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Abstract:
Interactions among and between human and non-human agents across scales are central to social-ecological systems (SES) and their dynamics. Among the emergent processes vital to navigating change, social learning, especially across cultural and onto-epistemological perspectives, has gained traction for building adaptive capacity, fostering collaboration, and enabling transformative governance. Yet, many learning theories in SES research offer limited insight into the fine-grained, embodied, and relational dynamics through which learning unfolds.
This paper bridges Pahl-Wostl’s social learning framework with the predictive processing (PP) paradigm from cognitive science to illuminate micro-level mechanisms of perception, action, and learning in SES. As we show, PP offers a biologically grounded, process-based account of how internal models are formed and revised through recursive loops of perception and (inter-)action with complex environments.
Acknowledging that theorizing learning in SES requires recognizing the inseparable entanglement of the social and the ecological, we introduce the concept of social-ecological learning. This lens highlights how human–human and human–nature relations co-shape what and how agents learn, emphasizing that social learning in SES is always ecologically situated, and vice versa.
Finally, we integrate PP’s distinctions between parametric and structure learning with loop learning theory to offer a novel entry point for examining learning across scales—from incremental updates to deep shifts in assumptions and worldviews, and from individual sense-making to broader societal change.
Our framework bridges theoretical silos and contributes to sustainability science by advancing a relational, embodied, and embedded understanding of learning in SES–essential for fostering transformative capacity in an uncertain, rapidly changing world.
