Multi-dimensional policy decision-making model based on the quantum probability
The case of Korea’s nuclear power plant policy
Bibliographic Data
| ID | 22235019 |
|---|---|
| Authors | Seunghwan Myeong (0000-0002-6730-2770, Inha University, corresponding author), Jae Woo Lee (0000-0002-5739-9804, Inha University), Jaehyun Bae (0000-0002-7300-4518, Inha University), Chang Hee Cho (Inha University) |
| Year | 2025 |
| Volume | 12 |
| Issue | 1 |
| Publication date | 2025-06-26 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Humanities and Social Sciences Communications (JOURNAL) |
| Journal identifiers | ISSN: 2662-9992 • E-ISSN: 2662-9992 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1057/s41599-025-05019-z |
| OpenAlex | W4411670787 |
| Language | EN |
| References cited | 28 |
Implementing a digital government is a more rational and transparent decision. However, many complex real-practice policies, such as nuclear energy, pensions, climate change, and aging societies, may require more nuanced and multi-dimensional perspectives. This study critiques the schools of deterministic perspectives and examines alternative frameworks grounded in quantum probability theories and multi-dimensional decision-making models. It further examines policy transparency and its interaction with policy values. Through an empirical case study of Korea’s nuclear power policy, the study demonstrates the transformative potential of a quantum-informed approach in policy-making. Our findings emphasize incorporating interaction, correction, and recalibration processes to improve policy effectiveness. In this respect, this study proposes a multi-dimensional policy-making model based on quantum probability. Inspiring a new era in policy-making, this quantum probability model proposes the integration of norms, institutions, technology, and contextual variables
Decision-making models · Nuclear energy policy · Nuclear physics · Nuclear power · Nuclear power plant · Physics · Quantum mechanics · Computer Science · Risk Perception and Management · Technology and Data Analysis · Artificial Intelligence
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| Citation velocity | historical |
|---|---|
| Highly cited | No |