Eight grand challenges in socio-environmental systems modeling

TLDR

Socio‑environmental systems modeling is essential for characterizing complex societal and environmental issues, integrating diverse knowledge, and engaging stakeholders to support social learning and decision‑making. The paper aims to identify and formulate grand challenges that must be overcome to accelerate the development and adaptation of SES modeling. The authors delineate eight grand challenges—bridging epistemologies, managing multi‑dimensional uncertainty, addressing scale issues, integrating qualitative and quantitative data, promoting policy impact, capturing structural change, representing human dimensions, and leveraging new data types—and outline basic steps to surmount each. These challenges constrain the effective use of SES modeling for decision support, and the paper highlights priority research areas to advance modeling products, processes, and practices.

Abstract

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.