文章基本信息

标题：Proactive Management of Regulatory Policy Ripple Effects via a Computational Hierarchical Change Management Structure
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作者：Abdulrahman Alrabiah ; Steve Drew
期刊名称：Risks
印刷版ISSN：2227-9091
出版年度：2020
卷号：8
期号：49
页码：49
DOI：10.3390/risks8020049
语种：English
出版社：MDPI, Open Access Journal
摘要：The paper proposes a novel computational impact analysis framework to proactively manage dynamic constraints and optimally promote the inception of central banks’ regulatory policies. Currently, central banks are encountering contradictory challenges in developing and implementing regulatory policy. These constraints mainly comprise of incomplete or anomalous information (information asymmetry), and very tight temporal and resources limitations (bounded rationality) when the efficiency of a policy is determined at a system-level. The complex relationships of the policy attributes and their interactions generate very dynamic emergent behaviours due to the complex causal relationships. This paper adopted and tailored the hierarchical change management structure framework to design a first step framework called ‘computational regulatory policy change governance’. The methodology uses interviews, focus-group workshop and the application of empirical data. The results of the evaluation and case study validate its applicability in computing policy parameters and the impacts of their interactions. The evaluation of the framework gained a remarkable score, averaging a 130 per cent improvement compared to the existing methods. However, the research paper used a single case study, and its outcomes require further evaluation and testing. Accordingly, we invite regulators, banks, scholars and practitioners to explore the uniqueness and features of the proposed framework.