Mapping the Intellectual Structure of Uncertainty Research in Decision Science and Management: A Keyword Co-Occurrence Network Analysis

Uncertainty is an important and frequent concept in decision science and management. It influences how researchers consider risks, take decisions and improve the adaptability of organizations. Although much research has been conducted, the interdisciplinary framework linking different fields of uncertainty is still in a divided condition. These disciplines include classical decision theory, behavioral economics and practical environmental management. In this review, bibliometric network mapping is applied to show and analyse the co-occurrence of keywords related to the idea of "uncertainty" in the decision science and management literature. By analysing the resultant keyword co-occurrence network, four major themes are found: 1. Methodological and modelling methods such as multi-criteria decision analysis, optimization and system approach. 2. Theoretical bases of classical decision theory like expected utility, entropy and decision-making under uncertainty. 3. Behavioural and risk-related aspects such as risk, ambiguity, heuristics, judgment and rationality. 4. Practical management and sustainable conditions like deep uncertainty, climate change, policy and trust. The prominent positions and sizes of the nodes representing "uncertainty", "decision-making", "risk", "model" and "decision-making" reveal their functions as conceptual links among various sub-fields. We discuss the theoretical and practical effects of this thematic structure and suggest some directions for future integrated research, especially on the intersection of deep uncertainty, adaptive management and behavioural decision theory.

decision science and management

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