Quantal Response Equilibrium presents a stochastic theory of games that unites probabilistic choice models developed in psychology and statistics with the Nash equilibrium approach of classical game theory. Nash equilibrium assumes precise and perfect decision making in games, but human behavior is inherently stochastic and people realize that the behavior of others is not perfectly predictable. In contrast, QRE models choice behavior as probabilistic and extends classical game theory into a more realistic and useful framework with broad applications for economics, political science, management, and other social sciences. Quantal Response Equilibrium spans the range from basic theoretical foundations to examples of how the principles yield useful predictions and insights in strategic settings, including voting, bargaining, auctions, public goods provision, and more. The approach provides a natural framework for estimating the effects of behavioral factors like altruism, reciprocity, risk aversion, judgment fallacies, and impatience. New theoretical results push the frontiers of models that include heterogeneity, learning, and well-specified behavioral modifications of rational choice and rational expectations. The empirical relevance of the theory is enhanced by discussion of data from controlled laboratory experiments, along with a detailed users' guide for estimation techniques. Quantal Response Equilibrium makes pioneering game-theoretic methods and interdisciplinary applications available to a wide audience.

How do interacting decision-makers make strategic choices? If they’re rational and can somehow predict each other’s behavior, they may find themselves in a Nash equilibrium. However, humans display pervasive and systematic departures from rationality. They often do not conform to the predictions of the Nash equilibrium, or its various refinements. This has led to the growth of behavioral game theory, which accounts for how people actually make strategic decisions by incorporating social preferences, bounded rationality (for example, limited iterated reasoning), and learning from experience. This book brings together new advances in the field of behavioral game theory that help us understand how people actually make strategic decisions in game-theoretic situations.

The aim of this Handbook is twofold: to educate and to inspire. It is meant for researchers and graduate students who are interested in taking a data-based and behavioral approach to the study of game theory. Educators and students of economics will find the Handbook useful as a companion book to conventional upper-level game theory textbooks, enabling them to compare and contrast actual behavior with theoretical predictions. Researchers and non-specialists will find valuable examples of laboratory and field experiments that test game theoretic propositions and suggest new ways of modeling strategic behavior. Chapters are organized into several sections; each section concludes with an inspirational chapter, offering suggestions on new directions and cutting-edge topics of research in experimental game theory.

In this book, David K. Levine questions the idea that behavioral economics is the answer to economic problems. He explores the successes and failures of contemporary economics both inside and outside the laboratory, and asks whether popular behavioral theories of psychological biases are solutions to the failures. The book not only provides an overview of popular behavioral theories and their history, but also gives the reader the tools for scrutinizing them.

Students of comparative politics have long faced a vexing dilemma: how can social scientists draw broad, applicable principles of political order from specific historical examples? In Analytic Narratives, five senior scholars offer a new and ambitious methodological response to this important question. By employing rational-choice and game theory, the authors propose a way of extracting empirically testable, general hypotheses from particular cases. The result is both a methodological manifesto and an applied handbook that political scientists, economic historians, sociologists, and students of political economy will find essential. In their jointly written introduction, the authors frame their approach to the origins and evolution of political institutions. The individual essays that follow demonstrate the concept of the analytic narrative--a rational-choice approach to explain political outcomes--in case studies. Avner Greif traces the institutional foundations of commercial expansion in twelfth-century Genoa. Jean-Laurent Rosenthal analyzes how divergent fiscal policies affected absolutist European governments, while Margaret Levi examines the transformation of nineteenth-century conscription laws in France, the United States, and Prussia. Robert Bates explores the emergence of a regulatory organization in the international coffee market. Finally, Barry Weingast studies the institutional foundations of democracy in the antebellum United States and its breakdown in the Civil War. In the process, these studies highlight the economic role of political organizations, the rise and deterioration of political communities, and the role of coercion, especially warfare, in political life. The results are both empirically relevant and theoretically sophisticated. Analytic Narratives is an innovative and provocative work that bridges the gap between the game-theoretic and empirically driven approaches in political economy. Political historians will find the use of rational-choice models novel; theorists will discover arguments more robust and nuanced than those derived from abstract models. The book improves on earlier studies by advocating--and applying--a cross-disciplinary approach to explain strategic decision making in history.

This book provides a unique historical perspective on expectations in economic theory, and applications of expectations models in economic history. Based on papers presented at the 2017 Thomas Guggenheim Conference, it brings together the work of economists, historians of economics, and economic historians on issues and events concerning expectations in economics and economic history. The contributions address: (i) the history of expectations models; (ii) growth, expectations and political economy; (iii) controversies regarding expectations methods and models; (iv) expectations in theory and reality; and (v) expectations in economic history. The book opens with a lecture by Thomas Guggenheim Prize winner Duncan Foley on the evolution of expectations in modern economic thought. The remaining content is divided into two parts, the first of which focuses on the utilization of expectations in the “ancient” and “meso” periods of high theory, i.e., from Smithian to Keynesian approaches. The papers cover topics such as “modern” applications of expectations in both “Tobinesque-Phillips” and “Harrodian-Solowian” contexts, and the debate between Friedmanite and Keynesian approaches to expectation formation. In turn, the last part presents essays on the role of economic expectations in connection with historical events and contexts, ranging from the early 20th century to World War II, and on the application of expectations theory to hyperinflation and stabilization, taking Israel as a case study.

GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. Readers will also enjoy: A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deception An exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threats Practical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systems In-depth examinations of generative models for cyber security Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security.