In the continuously evolving landscape of sustainability science, a transformative approach known as Transdisciplinary Complexity Science for Sustainability (TCSS) is carving a path toward deeper, more actionable understanding of complex systems. Grounded in the recognition that sustainability challenges are inherently multifaceted and dynamic, TCSS integrates rigorous complexity science methodologies with inclusive, collaborative transdisciplinary processes. This fusion aims to foster holistic insights and practical solutions that resonate across stakeholder groups and real-world contexts.
At the heart of TCSS lies a structured yet flexible four-phase process designed to navigate from initial engagement to actionable outcomes. The journey begins with the initiation phase, a critical period that lays the groundwork for success by setting clear expectations about the nature and limitations of complexity science methods. This stage is vital for managing stakeholder perceptions, many of whom may be unfamiliar with computational and systemic modeling approaches. Crucially, this early dialogue helps stakeholders understand what complexity models can realistically achieve, helping to align perspectives and reduce misunderstandings that could hinder collaboration.
In parallel, the initiation phase actively incorporates methodological tools such as network analysis to map stakeholder diversity and power dynamics. By visualizing relational structures and influence asymmetries within stakeholder groups, facilitators can ensure that marginalized voices are not overlooked. This emphasis on inclusivity is pivotal; the early formation of the research coalition shapes model design decisions that are often challenging to revise later. A richly representative stakeholder assemblage thus ensures that crucial system elements and perspectives are integrated from the outset, enhancing both the legitimacy and comprehensiveness of subsequent modeling efforts.
Transitioning from initiation, the problem identification phase is where the collaborative definition and framing of the sustainability challenge occurs. This phase leverages the concept of boundary objects—adaptable artifacts that different stakeholders interpret through their own lenses, yet which serve as focal points for collective engagement. Complexity models themselves frequently serve as such boundary objects, allowing participants to visualize system components and interactions aligned with their values and priorities. This innovative use of modeling fosters shared understanding and narrows divergences arising from varied stakeholder knowledge bases.
Fundamentally, the problem identification phase is not about imposing rigid scientific frameworks but adapting modeling approaches to fit the context and concerns of stakeholders. This adaptive process clarifies system boundaries and invites participants to distinguish between variables that warrant inclusion versus those considered external or peripheral. The deliberate allocation of sufficient time and resources in this phase is essential to enable the participation of diverse groups, ensuring the representativeness and credibility of the modeling exercise. The outcome is a collaboratively constructed problem space that serves as a foundation for the next, more data-driven phases of the TCSS process.
The third phase, knowledge co-production, stands as the dynamic core of TCSS, where iterative cycles of data collection, model development, and stakeholder feedback catalyze co-learning. Here, complexity science methods converge with transdisciplinary engagement to foster continuous refinement of theoretical and practical insights. Established facilitation techniques, such as group model building and companion modeling, guide this mutual shaping of models and understandings. These approaches empower stakeholders not merely as data providers but as active knowledge contributors and critical evaluators of model relevance.
A prime example of this collaborative iteration can be seen in processes where stakeholder-generated causal loop diagrams surface local perceptions of sustainability challenges and system dynamics. Scientific data and expertise enrich these insights, informing quantification and scenario modeling. The back-and-forth between modeling teams and stakeholders ensures the model remains contextually grounded and pragmatically useful. Notably, this phase encourages reflexivity—participants collectively scrutinize assumptions, methodological choices, and value-laden implications, effectively iterating upon previous phases to maintain relevance and inclusivity.
The iterative and reflexive nature of the co-production phase not only improves model accuracy but also fosters empowerment and shared ownership among participants. However, systematic frameworks for evaluating learning processes and co-production effectiveness are still emerging. Addressing this gap could greatly enhance the rigor and reproducibility of transdisciplinary complexity research, ultimately strengthening the link between scientific inquiry and sustainable action.
Finally, the reintegration phase encapsulates the critical step of translating complex insights into actionable knowledge. Here, the multifaceted learning accrued from previous phases converges toward decision-making and intervention design. This phase prioritizes the presentation of model outputs and scenarios in ways that resonate with stakeholders’ values and operational needs, providing a quantitative and qualitative basis for policy and practice. Crucially, reintegration extends beyond information delivery to include assessing perceived learning, empowerment, and readiness to act.
To date, evaluation of outcomes in this phase remains underdeveloped, highlighting the need for new frameworks that assess not only sustainability impacts but also co-learning processes and empowerment effects. Existing transdisciplinary assessment frameworks offer starting points, but incorporating complexity science dimensions will be imperative as TCSS evolves. Enhancing transparency in how transdisciplinary methods inform model design, perhaps through protocols adapted from agent-based modeling like the ODD framework, could further bolster trustworthiness and comparative analysis across studies.
Throughout the entire TCSS process, the interplay of complexity science and transdisciplinary methods offers unprecedented opportunities to engage with sustainability challenges as living, evolving systems. This approach acknowledges that such challenges cannot be solved by isolated disciplinary efforts or static models but require ongoing negotiation among diverse knowledge forms, values, and interests. By foregrounding reflexivity and stakeholder diversity, TCSS seeks to democratize and sophisticate the science-policy interface, fostering solutions that are both scientifically robust and socially legitimate.
Moreover, the structured yet iterative design of TCSS underscores the emergent nature of sustainability knowledge. The process anticipates revisiting earlier phases in response to new insights or shifting stakeholder priorities, reflecting the adaptive complexity of the systems under study. This flexibility is fundamental to preventing oversimplification and sustaining meaningful engagement, ultimately enhancing the capacity to address systemic sustainability challenges with nuance and care.
In sum, TCSS represents a paradigm shift in sustainability science, integrating rigorous complexity modeling with participatory transdisciplinary processes that respect epistemic diversity and power asymmetries. The methodical exploration of system structure and dynamics, coupled with reflexive knowledge co-production, holds promise for generating actionable, context-sensitive solutions that extend beyond academic inquiry to real-world impact.
Emerging research emphasizes the importance of embedding robust evaluation frameworks within TCSS to systematically capture co-learning outcomes and model effectiveness. Such advancements will be instrumental in validating TCSS as a gold standard for sustainability research and practice. Likewise, the continued development of standardized reporting protocols will elevate the transparency and reproducibility of transdisciplinary complexity studies.
As global sustainability challenges grow ever more intricate, employing approaches like TCSS offers a beacon of hope—one grounded in science, enriched by diverse stakeholder insights, and committed to catalyzing meaningful action. The horizon of sustainability research hinges on such innovative, integrative methods to transform understanding into lasting, equitable change.
Subject of Research: Transdisciplinary Complexity Science for Sustainability (TCSS) process and methodology development.
Article Title: Transdisciplinary complexity science: deepening system understanding for sustainability.
Article References:
de Jager, L.A., Bal, M., Baudena, M. et al. Transdisciplinary complexity science: deepening system understanding for sustainability. Humanit Soc Sci Commun 12, 1384 (2025). https://doi.org/10.1057/s41599-025-05548-7
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