Eight grand challenges in socio-environmental systems modeling

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.

Developing shared qualitative models for complex systems

“…Variables with moderate to high betweenness centrality Table 2: Dominant challenges in modelling complex systems (Elsawah et al 2020) and the corresponding phase of our method that seeks to assist in overcoming them. Figure 1: Overview of the method for creating a shared qualitative model of a complex system, comprising four phases.…”

“…Understanding these perceptions is critical in modelling for at least four main reasons. First, it can reveal areas of knowledge uncertainty, another of the 'grand challenges' of modelling (Elsawah et al 2020). Second, it captures a person's causal knowledge about how a system works, which can assist with quantifying complex systems (Voinov et al 2018).…”

“…"a mode of thinking that people engage in when they are deeply involved in a cohesive in-group, when the members' strivings for unanimity override their motivation to realistically appraise alternative[s], Irving 1972, p. 9) and implicit assumptions about how a system works (Lade et al 2017). Thus, eliciting, illuminating, exploring, negotiating and sharing our processes of knowledge construction is likely to bring us closer to a more accurate model of a complex system, and by extension then, a more reliable quantification of that system (Elsawah et al 2020;Kelly et al 2013;Voinov et al 2018). We need to focus, therefore, not only on the tangible model of a complex system, but also intangible processes of knowledge construction that are used to create that model.…”

Using mixed methods to construct and analyze a participatory agent-based model of a complex Zimbabwean agro-pastoral system

“…[2] Simulation studies are a common strategy for addressing these challenges, facilitating exploration of complex system responses to current conditions as well as their possible responses to changing system drivers in the future. [3,4] Even with these tools, complex social-ecological systems can also be notoriously "wicked" to manage, in the sense that defining the problem to be solved can be elusive and vary from stakeholder to stakeholder, attempted solutions may cause as many problems as they solve, and each attempt has consequences that cannot be ignored. [5] Collaborative research strategies can be key in working with these challenges.…”

“…[5] Collaborative research strategies can be key in working with these challenges. [4] Bringing together simulation modeling strategies with community collaboration has been referred to by many names, but can be broadly termed "participatory modeling." [6] These processes can involve focus groups, role-playing games, workshops, and many other types of community engagement, and can involve community members in some or all parts of the modeling process (including goal setting, data collection, design, implementation, verification and calibration, validation, use, and outcome analysis).…”