Judgment in Predictive Analytics
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Description
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.
Matthias Seifert is a tenured Associate Professor of Decision Sciences in the Operations and Technology area at IE University. In addition, he is currently serving as a technical member (representing Germany) of the Exploratory Team on Prediction and Intelligence Analysis at the NATO Science and Technology Organization. Previously, he was affiliated to 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, Nature Human Behavior, Journal of Operations Management, Personality and Social Psychology Bulletin (and others) as well as in practitioner outlets such as Harvard Business Review and MIT Sloan Management Review.
In the past, he was an elected Council Member of the Decision Analysis Society (INFORMS). His work 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 World’s Best 40 Under 40 Business School Professors” by Poets & Quants.