Complex systems and inter/transdisciplinary research: A review

Descalzi, O.; Curilef, S.; Velazquez, L.; Muñoz, V.

doi:10.1063/5.0188971

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Each term represents increasing levels of stakeholder engagement in the research process, moving from participatory research, where stakeholders may contribute insights or feedback (Mackinson et al, 2011;Avilés Irahola et al, 2022), to knowledge co-production (Norström et al, 2020;Cazé et al, 2022;Jidda-Fada and Bennett, 2022), which entails a more equal partnership between researchers and stakeholders, and is culminating in transdisciplinary research. This most integrated form not only bridges academic disciplines, but also deeply involves all stakeholders in the research process from conception to execution (Steger et al, 2021;Franke et al, 2022;Klein, 2022;Archibald et al, 2023;Reed et al, 2023;Descalzi et al, 2024). These distinctions align with Arnstein's "ladder of participation, " highlighting the depth and breadth of engagement critical for addressing complex ocean governance challenges (Arnstein, 1969).…”

Section: Introductionmentioning

confidence: 95%

Co-design capacity development for the UN Ocean Decade

Zhang,

Ferse,

Jennerjahn

et al. 2024

Front. Ocean Sustain.

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Effective ocean governance plays a critical role in managing and coordinating human activities, policies, and institutions to ensure the sustainable use and conservation of marine resources. Recognizing the importance of effective ocean governance, there is a growing awareness that new research approaches are needed to bring together actors from academia and non-academic sectors. Together they need to develop transformative science solutions for a more inclusive and equitable ocean governance system. Despite widespread interest in this collaborative endeavor, limited resources are available to provide structured support and training. This creates a pressing need to develop capacity and promote a shared understanding of collaborative research methodologies, such as co-design. In response to the need for capacity development in such methodologies, the “Co-design for the Ocean Decade” online training course was launched in collaboration with IOC-UNESCO in 2022, starting with a first pilot course in the African region. This first co-design training course under the Ocean Decade framework serves as a practical and research-backed model for future co-design training programs in other regions. The evaluation results of the course reveal its effectiveness in enhancing participants' knowledge and skills in key co-design areas, particularly in communication with stakeholders. The evaluation also highlights areas for improvement, such as the need for (i) monitoring and evaluation, and (ii) follow-up opportunities for practice and funding support. Based on the evaluation, for future research and training programs, we recommend: (1) to incorporate local and regional context into the training, (2) to integrate co-design training into ocean science education, and (3) to embed co-design of research and training into the capacity development program of the Ocean Decade.

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Section: Introductionmentioning

confidence: 95%

Co-design capacity development for the UN Ocean Decade

Zhang,

Ferse,

Jennerjahn

et al. 2024

Front. Ocean Sustain.

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Deep-learning reconstruction of complex dynamical networks from incomplete data

Ding,

Kong,

Zhang

et al. 2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Reconstructing complex networks and predicting the dynamics are particularly challenging in real-world applications because the available information and data are incomplete. We develop a unified collaborative deep-learning framework consisting of three modules: network inference, state estimation, and dynamical learning. The complete network structure is first inferred and the states of the unobserved nodes are estimated, based on which the dynamical learning module is activated to determine the dynamical evolution rules. An alternating parameter updating strategy is deployed to improve the inference and prediction accuracy. Our framework outperforms baseline methods for synthetic and empirical networks hosting a variety of dynamical processes. A reciprocity emerges between network inference and dynamical prediction: better inference of network structure improves the accuracy of dynamical prediction, and vice versa. We demonstrate the superior performance of our framework on an influenza dataset consisting of 37 US States and a PM2.5 dataset covering 184 cities in China.

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Chaotic characteristic analysis and prediction of bottleneck-delay time series under the Internet macro-topology

Tian,

Guo

2024

Eur. Phys. J. Plus

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Complex systems and inter/transdisciplinary research: A review

Cited by 3 publications

References 27 publications

Co-design capacity development for the UN Ocean Decade

Co-design capacity development for the UN Ocean Decade

Deep-learning reconstruction of complex dynamical networks from incomplete data

Chaotic characteristic analysis and prediction of bottleneck-delay time series under the Internet macro-topology

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