This book lays out a foundation and taxonomy for Behavioral Decision Analysis, featuring representative work across various domains. Traditional research in the domain of Decision Analysis has focused on the design and application of logically consistent tools to support decision makers during the process of structuring problem complexity, modeling uncertainty, generating predictions, eliciting preferences, and, ultimately, making better decisions. Two commonly held assumptions are that the decision maker's cognitive belief system is fully accessible and that this system can be understood and formalized by trained analysts. However, in past years, an active line of research has emerged studying instances in which such assumptions may not hold. This book unites this community under the common theme of Behavioral Decision Analysis. The taxonomy used in this book categorizes research based on task focus (prediction or decision) and behavioral level (individual or group). Two theoretical lenses that lie at the interface between (1) normative and descriptive research, and (2) normative and prescriptive research are introduced. The book then proceeds to highlight representative works across the two lenses focused on individual and group-level decision making. Featuring various methodologies and applications, the book serves as a reference for researchers, students, and professionals across different disciplines with a common interest in Behavioral Decision Analysis.
Florian Federspiel is an Assistant Professor of Decision Sciences and Head of the Quantitative Methods and Analytics area at INCAE Business School (Costa Rica). Prior to joining INCAE he was a Visiting Scholar in the Operations and Information Management Group of Georgetown University's McDonough School of Business in the USA and Adjunct Professor in the area of Operations and Technology at IE Business School (Spain). His research focuses on behavioral decision analysis, particularly regarding the issues of risk perception and risk preferences.
Gilberto Montibeller is a Full Professor of Operations Management at the University of Bristol Business School (UK). He is an Associate Editor ofthe INFORMS Decision Analysis Journal and an Area Editor of the Journal of Multi-Criteria Decision Analysis. He has published widely in decision sciences; the quality of his research has been recognized by best publication awards by the INFORMS Decision Analysis Society, the Society for Risk Analysis, and the International Society on Multi-Criteria Decision Making. His research is focused on behavioral issues in risk and decision analysis and on the applications of these methods for health security and health operations. The scientific rigour and impact of his applied research projects have been recognised by the INFORMS Decision Analysis Society Practice and the EURO Excellence in Practice Awards.
Matthias Seifert is an Associate Professor of Decision Sciences in the Operations and Technology area at IE University (Spain). Prior to joining IE, he was affiliatedwith the London Business School, Cambridge University, and the London School of Economics and Political Science. Dr. Seifert's research focuses on decision making under risk and uncertainty and managerial forecasting. His work has been published in top academic journals including Management Science, Organizational Behavior and Human Decision Processes, Journal of Operations Management, Nature Human Behavior, Personality and Social Psychology Bulletin (and others) as well as in practitioner outlets such as Harvard Business Review and MIT Sloan Management Review. Dr. Seifert is an Associate Editor at the International Journal of Forecasting and serves on the editorial board of outlets including Organizational Behavior and Human Decision Processes and Decision Analysis. His research has been featured by public media including Forbes India, Ideas for Leaders, CBS News, the Financial Times International ("Professor of the Week"), Psychology Today and others. In 2016, he has been named as one of "The Best 40 Under 40 Business School Professors" by Poets & Quants.
