Beyond Risk: A Measure of Distribution Uncertainty

Tao Lu (), Lihong Zhang (), Xiaoquan (Michael) Zhang () and Zhenling Zhao ()
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Tao Lu: College of Business, Southern University of Science and Technology, Shenzhen 518055, China
Lihong Zhang: School of Economics and Management, Tsinghua University, Beijing 100084, China
Xiaoquan (Michael) Zhang: Department of Decisions, Operations and Technology, CUHK Business School, Chinese University of Hong Kong, Hong Kong
Zhenling Zhao: Faculty of Business in Scitech, School of Management, University of Science and Technology of China, Hefei 230026, China

Information Systems Research, 2025, vol. 36, issue 2, 944-961

Abstract: Uncertainty, particularly distribution uncertainty (a.k.a. ambiguity), holds significant relevance in both academic research and practical applications. Much of the existing research, however, has concentrated primarily on addressing outcome uncertainty (or risk), frequently neglecting the aspect of distribution uncertainty. This research delves into distribution uncertainty, a critical yet often overlooked aspect of empirical research. We argue that there is a pressing need to integrate considerations of ambiguity directly into the development and implementation of data analytics models, calling for the promotion and wider use of a well-defined measure of ambiguity. We introduce a quantitative measure of ambiguity that surpasses conventional approaches by precisely capturing distribution uncertainty. We illustrate the properties and advantages of this measure, highlighting its ability to enhance empirical models, yield more reliable parameter estimates, and contribute to the decision-making process. Using decision making in the financial market as an example, we demonstrate the value of this ambiguity measure. This paper promotes a more nuanced understanding of uncertainty and offers implications for both research methodologies and practical risk management.

Keywords: ambiguity; financial market; uncertainty; distribution uncertainty; statistical inference (search for similar items in EconPapers)
Date: 2025
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