Political Complexity: Nonlinear Models of PoliticsDiana Richards Doyle This collection illustrates how nonlinear methods can provide new insight into existing political questions. Politics is often characterized by unexpected consequences, sensitivity to small changes, non-equilibrium dynamics, the emergence of patterns, and sudden changes in outcomes. These are all attributes of nonlinear processes. Bringing together a variety of recent nonlinear modeling approaches, Political Complexity explores what happens when political actors operate in a dynamic and complex social environment. The contributions to this collection are organized in terms of three branches within non-linear theory: spatial nonlinearity, temporal nonlinearity, and functional nonlinearity. The chapters advance beyond analogy towards developing rigorous nonlinear models capable of empirical verification. Contributions to this volume cover the areas of landscape theory, computational modeling, time series analysis, cross-sectional analysis, dynamic game theory, duration models, neural networks, and hidden Markov models. They address such questions as: Is international cooperation necessary for effective economic sanctions? Is it possible to predict alliance configurations in the international system? Is a bureaucratic agency harder to remove as time goes on? Is it possible to predict which international crises will result in war and which will avoid conflict? Is decentralization in a federal system always beneficial? The contributors are David Bearce, Scott Bennett, Chris Brooks, Daniel Carpenter, Melvin Hinich, Ken Kollman, Susanne Lohmann, Walter Mebane, John Miller, Robert E. Molyneaux, Scott Page, Philip Schrodt, and Langche Zeng. This book will be of interest to a broad group of political scientists, ranging from those who employ nonlinear methods to those curious to see what it is about. Scholars in other social science disciplines will find the new methodologies insightful for their own substantive work. Diana Richards is Associate Professor of Political Science, University of Minnesota. |
Contents
Contents | 1 |
Consequences of Nonlinear Preferences | 23 |
Landscapes as Analogues of Political Phenomena | 46 |
Copyright | |
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abstains in period act in period actors agency aggregate algorithm analysis applies approach assumption Baum-Welch algorithm BCOW behavior bifurcation central challenger choice coalition complex computational configuration cooperation data set defect dependent variable distribution duration dynamics economic sanctions effect election empirical environmental equilibrium essay estimated expected utility forecasting global global optimum hazard hidden Markov models Hinich hypotheses Imax incentive increases incumbent inference Informational Cascades input issue landscape theory legislature linear model linear regression logit m₁ mixed strategy n₁ n₂ Nash equilibria neural network model nonlinear modeling nonwar optimal optimum outcomes output overturn the status PACs patterns payoff players Political Science predictions preferences probability problem propensity regression relationships sanction success Schrodt sequences signal social softmax stable statistical status quo success score termination threshold tion towns U.S. dollar utility of abstaining utility of acting values vector voters voting