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Political Complexity: Nonlinear Models of Politics
Diana Richards, EditorThis 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.
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.
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Cover
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Title
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Copyright
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Dedication
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Contents
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Nonlinear Modeling: All Things Suffer Change
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Part 1. Spatial Nonlinearity: Optimization and Search
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Consequences of Nonlinear Preferences in a Federal System
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Landscapes as Analogues of Political Phenomena
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Part 2. Temporal Nonlinearity: Complexity over Time
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Episodic Nonlinear Event Detection: Political Epochs in Exchange Rates
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Congressional Campaign Contributions, District Service, and Electoral Outcomes in the United States: Statistical Tests of a Formal Game Model with Nonlinear Dynamics
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I Know You Know He or She Knows We Know You Know They Know: Common Knowledge and the Unpredictability of Informational Cascades
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Nonlinear Dynamics in Games: Convergence and Stability in International Environmental Agreements
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Part 3. Functional Nonlinearity: Networks and Patterns
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Stochastic Prediction and Estimation of Nonlinear Political Durations: An Application to the Lifetime of Bureaus
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Neural Network Models for Political Analysis
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Economic Sanctions and Neural Networks: Forecasting Effectiveness and Reconsidering Cooperation
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Pattern Recognition of International Crises Using Hidden Markov Models
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Part 4. Conclusion
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Optimizing, Strategizing, and Recognizing: Learning in a Dynamic Social Environment
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Contributors
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Index
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Citable Link
Published: 2000
Publisher: University of Michigan Press
- 978-0-472-10964-7 (hardcover)
- 978-0-472-02699-9 (ebook)