A meta‐analysis of SES framework case studies: Identifying dyad and triad archetypes

Abstract

Abstract There is a need to synthesize the vast amount of empirical case study research on social‐ecological systems (SES) to advance theory. Innovative methods are needed to identify patterns of system interactions and outcomes at different levels of abstraction. Many identifiable patterns may only be relevant to small sets of cases, a sector or regional context, and some more broadly. Theory needs to match these levels while still retaining enough details to inform context‐specific governance. Archetype analysis offers concepts and methods for synthesizing and explaining patterns of interactions across cases. At the most basic level, there is a need to identify two and three independent variable groupings (i.e. dyads and triads) as a starting point for archetype identification (i.e. as theoretical building blocks). The causal explanations of dyads and triads are easier to understand than larger models, and once identified, can be used as building blocks to construct or explain larger theoretical models. We analyse the recurrence of independent variable interactions across 71 quantitative SES models generated from qualitative case study research applying Ostrom's SES framework and examine their relationships to specific outcomes (positive or negative, social or ecological). We use hierarchical clustering, principal component analysis and network analysis tools to identify the frequency and recurrence of dyads and triads across models of different sizes and outcome groups. We also measure the novelty of model composition as models get larger. We support our quantitative model findings with illustrative visual and narrative examples in four case study boxes covering deforestation in Indonesia, pollution in the Rhine River, fisheries management in Chile and renewable wind energy management in Belgium. Findings indicate which pairs of two (dyads) and three (triads) variables are most frequently linked to either positive or negative, social or ecological outcomes. We show which pairs account for most of the variation of interactions across all the models (i.e. the optimal suite). Both the most frequent and optimal suite sets are good starting points for assessing how dyads and triads can fulfil the role of explanatory archetype candidates. We further discuss challenges and opportunities for future SES modelling and synthesis research using archetype analysis. Read the free Plain Language Summary for this article on the Journal blog.

Keywords

• archetypes
• cluster analysis
• common‐pool resources
• environmental governance
• governance theory
• social‐ecological systems