The third volume of Arrow's Collected Papers concerns the basic concept of rationality as it applies to an economic decision maker. In particular, it addresses the problem of choice faced by consumers in a multicommodity world and presents specific models of choice useful in economic analysis. It also discusses choice models under uncertainty.
A timeless classic of economic theory that remains fascinating and pertinent today, this is Frank Knight's famous explanation of why perfect competition cannot eliminate profits, the important differences between "risk" and "uncertainty," and the vital role of the entrepreneur in profitmaking. Based on Knight's PhD dissertation, this 1921 work, balancing theory with fact to come to stunning insights, is a distinct pleasure to read. FRANK H. KNIGHT (1885-1972) is considered by some the greatest American scholar of economics of the 20th century. An economics professor at the University of Chicago from 1927 until 1955, he was one of the founders of the Chicago school of economics, which influenced Milton Friedman and George Stigler.
This book describes the classical axiomatic theories of decision under uncertainty, as well as critiques thereof and alternative theories. It focuses on the meaning of probability, discussing some definitions and surveying their scope of applicability. The behavioral definition of subjective probability serves as a way to present the classical theories, culminating in Savage's theorem. The limitations of this result as a definition of probability lead to two directions - first, similar behavioral definitions of more general theories, such as non-additive probabilities and multiple priors, and second, cognitive derivations based on case-based techniques.
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
These authors draw on nearly 50 years of combined teaching and consulting experience to give readers a straightforward yet systematic approach for making estimates about the likelihood and consequences of future events -- and then using those assessments to arrive at sound decisions. The book's real-world cases, supplemented with expository text and spreadsheets, help readers master such techniques as decision trees and simulation, such concepts as probability, the value of information, and strategic gaming; and such applications as inventory stocking problems, bidding situations, and negotiating.
Economic analysis of choice under uncertainty has been dominated by the expected utility (EU) model, yet the EU model has never been without critics. Psychologists accumulated evidence that individual choices under uncertainty were inconsistent with the predictions of the EU model. Applied work in areas such as finance was dominated by the simpler mean-variance analysis. In the 1980s this skepticism was dispelled as a number of generalizations of EU were proposed, most of which were capable of explaining evidence inconsistent with EU, while preserving transitivity and dominance. Generalized expected utility is now a flourishing subfield of economics, with dozens of competing models and considerable literature exploring their theoretical properties and comparing their empirical performance. But the EU model remains the principal tool for the analysis of choice under uncertainty. There is a view that generalized models are too difficult to handle or incapable of generating sharp results. This creates a need to show that the new models can be used in the kinds of economic analysis for which EU has been used, and that they can yield new and interesting results. This book meets this need by describing one of the most popular generalized models -- the rank-dependent expected utility model (RDEU), also known as anticipated utility, EU with rank-dependent preferences, the dual theory of choice under uncertainty, and simply as rank-dependent utility. As the many names indicate, the model has been approached in many ways by many scientists and for this reason, consideration of a single model sheds light on many of the concerns that have motivated the development of generalized utility models. The popularity of the RDEU model rests on its simplicity and tractability. The standard tools of analysis developed for EU theory may be applied to the RDEU model, but since RDEU admits behavior inconsistent with EU, the field of potential applications is widened. As such, the RDEU model is not as much a competitor to EU as an extension based on less restrictive assumptions.
The social sciences study knowing subjects and their interactions. A "cog nitive turn", based on cognitive science, has the potential to enrich these sciences considerably. Cognitive economics belongs within this movement of the social sciences. It aims to take into account the cognitive processes of individuals in economic theory, both on the level of the agent and on the level of their dynamic interactions and the resulting collective phenomena. This is an ambitious research programme that aims to link two levels of com plexity: the level of cognitive phenomena as studied and tested by cognitive science, and the level of collective phenomena produced by the economic in teractions between agents. Such an objective requires cooperation, not only between economists and cognitive scientists but also with mathematicians, physicists and computer scientists, in order to renew, study and simulate models of dynamical systems involving economic agents and their cognitive mechanisms. The hard core of classical economics is the General Equilibrium Theory, based on the optimising rationality of the agent and on static concepts of equilibrium, following a point of view systemised in the framework of Game Theory. The agent is considered "rational" if everything takes place as if he was maximising a function representing his preferences, his utility function.
An understanding of risk and how to deal with it is an essential part of modern economics. Whether liability litigation for pharmaceutical firms or an individual's having insufficient wealth to retire, risk is something that can be recognized, quantified, analyzed, treated--and incorporated into our decision-making processes. This book represents a concise summary of basic multiperiod decision-making under risk. Its detailed coverage of a broad range of topics is ideally suited for use in advanced undergraduate and introductory graduate courses either as a self-contained text, or the introductory chapters combined with a selection of later chapters can represent core reading in courses on macroeconomics, insurance, portfolio choice, or asset pricing. The authors start with the fundamentals of risk measurement and risk aversion. They then apply these concepts to insurance decisions and portfolio choice in a one-period model. After examining these decisions in their one-period setting, they devote most of the book to a multiperiod context, which adds the long-term perspective most risk management analyses require. Each chapter concludes with a discussion of the relevant literature and a set of problems. The book presents a thoroughly accessible introduction to risk, bridging the gap between the traditionally separate economics and finance literatures.
Decision Making Under Uncertainty in Electricity Markets provides models and procedures to be used by electricity market agents to make informed decisions under uncertainty. These procedures rely on well established stochastic programming models, which make them efficient and robust. Particularly, these techniques allow electricity producers to derive offering strategies for the pool and contracting decisions in the futures market. Retailers use these techniques to derive selling prices to clients and energy procurement strategies through the pool, the futures market and bilateral contracting. Using the proposed models, consumers can derive the best energy procurement strategies using the available trading floors. The market operator can use the techniques proposed in this book to clear simultaneously energy and reserve markets promoting efficiency and equity. The techniques described in this book are of interest for professionals working on energy markets, and for graduate students in power engineering, applied mathematics, applied economics, and operations research.
