To improve the capacity of existing policy networks, we need approaches to assess current performance, identify priority areas, and measure the effectiveness of interventions. We conduct conceptual research to provide frameworks for practical progress and methods for theoretical progress. To promote empirical and hypothesis-driven policy process studies, we initiate research collaborations that build bridges between two distant fields: computational modeling and policy process studies.
White paper: Policymaking for the Long-term Future: Improving Institutional Fit.
To effectively contribute to the resilience and advancement of civilization, improvements have to be made on four dimensions: spatial, temporal, functional, and representational.
A preliminary assessment suggests that the long-term impact of current policymaking institutions is, despite noteworthy contemporary achievements, limited by several factors: fragile and relatively underdeveloped means of global coordination; a lack of preparedness to anticipate, prevent or recover from potential global catastrophes; siloed structures incapable of coping with cross-cutting challenges; pervasive short-termism leading to negligence of future generations; and underdeveloped capacities for policy learning.
Building on these observations, we suggest three avenues for improving long-term institutional fit: representing future generations; embedding into policy agendas the prevention of global catastrophic risks, as well as the recovery and learning from inevitable shocks; and shifting popular narratives to focus on the creation of transgenerational global public goods and adaptive capabilities.
To boost institutional changes, we propose five improvements: fostering moral reflection; training systems thinking; improving the science-policy interface; training decision-making under uncertainty; and facilitating group deliberation.
Throughout the white paper, we adapt existing theoretical frameworks from systems, political, and decision science and synthesize relevant evidence. We aim to inspire future scholarship and equip policy practitioners with an overview of how to transform policymaking for the long term.
We are now preparing an academic publication to formally introduce the concept into the literature.
A Computational Turn in Policy Process Studies: Co-evolving Network Dynamics of Policy Change
Building on a critical review of the application of complexity theory to policy process studies, we present and implement a baseline model of policy processes using the logic of co-evolving networks. Our model suggests that an actors’ influence depends on their environment and on exogenous events facilitating dialogue and consensus-building. Our results validate previous opinion dynamics models and generate novel patterns. Our discussion provides ground for further research and outlines the path for the field to achieve a computational turn.
White paper: Advancing the field of computational policy process studies
While policy process theory has converged on the view that policymaking can be studied as a complex system, the literature has only minimally used the methodological complement to the theory - computational modelling. Implementations are rare, mainly pushed by computer scientists in trans-disciplinary work and often so detached from mainstream theory that they form a separate line of research instead of testing theories from the social sciences.
This paper builds on the theory of policy processes and computational sciences to advance the computational turn of policy process studies. We examine how and why complexity science lends itself to study policymaking, propose a workflow to guide the creation of computational policy process models, describe the contours of a computational approach to policy process modeling and define goals for future research that follow from this computational turn.