Economists and psychologists have, on the whole, exhibited sharply different perspectives on the elicitation of preferences. Economists, who have made preference the central primitive in their thinking about human behavior, have for the most part rejected elicitation and have instead sought to infer preferences from observations of choice behavior. Psychologists, who have tended to think of preference as a context-determined subjective construct, have embraced elicitation as their dominant approach to measurement. This volume, based on a symposium organized by Daniel McFadden at the University of California at Berkeley, provides a provocative and constructive engagement between economists and psychologists on the elicitation of preferences.

Provides stated preference data collection methods, discrete choice models, and statistical analysis tools that can be used to forecast demand and assess welfare impacts for new or modified products or services in real markets, and summarize the conditions under which the reliability of these methods has been demonstrated or can be tested.

This book is about elicitation: the facilitation of the quantitative expression of subjective judgement about matters of fact, interacting with subject experts, or about matters of value, interacting with decision makers or stakeholders. It offers an integrated presentation of procedures and processes that allow analysts and experts to think clearly about numbers, particularly the inputs for decision support systems and models. This presentation encompasses research originating in the communities of structured probability elicitation/calibration and multi-criteria decision analysis, often unaware of each other’s developments. Chapters 2 through 9 focus on processes to elicit uncertainty from experts, including the Classical Method for aggregating judgements from multiple experts concerning probability distributions; the issue of validation in the Classical Method; the Sheffield elicitation framework; the IDEA protocol; approaches following the Bayesian perspective; the main elements of structured expert processes for dependence elicitation; and how mathematical methods can incorporate correlations between experts. Chapters 10 through 14 focus on processes to elicit preferences from stakeholders or decision makers, including two chapters on problems under uncertainty (utility functions), and three chapters that address elicitation of preferences independently of, or in absence of, any uncertainty elicitation (value functions and ELECTRE). Two chapters then focus on cross-cutting issues for elicitation of uncertainties and elicitation of preferences: biases and selection of experts. Finally, the last group of chapters illustrates how some of the presented approaches are applied in practice, including a food security case in the UK; expert elicitation in health care decision making; an expert judgement based method to elicit nuclear threat risks in US ports; risk assessment in a pulp and paper manufacturer in the Nordic countries; and elicitation of preferences for crop planning in a Greek region.

One of the main themes that has emerged from behavioral decision research during the past three decades is the view that people's preferences are often constructed in the process of elicitation. This idea is derived from studies demonstrating that normatively equivalent methods of elicitation (e.g., choice and pricing) give rise to systematically different responses. These preference reversals violate the principle of procedure invariance that is fundamental to all theories of rational choice. If different elicitation procedures produce different orderings of options, how can preferences be defined and in what sense do they exist? This book shows not only the historical roots of preference construction but also the blossoming of the concept within psychology, law, marketing, philosophy, environmental policy, and economics. Decision making is now understood to be a highly contingent form of information processing, sensitive to task complexity, time pressure, response mode, framing, reference points, and other contextual factors.

Computational social choice is an expanding field that merges classical topics like economics and voting theory with more modern topics like artificial intelligence, multiagent systems, and computational complexity. This book provides a concise introduction to the main research lines in this field, covering aspects such as preference modelling, uncertainty reasoning, social choice, stable matching, and computational aspects of preference aggregation and manipulation. The book is centered around the notion of preference reasoning, both in the single-agent and the multi-agent setting. It presents the main approaches to modeling and reasoning with preferences, with particular attention to two popular and powerful formalisms, soft constraints and CP-nets. The authors consider preference elicitation and various forms of uncertainty in soft constraints. They review the most relevant results in voting, with special attention to computational social choice. Finally, the book considers preferences in matching problems. The book is intended for students and researchers who may be interested in an introduction to preference reasoning and multi-agent preference aggregation, and who want to know the basic notions and results in computational social choice. Table of Contents: Introduction / Preference Modeling and Reasoning / Uncertainty in Preference Reasoning / Aggregating Preferences / Stable Marriage Problems

This book focuses on the recent development of methodologies and computation methods in mathematical and statistical modelling, computational science and applied mathematics. It emphasizes the development of theories and applications, and promotes interdisciplinary endeavour among mathematicians, statisticians, scientists, engineers and researchers from other disciplines. The book provides ideas, methods and tools in mathematical and statistical modelling that have been developed for a wide range of research fields, including medical, health sciences, biology, environmental science, engineering, physics and chemistry, finance, economics and social sciences. It presents original results addressing real-world problems. The contributions are products of a highly successful meeting held in August 2017 on the main campus of Wilfrid Laurier University, in Waterloo, Canada, the International Conference on Applied Mathematics, Modeling and Computational Science (AMMCS-2017). They make this book a valuable resource for readers interested not only in a broader overview of the methods, ideas and tools in mathematical and statistical approaches, but also in how they can attain valuable insights into problems arising in other disciplines.

Collaborative Filtering Recommender Systems discusses a wide variety of the recommender choices available and their implications, providing both practitioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.

This volume offers fresh insights into economic development and growth in emerging economies. It includes contributions covering topics such as natural disasters and income inequalities, the environmental impact of economic growth, social preferences, information and market disorder under democracy, inflation targeting and its covariates, economic empowerment. This book is intended for scholars in the field of economics, and those interested in furthering economic development.

This timely reference guide is specifically directed toward the needs of second language researchers, who can expect to gain a clearer understanding of which techniques may be most appropriate and fruitful in given research domains. Data Elicitation for Second and Foreign Language Research is a perfect companion to the same author team’s bestselling Second Language Research: Methodology and Design. It is an indispensable text for graduate or advanced-level undergraduate students who are beginning research projects in the fields of applied linguistics, second language acquisition, and TESOL as well as a comprehensive reference for more seasoned researchers.

Amos Tversky (1937–1996), a towering figure in cognitive and mathematical psychology, devoted his professional life to the study of similarity, judgment, and decision making. He had a unique ability to master the technicalities of normative ideals and then to intuit and demonstrate experimentally their systematic violation due to the vagaries and consequences of human information processing. He created new areas of study and helped transform disciplines as varied as economics, law, medicine, political science, philosophy, and statistics. This book collects forty of Tversky's articles, selected by him in collaboration with the editor during the last months of Tversky's life. It is divided into three sections: Similarity, Judgment, and Preferences. The Preferences section is subdivided into Probabilistic Models of Choice, Choice under Risk and Uncertainty, and Contingent Preferences. Included are several articles written with his frequent collaborator, Nobel Prize-winning economist Daniel Kahneman.