Remarkable advance in quantitative marketing research in the last two decades, incorporating applied microeconomic theories, operations research and management applications, has brought the field of marketing alongside with finance, accounting and productionto within an executive'sreach for a sophisticatedtoolbox for decision making in an increasingly competitive and complex business environment. A quick look at Marketing, a recently published book edited by Eliashberg and Lilien would indicate even to the casual reader the extent of such methodological progress made by marketing scholars. Even in such an impressive and nearly exhaustive collection oftopics, with the notable exception pointed out by the editors of applicationsofthe scanner data, and in spite of the reference to it, an important omission is related to the issues ofmarketing decisions under conditions ofuncertainty. It is fairly obvious to the marketing executive and academician alike to recognize the important role uncertaintyplays in marketingdecisions such as pricing, promotion, advertising, sales force management, and others. The major purpose of this study is to address certain major marketing decision variables within the general context of an uncertain environment. While there have been significant progresses in analyzing marketing behaviors in a stochastic environment,the sourcesscatteramong differentmanagementandmarketingjoumals; and to the extent that these issues are addressed at all, they have aimed mainly at each separate, specifictopic at a time. Thus, our effort to bring these studies together in the same framework should facilitate our in-depth analysis of these important phenomena.

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.

How should firms decide whether and when to invest in new capital equipment, additions to their workforce, or the development of new products? Why have traditional economic models of investment failed to explain the behavior of investment spending in the United States and other countries? In this book, Avinash Dixit and Robert Pindyck provide the first detailed exposition of a new theoretical approach to the capital investment decisions of firms, stressing the irreversibility of most investment decisions, and the ongoing uncertainty of the economic environment in which these decisions are made. In so doing, they answer important questions about investment decisions and the behavior of investment spending. This new approach to investment recognizes the option value of waiting for better (but never complete) information. It exploits an analogy with the theory of options in financial markets, which permits a much richer dynamic framework than was possible with the traditional theory of investment. The authors present the new theory in a clear and systematic way, and consolidate, synthesize, and extend the various strands of research that have come out of the theory. Their book shows the importance of the theory for understanding investment behavior of firms; develops the implications of this theory for industry dynamics and for government policy concerning investment; and shows how the theory can be applied to specific industries and to a wide variety of business problems.

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.

Uncertainty affects us in our everyday lives, and in a wide range of situations but how do individuals and indeed organisations respond to uncertainty and how does it impact their decision-making and actions? Based on the latest developments in academic research, the author offers solid advice on how to manage uncertainty in every-day life, bringing a new perspective to these issues and extrapolating this to offer implications for an organisational and managerial context. The author brings this emerging area of research to a wider audience by: Tying together insights from various fields including psychology, engineering, business and management. Creating a framework for usefully applying the research concepts in every-day life. Extrapolating insights from the psychology of individual decision makers to the organisational context and managerial decision-making. Creating highly applicable and impactful recommendations for how managers, organisations, and every day people can understand and manage uncertainty in their life. The book is divided into two main parts. Part I deals with the behaviour of individuals facing uncertainty and includes accessible explanations and examples of every-day applications, while Part II deals with behaviour in organisations facing uncertainty, where insights from Part I are combined and related to the organisational and work context to explore how, for example, (mis)-perceptions and decision-making biases impact managerial life. This is a must read for both managers and those who are seeking to better understand their own behaviour and management approach.