Using a Metadata Approach to Extend the Functional Resonance Analysis Method to Model Quantitatively, Emergent Behaviours in Complex Systems
In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is, of course, exacerbated by the increasing involvement and augmentation with “black...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Systems |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-8954/12/3/90 |
| _version_ | 1797239218670403584 |
|---|---|
| author | Rees Hill David Slater |
| author_facet | Rees Hill David Slater |
| author_sort | Rees Hill |
| collection | DOAJ |
| description | In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is, of course, exacerbated by the increasing involvement and augmentation with “black box” AI contributions. Hollnagel produced a methodology (FRAM) which did allow the analyst insights into these systems’ behaviour, but the model-based system engineering applications demand numbers and a quantitative approach. In the last 10 years, this original approach, developed to model systems as sets of interactive, interdependent “functions” (abstracted from agent or component details), has been further developed to the point where it can take the basic data and structures from the current component-focussed system engineering “models”, and can pull them all together into dynamic models (as opposed to the static, fixed System Theoretic Process Accimaps) from which analysts can discern how they really work in practice, and predict the emergent behaviours characteristic of complex systems. This paper describes how the FRAM methodology has now been extended to provide these extra, essential attributes. It also describes its implementation using an open-source software, freely available for use and verification on the GitHub site. |
| first_indexed | 2024-04-24T17:48:03Z |
| format | Article |
| id | doaj.art-291470af083344cc8463aba74b5e16b8 |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-04-24T17:48:03Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-291470af083344cc8463aba74b5e16b82024-03-27T14:05:43ZengMDPI AGSystems2079-89542024-03-011239010.3390/systems12030090Using a Metadata Approach to Extend the Functional Resonance Analysis Method to Model Quantitatively, Emergent Behaviours in Complex SystemsRees Hill0David Slater1Zerprize Ltd., Christchurch 8053, New ZealandSchool of Engineering, Cardiff University, Cardiff CF24 3AA, UKIn an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is, of course, exacerbated by the increasing involvement and augmentation with “black box” AI contributions. Hollnagel produced a methodology (FRAM) which did allow the analyst insights into these systems’ behaviour, but the model-based system engineering applications demand numbers and a quantitative approach. In the last 10 years, this original approach, developed to model systems as sets of interactive, interdependent “functions” (abstracted from agent or component details), has been further developed to the point where it can take the basic data and structures from the current component-focussed system engineering “models”, and can pull them all together into dynamic models (as opposed to the static, fixed System Theoretic Process Accimaps) from which analysts can discern how they really work in practice, and predict the emergent behaviours characteristic of complex systems. This paper describes how the FRAM methodology has now been extended to provide these extra, essential attributes. It also describes its implementation using an open-source software, freely available for use and verification on the GitHub site.https://www.mdpi.com/2079-8954/12/3/90complex systemsmodel-based system engineeringFRAM |
| spellingShingle | Rees Hill David Slater Using a Metadata Approach to Extend the Functional Resonance Analysis Method to Model Quantitatively, Emergent Behaviours in Complex Systems Systems complex systems model-based system engineering FRAM |
| title | Using a Metadata Approach to Extend the Functional Resonance Analysis Method to Model Quantitatively, Emergent Behaviours in Complex Systems |
| title_full | Using a Metadata Approach to Extend the Functional Resonance Analysis Method to Model Quantitatively, Emergent Behaviours in Complex Systems |
| title_fullStr | Using a Metadata Approach to Extend the Functional Resonance Analysis Method to Model Quantitatively, Emergent Behaviours in Complex Systems |
| title_full_unstemmed | Using a Metadata Approach to Extend the Functional Resonance Analysis Method to Model Quantitatively, Emergent Behaviours in Complex Systems |
| title_short | Using a Metadata Approach to Extend the Functional Resonance Analysis Method to Model Quantitatively, Emergent Behaviours in Complex Systems |
| title_sort | using a metadata approach to extend the functional resonance analysis method to model quantitatively emergent behaviours in complex systems |
| topic | complex systems model-based system engineering FRAM |
| url | https://www.mdpi.com/2079-8954/12/3/90 |
| work_keys_str_mv | AT reeshill usingametadataapproachtoextendthefunctionalresonanceanalysismethodtomodelquantitativelyemergentbehavioursincomplexsystems AT davidslater usingametadataapproachtoextendthefunctionalresonanceanalysismethodtomodelquantitativelyemergentbehavioursincomplexsystems |