We apply an interdisciplinary approach that combines participatory processes, complexity science, and computational modelling to support policymakers in dealing with complex challenges.

To face our current and future challenges, we need to understand how to navigate a complex world, including how to detect early warning signals, how to increase resilience, and how to (responsibly and effectively) intervene in complex adaptive systems. The POLDER methodology provides policymakers with insights to navigate increasingly complex landscapes and make better-informed decisions, reducing uncertainty and increasing the impact of policy interventions.

POLDER makes fundamental knowledge and research methodologies from Complexity science applicable to real-world problems. Complexity science studies all kinds of coupled processes, in which feedback mechanisms lead to nonlinear systemic responses. It brings together knowledge from different disciplines (e.g., natural and engineering sciences, statistics, economics, behavioural and social sciences, law, ethics, public administration) and then uses mathematical and computational methods to describe how systems organize themselves. I.e., how small disturbances can multiply rapidly, and how interventions can lead to completely unintended effects, cascades, tipping points and even catastrophic collapse. By mapping the dynamics of the underlying intertwined processes, and then using simulation methods to investigate various what-if scenarios, new avenues of policy making can be explored.

Participatory model building

Participatory model building is the cornerstone of our methodological approach. It involves engaging policymakers, experts, and community members in the co-creation of models that reflect the complexity of real-world problems. We apply a participatory process to bring additional data and validation to our models. Moreover, this approach ensures that multiple perspectives are confronted and included, thereby enhancing the efficacy and acceptability of policy recommendations. The process can include, but is not limited to, group-model-building sessions, one-on-one interviews, surveys, reflective individual feedback, Delphi methods, and focus groups.

Complexity Theory

At the core of POLDER’s methodology is complexity theory, which studies how simple interactions among components can lead to complex, emergent behaviors in systems. In complexity theory, we delve into the properties of non-linear dynamics, feedback loops, and adaptability. We focus particularly on emergent properties that are not evident when examining individual components but become apparent when looking at the system as a whole. This theoretical framework enables us to model unforeseen consequences, tipping points, and the resilience of systems.

Computational modelling

We employ computational modelling as a tool to translate complex theories and real-world data into actionable insights. Through algorithmic processes and statistical methods, these models simulate various what-if scenarios, assess potential policy impacts, and allow for dynamic adjustments. The outcome is an interactive landscape that stakeholders can use to test and refine policies before they are enacted.

Simulations & interventions

Simulation is where theory meets application. After constructing our computational models, we simulate various intervention scenarios to understand their potential impacts. These simulated interventions serve as virtual policy ‘test runs’, enabling policymakers to assess outcomes without the risks associated with real-world implementation. Importantly, our simulations are iterative; they can be refined using additional data or insights derived from additional participatory interactions, ensuring that our models and recommendations remain as current and accurate as possible.

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