Testing social network metrics as proxies for governance performance: A simulation-based experiment in watershed management

This study introduces a simulation-based modelling framework to systematically evaluate whether widely used social network analysis (SNA) metrics function as credible proxies for governance performance. I generated 100 synthetic governance networks with covariance structures linking collaboration, equity, resilience, participation, and coordination to structural properties. A suite of analyses, including multiple regression models, permutation tests, partial correlations, and hierarchical clustering, was applied to test the predictive validity of reciprocity, transitivity, Gini degree, k-core, betweenness centrality, clustering coefficient, modularity, and density. Results demonstrate reproducible structural–functional linkages: reciprocity and transitivity robustly predict collaboration, equity is inversely tied to Gini degree, and resilience depends on k-core prominence and betweenness centrality. The modelling workflow, implemented in Python with open scripts and datasets, provides transparent benchmarks for interpreting governance-relevant network metrics. Beyond advancing theory, this framework enhances the diagnostic utility of SNA, supporting more reliable decision-support tools for watershed governance and environmental management. By embedding governance processes into a reproducible, simulation-based workflow, this study extends the reach of ecological informatics beyond biophysical systems to include social structures that shape environmental outcomes. The approach provides transferable benchmarks and open-source resources that strengthen reproducibility, comparability, and integration of governance diagnostics within ecological informatics research.