Proactive Management of Regulatory Policy Ripple Effects via a Computational Hierarchical Change Management Structure

Abdulrahman Alrabiah and Steve Drew
Additional contact information
Abdulrahman Alrabiah: School of Information and Communication Technology, Griffith University, Southport 4215, Australia
Steve Drew: Tasmanian Institute of Learning and Teaching, University of Tasmania, Hobart 7001, Australia

Risks, 2020, vol. 8, issue 2, 1-29

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.

Keywords: regulatory policy management; banking regulation; computational regulation; ripple effects; feedback loops; causal loop analysis; quality attribute constraints (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-9091/8/2/49/pdf (application/pdf)
https://www.mdpi.com/2227-9091/8/2/49/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:8:y:2020:i:2:p:49-:d:361207

Access Statistics for this article

Risks is currently edited by Mr. Claude Zhang

More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().