Beyond Risk: A Measure of Distribution Uncertainty

• 成果类型: Article
• 署名作者: Lu, Tao; Zhang, Lihong; Zhang, Xiaoquan (Michael); Zhao, Zhenling
• 署名单位: Southern University of Science & Technology; Tsinghua University; Chinese University of Hong Kong; Chinese Academy of Sciences; University of Science & Technology of China, CAS
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2022.0089
• 发表日期: 2025
• 关键词: expected utility; Financial market; rare disasters; rich domain; INFORMATION; ambiguity; product; MODEL; options; IMPACT

摘要

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 welldefined 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.