Exploring mechanisms for systemic thinking in decision-making through three country applications of SDG Synergies

Increased systems thinking capacity-that is, the capacity to consider systemic effects of policies and actions-is necessary for translating knowledge on Sustainable Development Goals' (SDGs) interactions into practice. Various models and tools that seek to support more evidence-based policy-making have been developed with the purpose of exploring system effects across SDGs. However, these often lack integration of behavioral aspects and contextual factors that influence the decision-making process. We analyze three applications of a decision-support approach called SDG Synergies, which aims at building capacity in systems thinking among decision-makers and implementing agencies. Our objective is to explore how behavior and context influences whether and how knowledge is taken up and acted upon when making decisions. Drawing on empirical material from Mongolia, Colombia, and Sri Lanka, we identify three sets of mechanisms that appear important for enabling more systemic thinking: system boundaries (time, scale, and space), rules of engagement (ownership, representation, and purpose), and biases (confirmation biases and participation biases). Results highlight some key challenges for systemic thinking that merit further attention in future applications, including the importance of localizing SDGs and incorporating this knowledge to national-level assessments, an unwillingness of stakeholders to acknowledge trade-offs, the challenge of addressing transformational as opposed to incremental change, and striking a balance between the flexibility of the approach vis-a-vis scientific robustness.

Automated summary

This study examines how to enhance the system thinking capacity among decision-makers and implementing agencies to promote the practice of Sustainable Development Goals. Key challenges identified include localizing SDGs, stakeholders' reluctance to acknowledge trade-offs, addressing transformational change, and balancing flexibility with scientific robustness in the approach.