Global Challenges, Governance, and Complexity
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Global Challenges, Governance, and Complexity

Applications and Frontiers

Edited by Victor Galaz

There is an increased interest in integrating insights from the complexity sciences to studies of governance and policy. While the issue has been debated, and the term of ‘complexity’ has multiple and sometimes contested interpretations, it is also clear the field has spurred a number of interesting theoretical and empirical efforts. The book includes key thinkers in the field, elaborates on different analytical approaches in studying governance, institutions and policy in the face of complexity, and showcases empirical applications and insights.
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Chapter 13: Cognitive capacities for global governance in the face of complexity: the case of climate tipping points

Manjana Milkoreit

Abstract

Addressing today’s increasingly complex global governance problems requires that decision-makers design appropriate institutional and policy responses to various complex-system characteristics. This requirement brings into focus decision-makers’ cognitive processes: What is their understanding of the dynamics of coupled human-earth systems? (How) Do they learn about complex systems? Can they imagine these systems’ future trajectories, and apply their evolving understanding in specific decision-making contexts in real time? These cognitive processes are essential capacities for effective global governance in the face of complexity. Using the climate change as a case study, and focusing on climate tipping points as an exemplary feature of complexity, I study the presence of three of these cognitive capacities among international negotiators: complex-systems thinking, future-thinking, and application to the current context of the Paris Agreement. I find that these capacities currently exist in a limited fashion, which places constraints on the potential effectiveness of the climate regime. I discuss how these cognitive capacities can be developed and fostered, loosening some of the existing constraints.

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