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Proactive management of regulatory policy ripple effects via a computational hierarchical change management structure

Citation

Alrabiah, A and Drew, S, Proactive management of regulatory policy ripple effects via a computational hierarchical change management structure, Risks, 8, (2) pp. 1-29. ISSN 2227-9091 (2020) [Refereed Article]


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DOI: doi:10.3390/risks8020049

Abstract

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.

Item Details

Item Type:Refereed Article
Keywords:regulatory policy management, banking regulation, computational regulation, ripple effects, feedback loops, causal loop analysis, quality attribute constraints
Research Division:Commerce, Management, Tourism and Services
Research Group:Banking, finance and investment
Research Field:Investment and risk management
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Drew, S (Dr Steve Drew)
ID Code:152831
Year Published:2020
Web of Science® Times Cited:1
Deposited By:Medicine
Deposited On:2022-08-25
Last Modified:2022-08-26
Downloads:0

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