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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.
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.
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.
Ît then rigorously analyses each model in the tradition of microeconomic theory, leading to a richer, more realistic picture of consumer behavior. Ran Spiegler analyses phenomena such as exploitative price plans in the credit market, complexity of financial products and other obfuscation practices, consumer antagonism to unexpected price increases, and the role of default options in consumer decision making. Spiegler unifies the relevant literature into three main strands: limited ability to anticipate and control future choices, limited ability to understand complex market environments, and sensitivity to reference points. Although the challenge of enriching the psychology of decision makers in economic models has been at the frontier of theoretical research in the last decade, there has been no graduate-level, theory-oriented textbook to cover developments in the last 10-15 years.
The form of bounded rationality characterizing the representative agent is key in the choice of the optimal monetary policy regime. While inflation targeting prevails for myopia that distorts agents' inflation expectations, price level targeting emerges as the optimal policy under myopia regarding the output gap, revenue, or interest rate. To the extent that bygones are not bygones under price level targeting, rational inflation expectations is a minimal condition for optimality in a behavioral world. Instrument rules implementation of this optimal policy is shown to be infeasible, questioning the ability of simple rules à la Taylor (1993) to assist the conduct of monetary policy. Bounded rationality is not necessarily associated with welfare losses.
Recognising that the economy is a complex system with boundedly rational interacting agents, applies complexity modelling to economics and finance.
The Oxford Handbook of Computational Economics and Finance provides a survey of both the foundations of and recent advances in the frontiers of analysis and action. It is both historically and interdisciplinarily rich and also tightly connected to the rise of digital society. It begins with the conventional view of computational economics, including recent algorithmic development in computing rational expectations, volatility, and general equilibrium. It then moves from traditional computing in economics and finance to recent developments in natural computing, including applications of nature-inspired intelligence, genetic programming, swarm intelligence, and fuzzy logic. Also examined are recent developments of network and agent-based computing in economics. How these approaches are applied is examined in chapters on such subjects as trading robots and automated markets. The last part deals with the epistemology of simulation in its trinity form with the integration of simulation, computation, and dynamics. Distinctive is the focus on natural computationalism and the examination of the implications of intelligent machines for the future of computational economics and finance. Not merely individual robots, but whole integrated systems are extending their "immigration" to the world of Homo sapiens, or symbiogenesis.
Offering alternative models based on such concepts as satisficing(acceptance of viable choices that may not be the undiscoverableoptimum) and bounded rationality (the limited extent to which rationalcalculation can direct human behavior), Simon shows concretely whymore empirical research based on experiments and direct observation, rather than just statistical analysis of economic aggregates, isneeded.
Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial Intelligence technologies such as deep learning. They are used in decision making. One element of decision making is the issue of rationality. Regulations such as the General Data Protection Regulation (GDPR) require that decisions that are made by these intelligent machines are explainable. Rational Machines and Artificial Intelligence proposes that explainable decisions are good but the explanation must be rational to prevent these decisions from being challenged. Noted author Tshilidzi Marwala studies the concept of machine rationality and compares this to the rationality bounds prescribed by Nobel Laureate Herbert Simon and rationality bounds derived from the work of Nobel Laureates Richard Thaler and Daniel Kahneman. Rational Machines and Artificial Intelligence describes why machine rationality is flexibly bounded due to advances in technology. This effectively means that optimally designed machines are more rational than human beings. Readers will also learn whether machine rationality can be quantified and identify how this can be achieved. Furthermore, the author discusses whether machine rationality is subjective. Finally, the author examines whether a population of intelligent machines collectively make more rational decisions than individual machines. Examples in biomedical engineering, social sciences and the financial sectors are used to illustrate these concepts. - Provides an introduction to the key questions and challenges surrounding Rational Machines, including, When do we rely on decisions made by intelligent machines? What do decisions made by intelligent machines mean? Are these decisions rational or fair? Can we quantify these decisions? and Is rationality subjective? - Introduces for the first time the concept of rational opportunity costs and the concept of flexibly bounded rationality as a rationality of intelligent machines and the implications of these issues on the reliability of machine decisions - Includes coverage of Rational Counterfactuals, group versus individual rationality, and rational markets - Discusses the application of Moore's Law and advancements in Artificial Intelligence, as well as developments in the area of data acquisition and analysis technologies and how they affect the boundaries of intelligent machine rationality
At the core of the rational expectations revolution is the insight that economic policy does not operate independently of economic agents' knowledge of that policy and their expectations of the effects of that policy. This means that there are very complicated feedback relationships existing between policy and the behaviour of economic agents, and these relationships pose very difficult problems in econometrics when one tries to exploit the rational expectations insight in formal economic modelling. This volume consists of work by two rational expectations pioneers dealing with the "nuts and bolts" problems of modelling the complications introduced by rational expectations. Each paper deals with aspects of the problem of making inferences about parameters of a dynamic economic model on the basis of time series observations. Each exploits restrictions on an econometric model imposed by the hypothesis that agents within the model have rational expectations.