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GoRIM: a model-driven method for enhancing regulatory intelligence

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Abstract

Regulators are under constant pressure to demonstrate if and how the regulations they administer, which impose many requirements on various systems and processes, achieve intended societal outcomes. Traditionally, regulators have relied on impact assessments, risk analysis, and cost–benefit analysis to assess compliance with regulations. These methods, however, are effort and time intensive and focus on the efficiency of regulatory processes rather than on the effectiveness of the regulatory initiatives meant to improve compliance to regulations and the latter’s impact on intended societal outcomes. Goal-oriented modelling and data analytics approaches provide the basis for the development of more sophisticated methods and tools to better address the needs of regulators. This paper introduces the goal-oriented regulatory intelligence method (GoRIM), which enables effective management of regulations through modelling and data analytics. Through continuous monitoring, assessing, and reporting on efficiency and effectiveness aspects, GoRIM is meant to facilitate the analysis of feedback loops between regulations, regulatory initiatives, and societal outcomes. To demonstrate the applicability and perceived usefulness of GoRIM in addressing the first feedback loop between regulations and initiatives, we evaluated it through three case studies involving regulators from different contexts, with positive results. GoRIM extends the concept of regulatory intelligence beyond the analysis of compliance. It also provides practical guidelines and tools to regulators for making, in a timely way, evidence-based decisions related to the addition, modification, or repeal of regulations and related regulatory initiatives. In addition, GoRIM helps better identify software and information needs for enabling such decisions.

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Availability of data and material

The interview questionnaires used to validate GoRIM, together with the protocol and the thematic analysis of answers, are available at http://bit.ly/GoRIM-supp. Other data collected as part of this research cannot be made available due to constraints imposed by our institution’s Research Ethics Board.

Code availability

jUCMNav’s code, including OCL well-formedness rules, are available online at https://github.com/JUCMNAV/.

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Acknowledgements

The authors wholeheartedly thank J. Mylopoulos, L. Peyton, and S. Liaskos for their feedback on GoRIM, S. Heap, S. Islam, G. Labasse, and J. Habbouche for contributing to some GoRIM tooling, as well as N. Cartwright, E. Braun, D. Ikonomi, A. Neef, C. Ladanowski, A. Willsie, C. Doiron, and C. Lacroix for their collaboration and support. We are also grateful to the many key informants who participated to our evaluation.

Funding

This work was supported by Natural Sciences and Engineering Research Council of Canada’s (NSERC) through its Discovery Grant Program, Interis Consulting/BDO, and the University of Ottawa.

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Authors and Affiliations

  1. Telfer School of Management, University of Ottawa, Ottawa, Canada

    Gregory Richards & Lysanne Lessard

  2. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Daniel Amyot

  3. Business Consultant with the Government of Canada, University of Ottawa, Ottawa, Canada

    Okhaide Akhigbe

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Appendix A: Selected well-formedness rules for GRL models

Appendix A: Selected well-formedness rules for GRL models

In step 3 of our method (Sect. 4.3), the modeller checks the GRL models against well-formedness rules, listed in Table

Table 4 Mapping between regulatory initiatives (or societal outcomes) and GRL model elements
Full size table

4. These 19 rules are user-selectable OCL constraints supported by jUCMNav [45]. Violations to these rules are reported automatically by jUCMNav, which then highlights the violating model elements. Satisfying these rules helps ensure that the input models satisfy specific static properties that go beyond standard URN.

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Akhigbe, O., Amyot, D., Richards, G. et al. GoRIM: a model-driven method for enhancing regulatory intelligence. Softw Syst Model 21, 1613–1641 (2022). https://doi.org/10.1007/s10270-021-00949-z

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  • DOI: https://doi.org/10.1007/s10270-021-00949-z

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