A Behavioral Theory of Elections (ebook)

Argumento de A Behavioral Theory of Elections (ebook)

"It . . . need[s] to be confronted and digested by every graduate student who hopes to make his or her scholarly name in this field, because it provides a way to unify the higgledy-piggledy world of political behavior. It isn't the final word, but it is an important early step."--Kenneth A. Shepsle, Perspectives on Politics

"[T]his book offers plenty food for thought for both theoretical and empirical minded scholars and is a must read for anyone interested in understanding the dynamics of electoral competition."--Jasper Muis, Journal of Artificial Societies and Social Simulation

"Traditional approaches in political science and economics have failed to explain why people vote or take other actions that apparently have no basis in self-interest. In this pathbreaking book, the authors provide the analytical foundations for a new behavioral theory of political participation."--Stephen Ansolabehere, Harvard University

"The authors apply the insights of psychology and bounded rationality to construct a new foundation for our understanding of how voters and politicians behave in complex strategic environments. Bold and highly original, this fascinating book is essential reading for anyone with a serious interest in elections and will fundamentally reshape how we think about political behavior."--Alan Gerber, Yale University

"Given the complexity of social processes, many social scientists question the assumption of rationality underlying game theoretic models of elections. The natural solution, to assume bounded rationality, has been stifled by the abundance of possible alternative models. This wonderful book examines a class of models grounded in aspiration-based learning and shows how they produce deep, explanatory insights into voter choice, turnout, party competition, and electoral outcomes. A tour de force!"--Scott E. Page, author of The Difference and Diversity and Complexity

"Distinctive in its use of aspiration-based adjustment models as a replacement for the traditional rational choice approaches, this book is clearly the first to develop a coherent model of elections based on reinforcement learning. Clearly written and effectively presented, it will advance the debate on the use of both behavioral and rational choice models in political science."--Mark Fey, University of Rochester0Acknowledgments xi

Chapter One: Bounded Rationality and Elections 1
1.1 Framing and Representations 5
1.2 Heuristics 8
1.3 Aspiration-based Adaptation and Bounded Rationality 12
1.4 Plan of This Book 21

Chapter Two: Aspiration-based Adaptive Rules 23
2.1 ABARs Defined 23
2.2 Some Important Properties of ABARs 33
2.3 The Evidential Status of Aspiration-based Adaptation 46

Chapter Three: Party Competition 52
3.1 Related Work 54
3.2 The Model and Its Implications 56
3.3 Informed and/or Sophisticated Challengers 68
3.4 Robustness Issues 74
3.5 Conclusions 78

Chapter Four: Turnout 80
4.1 The Model 82
4.2 Main Results 85
4.3 Variations in Participation 96
4.4 Conclusions 107

Chapter Five: Voter Choice 109
5.1 The Model 112
5.2 The Endogenous Emergence of Party Affiliation 116
5.3 Misperceptions 121
5.4 Retrospection and Prospection Combined 122
5.5 Voter Sophistication and Electoral Outcomes 124
5.6 Institutions and Unsophisticated Retrospective Voters 128
5.7 Conclusions 130

Chapter Six: An Integrated Model of Two-Party Elections 132
6.1 Full Computational Model for Two Parties 134
6.2 Some Results of the Basic Integrated Model 138
6.3 The Choices of Voters 141
6.4 Party Location 145
6.5 Turnout 148
6.6 New Questions 152
6.7 Conclusion 159

Chapter Seven: Elections with Multiple Parties 161
7.1 Extending Our Results to Multiple Parties 161
7.2 Multicandidate Competition and Duverger's Law 166
7.3 The Model and Simulation Results 173
7.4 An Intuition 180
7.5 ABARs and Dynamic Stability 183
7.6 Model Meets Data 184

Chapter Eight: Conclusions: Bounded Rationality and Elections 191
8.1 Testing the Theory 194
8.2 Normative Considerations: Voter Error and Systemic Performance 196
8.3 Extensions 198

Appendix A: Proofs 205
Appendix B: The Computational Model 215
B.1 Overview 215
B.2 Graphical Model 216
B.3 Batch Model 229
Bibliography 233
Index 249