preferences normal form game noted observations optimization out-of-sample testing overfitting parameter space parameter values payoffs Perl player political science population POSITION 7PT possible predictive presented problem produce qualitative question regime shifts regression ROC curves sample social sciences statistical model strategies survey data territory tions underfitting units utility functions variance voters