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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.
Traditional game theory predicts behavior contrary to how real people actually behave. And what traditional game theory prescribes as the rational thing to do is normally unattainable in real-life. The problem is that game theorists have traditionally assumed that agents have no cognitive limitations and know all logical and mathematical truths. Hence, traditional game theory does not account for people's cognitive limitations- their bounded rationality. I remove the strong assumptions about rational agents and adjust the principles of rationality for real people. I focus on the Centipede Game, a sequential game, with multiple stages, where ideal agents predict moves at the last stage, and then use these predictions to predict moves at preceding stages, settling on a strategy for moves throughout the interaction - a procedure called backward induction. Applying backward induction makes heavy demands on agents' cognitive capacities and is unrealistic reasoning for them. Thus, I develop an account of bounded rationality that applies a simpler procedure for agents to begin their interaction, by exploring and testing others' behavior until they reach a moment in the sequential game when they are able to apply limited backward induction. This analysis of behavior better predicts how real people actually behave, and prescribes a course of action attainable in real-life.
Discover the very latest game-theoretic approaches for designing, modeling, and optimizing emerging wireless communication networks and systems with this unique text. Providing a unified and comprehensive treatment throughout, it explains basic concepts and theories for designing novel distributed wireless networking mechanisms, describes emerging game-theoretic tools from an engineering perspective, and provides an extensive overview of recent applications. A wealth of new tools is covered - including matching theory and games with bounded rationality - and tutorial chapters show how to use these tools to solve current and future wireless networking problems in areas such as 5G networks, network virtualization, software defined networks, cloud computing, the Internet of Things, context-aware networks, green communications, and security. This is an ideal resource for telecommunications engineers, and researchers in industry and academia who are working on the design of efficient, scalable, and robust communication protocols for future wireless networks, as well as graduate students in these fields.
The book reports on recent experimental research on expectations and decision making in bargaining, markets, auctions, or coalition formation situations. The investi- gated topics deliver building stones for a bounded rational theory as an approach to explain behavior and interpersonal interactions in economic and social relationships.
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.
Two leaders in the field explore the foundations of bounded rationality and its effects on choices by individuals, firms, and the government. Bounded rationality recognizes that human behavior departs from the perfect rationality assumed by neoclassical economics. In this book, Sanjit Dhami and Cass R. Sunstein explore the foundations of bounded rationality and consider the implications of this approach for public policy and law, in particular for questions about choice, welfare, and freedom. The authors, both recognized as experts in the field, cover a wide range of empirical findings and assess theoretical work that attempts to explain those findings. Their presentation is comprehensive, coherent, and lucid, with even the most technical material explained accessibly. They not only offer observations and commentary on the existing literature but also explore new insights, ideas, and connections. After examining the traditional neoclassical framework, which they refer to as the Bayesian rationality approach (BRA), and its empirical issues, Dhami and Sunstein offer a detailed account of bounded rationality and how it can be incorporated into the social and behavioral sciences. They also discuss a set of models of heuristics-based choice and the philosophical foundations of behavioral economics. Finally, they examine libertarian paternalism and its strategies of “nudges.”