This book highlights research on the behavioral biases affecting judgmental accuracy in judgmental forecasting and showcases the state-of-the-art in judgment-based predictive analytics. In recent years, technological advancements have made it possible to use predictive analytics to exploit highly complex (big) data resources. Consequently, modern forecasting methodologies are based on sophisticated algorithms from the domain of machine learning and deep learning. However, research shows that in the majority of industry contexts, human judgment remains an indispensable component of the managerial forecasting process. This book discusses ways in which decision-makers can address human behavioral issues in judgmental forecasting.

The book begins by introducing readers to the notion of human-machine interactions. This includes a look at the necessity of managerial judgment in situations where organizations commonly have algorithmic decision support models at their disposal.The remainder of the book is divided into three parts, with Part I focusing on the role of individual-level judgment in the design and utilization of algorithmic models. The respective chapters cover individual-level biases such as algorithm aversion, model selection criteria, model-judgment aggregation issues and implications for behavioral change. In turn, Part II addresses the role of collective judgments in predictive analytics. The chapters focus on issues related to talent spotting, performance-weighted aggregation, and the wisdom of timely crowds. Part III concludes the book by shedding light on the importance of contextual factors as critical determinants of forecasting performance. Its chapters discuss the usefulness of scenario analysis, the role of external factors in time series forecasting and introduce the idea of mindful organizing as an approach to creating more sustainable forecasting practices in organizations.

Highlights the current state-of-the-art of judgment-based predictive analytics Describes and explains the uniqueness of behavioral issues in forecasting Covers various methodological approaches including analytical, experimental, and field study methods

Autorentext

Matthias Seifert is an Associate Professor of Decision Sciences in the Operations and Technology area at IE University. Prior to joining IE, he was affiliated with 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.

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