Application of Systems-Approach in Modelling Complex City-Scale Transdisciplinary Knowledge Co-Production Process and Learning Patterns for Climate Resilience

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dc.contributor.authorMkandawire, Burnet
dc.contributor.authorThole, Bernard
dc.contributor.authorMamiwa, Dereck
dc.contributor.authorMlowa, Tawina
dc.contributor.authorMcClure, Alice
dc.contributor.authorKavonic, Jessica
dc.contributor.authorJack, Christopher
dc.date.accessioned2021-10-15T07:44:39Z
dc.date.available2021-10-15T07:44:39Z
dc.date.issued2021-01-22
dc.date.updated2021-03-26T14:08:03Z
dc.description.abstractLiterature shows that much research has been conducted on the co-production of climate knowledge, but it has neither established a standardized and replicable model for the co-production process nor the emergent learning patterns as collaborators transition from the disciplinary comfort-zone (disciplinary and practice biases) to the transdisciplinary third-space. This study combines algorithmic simulation modelling and case study lessons from Learning Labs under a 4-year (2016–2019) climate change management project called Future Resilience of African CiTies and Lands in the City of Blantyre in Malawi. The study fills the research gap outlined above by applying a systems-approach to replicate the research process, and a Markov process to simulate the learning patterns. Results of the study make a number of contributions to knowledge. First, there are four distinct evolutionally stages when transitioning from the disciplinary comfort-zone to the transdisciplinary third-space, namely: Shock and resistance to change; experimenting and exploring; acceptance; and integration into the third-space. These stages are marked by state probabilities of the subsequent stages relative to the initial (disciplinary comfort-zone) state. A complete transition to the third-space is marked by probabilities greater than one, which is a system amplification, and it signifies that there has been a significant increase in learning among collaborating partners during the learning process. Second, a four-step decision support tool has been developed to rank the plausibility of decisions, which is very hard to achieve in practice. The tool characterizes decision determinants (policy actors, evidence and knowledge, and context), expands the determinants, checks what supports the decision, and then rates decisions on an ordinal scale of ten in terms of how knowledge producers and users support them. Third, for a successful transdisciplinary knowledge co-production, researchers should elucidate three system-archetypes (leverage points), namely: Salient features for successful co-production, determinant of support from collaborators, and knowledge co-production challenges. It is envisioned that academics, researchers, and policy makers will find the results useful in modelling and replicating the co-production process in a methodical and systemic way while solving complex climate resilience development problems in dynamic, socio-technical systems, as well as in sustainably mainstreaming the knowledge co-produced in policies and plans.en_US
dc.identifier10.3390/systems9010007
dc.identifier.apacitationMkandawire, B., Thole, B., Mamiwa, D., Mlowa, T., McClure, A., Kavonic, J., & Jack, C. (2021). Application of Systems-Approach in Modelling Complex City-Scale Transdisciplinary Knowledge Co-Production Process and Learning Patterns for Climate Resilience. <i>Systems</i>, 9(1), 7. http://hdl.handle.net/11427/35260en_ZA
dc.identifier.chicagocitationMkandawire, Burnet, Bernard Thole, Dereck Mamiwa, Tawina Mlowa, Alice McClure, Jessica Kavonic, and Christopher Jack "Application of Systems-Approach in Modelling Complex City-Scale Transdisciplinary Knowledge Co-Production Process and Learning Patterns for Climate Resilience." <i>Systems</i> 9, 1. (2021): 7. http://hdl.handle.net/11427/35260en_ZA
dc.identifier.citationMkandawire, B., Thole, B., Mamiwa, D., Mlowa, T., McClure, A., Kavonic, J. & Jack, C. 2021. Application of Systems-Approach in Modelling Complex City-Scale Transdisciplinary Knowledge Co-Production Process and Learning Patterns for Climate Resilience. <i>Systems.</i> 9(1):7. http://hdl.handle.net/11427/35260en_ZA
dc.identifier.ris TY - Journal Article AU - Mkandawire, Burnet AU - Thole, Bernard AU - Mamiwa, Dereck AU - Mlowa, Tawina AU - McClure, Alice AU - Kavonic, Jessica AU - Jack, Christopher AB - Literature shows that much research has been conducted on the co-production of climate knowledge, but it has neither established a standardized and replicable model for the co-production process nor the emergent learning patterns as collaborators transition from the disciplinary comfort-zone (disciplinary and practice biases) to the transdisciplinary third-space. This study combines algorithmic simulation modelling and case study lessons from Learning Labs under a 4-year (2016–2019) climate change management project called Future Resilience of African CiTies and Lands in the City of Blantyre in Malawi. The study fills the research gap outlined above by applying a systems-approach to replicate the research process, and a Markov process to simulate the learning patterns. Results of the study make a number of contributions to knowledge. First, there are four distinct evolutionally stages when transitioning from the disciplinary comfort-zone to the transdisciplinary third-space, namely: Shock and resistance to change; experimenting and exploring; acceptance; and integration into the third-space. These stages are marked by state probabilities of the subsequent stages relative to the initial (disciplinary comfort-zone) state. A complete transition to the third-space is marked by probabilities greater than one, which is a system amplification, and it signifies that there has been a significant increase in learning among collaborating partners during the learning process. Second, a four-step decision support tool has been developed to rank the plausibility of decisions, which is very hard to achieve in practice. The tool characterizes decision determinants (policy actors, evidence and knowledge, and context), expands the determinants, checks what supports the decision, and then rates decisions on an ordinal scale of ten in terms of how knowledge producers and users support them. Third, for a successful transdisciplinary knowledge co-production, researchers should elucidate three system-archetypes (leverage points), namely: Salient features for successful co-production, determinant of support from collaborators, and knowledge co-production challenges. It is envisioned that academics, researchers, and policy makers will find the results useful in modelling and replicating the co-production process in a methodical and systemic way while solving complex climate resilience development problems in dynamic, socio-technical systems, as well as in sustainably mainstreaming the knowledge co-produced in policies and plans. DA - 2021-01-22 DB - OpenUCT DP - University of Cape Town IS - 1 J1 - Systems LK - https://open.uct.ac.za PY - 2021 T1 - Application of Systems-Approach in Modelling Complex City-Scale Transdisciplinary Knowledge Co-Production Process and Learning Patterns for Climate Resilience TI - Application of Systems-Approach in Modelling Complex City-Scale Transdisciplinary Knowledge Co-Production Process and Learning Patterns for Climate Resilience UR - http://hdl.handle.net/11427/35260 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/35260
dc.identifier.vancouvercitationMkandawire B, Thole B, Mamiwa D, Mlowa T, McClure A, Kavonic J, et al. Application of Systems-Approach in Modelling Complex City-Scale Transdisciplinary Knowledge Co-Production Process and Learning Patterns for Climate Resilience. Systems. 2021;9(1):7. http://hdl.handle.net/11427/35260.en_ZA
dc.language.isoenen_US
dc.publisher.departmentDepartment of Environmental and Geographical Scienceen_US
dc.publisher.facultyFaculty of Scienceen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourceSystemsen_US
dc.source.journalissue1en_US
dc.source.journalvolume9en_US
dc.source.pagination7en_US
dc.source.urihttps://www.mdpi.com/journal/systems
dc.titleApplication of Systems-Approach in Modelling Complex City-Scale Transdisciplinary Knowledge Co-Production Process and Learning Patterns for Climate Resilienceen_US
dc.typeJournal Articleen_US
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