Abstract
Key lessons about and limits to social-ecological systems (SES) modeling are widely available and frustratingly consistent over time. Prominent challenges include outdated perspectives about systems and models along with persistent disciplinary hegemony. The inherent complexity in SES means that an emphasis on discrete prediction is misplaced and has potentially reduced model efficacy for decision-making. Although computer models are definitely the tool to use to identify the complex relationships within SES, humans are messy and hence the ‘social’ in SES is often ignored, glossed over, or reduced to simplistic economic or demographic variables. This combination of factors has perpetuated biases in what is worth pursuing and/or publishing.
In (re)visiting issues in SES modeling, including debates about model capabilities, data selection, and challenges in working across disciplinary lines, this reflection explores how the author’s experience aligns with extant literature as well as raises issues about what is absent from that body of work. The available lessons suggest that scholars and practitioners need to re-think how, why, and when to employ SES modeling in regulatory or other decision-making contexts.
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