1. INTRODUCTION In the Spring of 1975 we held an international workshop on the Foundations and Application of Decision Theory at the University of Western Ontario. To help structure the workshop into ordered and manageable sessions we distri buted the following statement of our goals to all invited participants. They in turn responded with useful revisions and suggested their own areas of interest. Since this procedure provided the eventual format of the sessions, we include it here as the most appropriate introduction to these collected papers result ing from the workshop. The reader can readily gauge the approximation to our mutual goals. 2. STATEMENT or OBJECTIVES AND RATIONALE (Attached to this statement is a bibliography; names of persons cited in the statement and writing in this century will be found referenced in the biblio graphy - certain 'classics' aside. ) 2. 1. Preamble We understand in the following the Theory of Decisions in a broader sense than is presently customary, construing it to embrace a general theory of deciSion-making, induding social, political and economic theory and applica tions. Thus, we subsume the Theory of Games under the head of Decision Theory, regarding it as a particularly clearly formulated version of part of the general theory of decision-making.

The book also contains a major new discussion of what it means to suppose that some event occurs or that some proposition is true.

Decision theory is generally taught in one of two very different ways. When of opti taught by theoretical statisticians, it tends to be presented as a set of mathematical techniques mality principles, together with a collection of various statistical procedures. When useful in establishing the optimality taught by applied decision theorists, it is usually a course in Bayesian analysis, showing how this one decision principle can be applied in various practical situations. The original goal I had in writing this book was to find some middle ground. I wanted a book which discussed the more theoretical ideas and techniques of decision theory, but in a manner that was constantly oriented towards solving statistical problems. In particular, it seemed crucial to include a discussion of when and why the various decision prin ciples should be used, and indeed why decision theory is needed at all. This original goal seemed indicated by my philosophical position at the time, which can best be described as basically neutral. I felt that no one approach to decision theory (or statistics) was clearly superior to the others, and so planned a rather low key and impartial presentation of the competing ideas. In the course of writing the book, however, I turned into a rabid Bayesian. There was no single cause for this conversion; just a gradual realization that things seemed to ultimately make sense only when looked at from the Bayesian viewpoint.

For courses in Decision Making and Engineering. The Fundamentals of Analyzing and Making Decisions Foundations of Decision Analysis is a groundbreaking text that explores the art of decision making, both in life and in professional settings. By exploring themes such as dealing with uncertainty and understanding the distinction between a decision and its outcome, the First Edition teaches readers to achieve clarity of action in any situation. The book treats decision making as an evolutionary process from a scientific standpoint. Strategic decision-making analysis is presented as a tool to help students understand, discuss, and settle on important life choices. Through this text, readers will understand the specific thought process that occurs behind approaching any decision to make easier and better life choices for themselves.

Decision theory provides a formal framework for making logical choices in the face of uncertainty. Given a set of alternatives, a set of consequences, and a correspondence between those sets, decision theory offers conceptually simple procedures for choice. This book presents an overview of the fundamental concepts and outcomes of rational decision making under uncertainty, highlighting the implications for statistical practice. The authors have developed a series of self contained chapters focusing on bridging the gaps between the different fields that have contributed to rational decision making and presenting ideas in a unified framework and notation while respecting and highlighting the different and sometimes conflicting perspectives. This book: * Provides a rich collection of techniques and procedures. * Discusses the foundational aspects and modern day practice. * Links foundations to practical applications in biostatistics, computer science, engineering and economics. * Presents different perspectives and controversies to encourage readers to form their own opinion of decision making and statistics. Decision Theory is fundamental to all scientific disciplines, including biostatistics, computer science, economics and engineering. Anyone interested in the whys and wherefores of statistical science will find much to enjoy in this book.

By framing issues, identifying risks, eliciting stakeholder preferences, and suggesting alternative approaches, decision analysts can offer workable solutions in domains such as the environment, health and medicine, engineering and operations research, and public policy. This book reviews and extends the material typically presented in introductory texts. Not a single book covers the broad scope of decision analysis at this advanced level. It will be a valuable resource for academics and students in decision analysis as well as decision analysts and managers

One of the goals of artificial intelligence (AI) is creating autonomous agents that must make decisions based on uncertain and incomplete information. The goal is to design rational agents that must take the best action given the information available and their goals. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions provides an introduction to different types of decision theory techniques, including MDPs, POMDPs, Influence Diagrams, and Reinforcement Learning, and illustrates their application in artificial intelligence. This book provides insights into the advantages and challenges of using decision theory models for developing intelligent systems.

Decision Theory and Decision Analysis: Trends and Challenges is divided into three parts. The first part, overviews, provides state-of-the-art surveys of various aspects of decision analysis and utility theory. The second part, theory and foundations, includes theoretical contributions on decision-making under uncertainty, partial beliefs and preferences. The third section, applications, reflects the real possibilities of recent theoretical developments such as non-expected utility theories, multicriteria decision techniques, and how these improve our understanding of other areas including artificial intelligence, economics, and environmental studies.

In this volume we present some o~ the papers that were delivered at FUR-82 - the First International Con~erence on Foundations o~ Utility and Risk Theory in Oslo, June 1982. The purpose o~ the con~erence was to provide a ~orum within which scientists could report on interesting applications o~ modern decision theory and exchange ideas about controversial issues in the ~oundations o~ the theory o~ choice under un certainty. With that purpose in mind we have selected a mixture of applied and theoretical papers that we hope will appeal to a wide spectrum o~ readers ~rom graduate students in social science departments and business schools to people involved in making hardheaded decisions in business and government. In an introductory article Ole Hagen gives an overview o~ various paradoxes in utility and risk theory and discusses these in the light o~ scientific methodology. He concludes the article by calling ~or joint efforts to provide decision makers with warkable theories. Kenneth Arrow takes up the same issue on a broad basis in his paper where he discusses the implications o~ behavior under uncertainty for policy. In the theoretical papers the reader will ~ind attempts at de~initive Statements of the meaning o~ old concepts and suggestions for the adoption o~ new concepts. For instance, Maurice Allais discusses four di~ferent interpretations o~ the axioms o~ probability and explains the need ~or an empirical characterization o~ the concept of chance.