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The notion of bounded rationality was initiated in the 1950s by Herbert Simon; only recently has it influenced mainstream economics. In this book, Ariel Rubinstein defines models of bounded rationality as those in which elements of the process of choice are explicitly embedded. The book focuses on the challenges of modeling bounded rationality, rather than on substantial economic implications. In the first part of the book, the author considers the modeling of choice. After discussing some psychological findings, he proceeds to the modeling of procedural rationality, knowledge, memory, the choice of what to know, and group decisions.In the second part, he discusses the fundamental difficulties of modeling bounded rationality in games. He begins with the modeling of a game with procedural rational players and then surveys repeated games with complexity considerations. He ends with a discussion of computability constraints in games. The final chapter includes a critique by Herbert Simon of the author's methodology and the author's response. The Zeuthen Lecture Book series is sponsored by the Institute of Economics at the University of Copenhagen.
Herbert Simon’s renowned theory of bounded rationality is principally interested in cognitive constraints and environmental factors and influences which prevent people from thinking or behaving according to formal rationality. Simon’s theory has been expanded in numerous directions and taken up by various disciplines with an interest in how humans think and behave. This includes philosophy, psychology, neurocognitive sciences, economics, political science, sociology, management, and organization studies. The Routledge Handbook of Bounded Rationality draws together an international team of leading experts to survey the recent literature and the latest developments in these related fields. The chapters feature entries on key behavioural phenomena, including reasoning, judgement, decision making, uncertainty, risk, heuristics and biases, and fast and frugal heuristics. The text also examines current ideas such as fast and slow thinking, nudge, ecological rationality, evolutionary psychology, embodied cognition, and neurophilosophy. Overall, the volume serves to provide the most complete state-of-the-art collection on bounded rationality available. This book is essential reading for students and scholars of economics, psychology, neurocognitive sciences, political sciences, and philosophy.
In a complex and uncertain world, humans and animals make decisions under the constraints of limited knowledge, resources, and time. Yet models of rational decision making in economics, cognitive science, biology, and other fields largely ignore these real constraints and instead assume agents with perfect information and unlimited time. About forty years ago, Herbert Simon challenged this view with his notion of "bounded rationality." Today, bounded rationality has become a fashionable term used for disparate views of reasoning. This book promotes bounded rationality as the key to understanding how real people make decisions. Using the concept of an "adaptive toolbox," a repertoire of fast and frugal rules for decision making under uncertainty, it attempts to impose more order and coherence on the idea of bounded rationality. The contributors view bounded rationality neither as optimization under constraints nor as the study of people's reasoning fallacies. The strategies in the adaptive toolbox dispense with optimization and, for the most part, with calculations of probabilities and utilities. The book extends the concept of bounded rationality from cognitive tools to emotions; it analyzes social norms, imitation, and other cultural tools as rational strategies; and it shows how smart heuristics can exploit the structure of environments.
In his Mattioli Lectures, Nobel Laureate Professor Herbert A. Simon directs attention to the kinds of empirical research that are necessary for progress in microeconomics. He traces the development of neoclassical economic theory and its gradual retreat from empiricism to abstraction. He then discusses the importance of business firms to the economic system, and the need for a thoroughly empirical understanding of how organisations work and reach their decisions. Finally, he examines innovative approaches to empirical research, including experimental economics, observational methods for studying economic behaviour, and the kinds of simulation models that are needed to interpret decision process. A round-table discussion of these issues follows; the participants, in addition to Professor Simon, are Professors Claudio Dematte, Massimo Egidi, Richard M. Goodwin, Robert Marris, Aldo Montesano and Riccardo Viale.
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.”
Computational Techniques for Modelling Learning in Economics offers a critical overview of the computational techniques that are frequently used for modelling learning in economics. It is a collection of papers, each of which focuses on a different way of modelling learning, including the techniques of evolutionary algorithms, genetic programming, neural networks, classifier systems, local interaction models, least squares learning, Bayesian learning, boundedly rational models and cognitive learning models. Each paper describes the technique it uses, gives an example of its applications, and discusses the advantages and disadvantages of the technique. Hence, the book offers some guidance in the field of modelling learning in computation economics. In addition, the material contains state-of-the-art applications of the learning models in economic contexts such as the learning of preference, the study of bidding behaviour, the development of expectations, the analysis of economic growth, the learning in the repeated prisoner's dilemma, and the changes of cognitive models during economic transition. The work even includes innovative ways of modelling learning that are not common in the literature, for example the study of the decomposition of task or the modelling of cognitive learning.
This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: deep learning; artificial intelligence; applications of game theory; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.
This book brings together the authors' joint papers from over a period of more than twenty years. The collection includes seven papers, each of which presents a novel and rigorous model in Economic Theory. All of the models are within the domain of implementation and mechanism design theories. These theories attempt to explain how incentive schemes and organizations can be designed with the goal of inducing agents to behave according to the designer's (principal's) objectives. Most of the literature assumes that agents are fully rational. In contrast, the authors inject into each model an element which conflicts with the standard notion of full rationality, demonstrating how such elements can dramatically change the mechanism design problem. Although all of the models presented in this volume touch on mechanism design issues, it is the formal modeling of bounded rationality that the authors are most interested in. A model of bounded rationality signifies a model that contains a procedural element of reasoning that is not consistent with full rationality. Rather than looking for a canonical model of bounded rationality, the articles introduce a variety of modeling devices that will capture procedural elements not previously considered, and which alter the analysis of the model. The book is a journey into the modeling of bounded rationality. It is a collection of modeling ideas rather than a general alternative theory of implementation.
. This major new book will be of particular interest not only to philosophers but to decision theorists, political scientists, economists, and researchers in artificial intelligence.