The research reported here deals with finding why people make some choices rather than others, why different people make different decisions in objectively similar situations. The book requires that its reader have some basic knowledge of statistical methods, and, since, it cuts across normally separate fields, it requires an adventuresome spirit. But, in return, the reader may expect to gain the use of a powerful tool that can be applied in his own practical projects and social science research.The message is on two levels. On one, the work is a practical handbook for application. On the other, it discusses some fundamental issues in the theory of decision-making and the social sciences.The book presents an application method for measuring assumptions realistic enough for use in management context. In a test-case study, the author uncovered startling diversity in the attributes investors use in picking stocks. More generally, such measures of assumptions are useful in managerial planning and control to aid in decision-making consistence, in learning to revise decision assumptions, and in designing information systems to support decision-making. They are also useful in improving joint decision-making and communication. Still other important applications are possible in consumer market research and in operations research modeling of decision processes. These applications are described with suggestive examples.To the management scientist the author seeks to show the benefits of extending explicitness beyond the traditional bounds of information systems into the realm of subjective decision assumptions. That is, subjective assumptions made explicit in a practical manner are employed as useful inputs to managerial information systems.Such measurement methods as reported here may also have widespread use in building social theory. Individual decision assumptions are key variables in microeconomics, in political science, in organization theory, and in the sociology of knowledge. Their measures play an analogous role in social science to that of thermometers in the development of thermodynamics.The material is developed as follows: First, the problem of discovering the assumptions which underlie decisions is sketched broadly. Alternative possible measurement approaches and theories are then described in logical order. An outline of the method for measuring assumptions is followed by the account of its use in a case study of stock market participants. It is this narrative that provides a practical handbook for the reader's use. A number of prototype applications are shown in some detail. The final chapters propose uses of the method for research in the social sciences and in accounting and the financial markets.

The Conference on "Utility: Theories, Measurements, and Applications" met at the Inn at Pasatiempo in Santa Cruz, California, from June II to 15, 1989. The all-star cast of attendees are listed as authors in the Table of Contents of this book (see p. V), except for Soo Hong Chew and Amos Tversky. The purpose of the conference, and of National Science Foundation Grant No. SES-8823012 that supported it, was to confront proponents of new generalized theories of utility with leading decision analysts com mitted to the implementation, in practice, of the more traditional theory that these new theories reject. That traditional model is variously iden tified in this book as expected utility or subjectively expected utility maximization (EU or SEU for short) and variously attributed to von Neumann and Morgenstern or Savage. I had feared that the conference might consist of an acrimonious debate between Olympian normative theorists uninterested in what people actually do and behavioral modelers obsessed with the cognitive illusions and uninterested in helping people to make wise decisions. I was entirely wrong. The conferees, in two dramatic straw votes at the open ing session, unanimously endorsed traditional SEU as the appropriate normative model and unanimously agreed that people don't act as that model requires. (These votes had a profound impact on my thinking; detail about them and about that impact is located in Chapter 10.

Over the past thirty-five years, a tremendous body of both theoretical and empirical research has been established on the `science of transportation'. The Handbook of Transportation Science has collected and synthesized this research into a systematic treatment of this field covering its fundamental concepts, methods, and principles. The purpose of this handbook is to define transportation as a scientific discipline that transcends transportation technology and methods. Whether by car, truck, airplane - or by a mode of transportation that has not yet been conceived - transportation obeys fundamental properties. The science of transportation defines these properties, and demonstrates how our knowledge of one mode of transportation can be used to explain the behavior of another. Transportation scientists are motivated by the desire to explain spatial interactions that result in movement of people or objects from place to place. Its methodologies draw from physics, operations research, probability and control theory. It is fundamentally a quantitative discipline, relying on mathematical models and optimization algorithms to explain the phenomena of transportation. The fourteen chapters in the handbook are written by the leading researchers in transportation science in an effort to define and categorize for the first time the scientific nature and state of the art of the field. As such, it is directed to the broader research community, transportation practitioners, and future transportation scientists.

Most economic evaluations of health care programmes at the moment are cost effectiveness and cost-utility analyses. The problem with these methods is that their theoretical foundations are unclear. This has led to confusion about how to define the costs and health effects and how to interpret the results of these studies. In the environmental and traffic safety fields it is instead common to carry out traditional cost-bene:fit analyses of health improving programmes. This striking difference in how health programmes are assessed in different fields was the original motivation for writing this book. The aim of the book is to tty and provide a coherent framework within cost-bene:fit analysis and welfare economics for the different methods of economic evaluation in the health care field. The book is written in an easily accessible manner and several examples of applications of the different methods are provided. It is my hope that it will be useful both for teaching purposes and as a guide for practitioners in the field. Glenn C. Blomquist, John D. Graham, Rich O'Conor and four anonymous referees provided helpful comments on previous versions of the manuscript. I would also like to express my gratitude to the following persons for helping me to prepare the manuscript: Carl-Magnus Berglund, Carin Blanksvard, Ann Brown, and Ziad Obeid.

The Encyclopedia of Medical Decision Making presents state-of-the-art research and ready-to-use facts sorting out findings on medical decision making and their applications.

Providing the first comprehensive treatment of the subject, this groundbreaking work is solidly founded on a decade of concentrated research, some of which is published here for the first time, as well as practical, ''hands on'' classroom experience. The clarity of presentation and abundance of examples and exercises make it suitable as a graduate level text in mathematics, decision making, artificial intelligence, and engineering courses.

To deal with the abundant amount of information in the environment in order to achieve our goals, human beings adopt a strategy to accumulate some information and filter out other information to ultimately make decisions. Since the development of cognitive science in the 1960s, researchers have been interested in understanding how human beings process and accumulate information for decision-making. Researchers have conducted extensive behavioral studies and applied a wide range of modeling tools to study human behavior in simple-detection tasks and two-choice decision tasks (e.g., discrimination, classification). In general, researchers often assume that the manner in which information is processed for decision-making is invariant across individuals given a particular experimental context. Independent variables, including speed-accuracy instructions, stimulus properties (i.e., intensity), and characteristics of the participants (i.e., aging, cognitive ability) are assumed to affect the parameters in a model (i.e., speed of information accumulation, response bias) but not the way that participants process information (e.g., the order of information processing). Given these assumptions, much modeling has been accomplished based on the grouped data, rather than the individual data. However, a growing number of studies have demonstrated that there were individual differences in the perceptual decision process. In the same task context, different groups of the participants may process information in different manners. The capacity and architecture of the decision mechanism were found to vary across individuals, implying that humans’ decision strategies can vary depending on the context to maximize their performance. In this special issue, we focused on a particular subset of cognitive models, particularly accumulator models, multinomial processing trees and systems factorial technology (SFT) as applied to perceptual decision making. The motivation for the focus on perceptual decision-making is threefold. Empirical studies of perception have grown out of a history of making a large number of observations for each individual so as to achieve precise estimates of each individual’s performance. This type of data, rather than a small number of observations per individual, is most amenable to achieving precision in individual-level and group-level cognitive modeling. Second, the interaction between the acquisition of perceptual information and the decisions based on that information (to the extent that those processes are distinguishable) offers rich data for scientific exploration. Finally, there is an increasing interest in the practical application of individual variation in perceptual ability, whether to inform perceptual training and expertise, or to guide personnel decisions. Although these practical applications are beyond the scope of this issue, we hope that the research presented herein may serve as the foundation for future endeavors in that domain.