An Open Letter in Support of Community Standards for Modeling Coupled Human-Natural Systems

The critical role of computation for understanding the complex social-natural world we inhabit calls for common standards for integrative modeling across earth, environmental, and social sciences.

We inhabit a world in which the dynamics of a globally networked society are inextricably entangled with biophysical forces in a planetary complex system. Humanity has passed beyond being a global keystone species; the feedbacks among human decisions, technology, and nature shape impacts that match, and can even exceed, biophysical forces (1–3). We inhabit an equally revolutionary social environment of nearly eight billion people connected in cross-cutting networks of information, power, economies, occupations, friendships, and kinship—networks that electronic media and mechanized transportation have extended world-wide (4). Although powerful human intuition once gave us understanding of the social and environmental consequences of our actions in the tiny communities in which humanity lived for hundreds of millennia (5), this is no longer the case. Instead of social settings of a few dozen families, we are politically, economically, and digitally connected at unimaginable scales—with the number of unique mobile phone subscriptions equalling the adult planetary population (6). The highly non-linear chains of cause and effect in this telecoupled world put us at risk of unforeseen, and potentially catastrophic, consequences with even the best intended policies (7). 

The size and worldwide extent of the human species, despite calamities of disease, famine, and war, attest to our capacity to manage growing societal systems and the agroecosystems on which they depend. Yet humankind in the 21^st century is confronted with a challenge unprecedented for any organism in Earth’s history: the need to sustainably ‘manage’ a planetary socio-ecological system we have helped to create (8). Our future depends on acknowledging and acting on this responsibility. To help navigate this unique grand challenge, we increasingly employ technologies—as humans have done throughout history—to augment our innate information processing capacities. Computational modeling is an increasingly critical tool to support policy and planning, scenario development, environmental management, resource investment, and security preparedness and now forms a research keystone in a wide array of fields spanning the earth, environmental, and social sciences (9–11). Particularly notable in this regard have been a suite of large and increasingly complex models of biophysical Earth systems and global economic markets in use since the late 20^th century. Although experiencing growing limitations in the face of the ever more complex and rapidly evolving challenges we confront today (12–16), this software has been invaluable for estimating the impact of human activities on the environment, informing policy, and exploring potential solutions for the future (16, 17). 

It is critical, then, that natural, social, and computer scientists work together to design next generation modeling environments that can support more effective policy-making at multiple scales to ensure our children inherit a sustainable planetary environment (18). Many of the prerequisites for such next generation modeling are already recognized. Scientific model code needs to be transparently and openly accessible so it can be used to help solve problems world-wide (19, 20). Transparency helps build trust in this relatively novel research practice, in the broader public sphere as well as within science. Moreover, we must democratize modeling technologies (now confined primarily to the most developed countries) to address sustainability issues that are global in scope. To be useful, models need to be sufficiently documented to be understandable and enable replicable research. Documentation and code access is a form of knowledge sharing that makes these essential tools more useful and encourages scientists to build on each other’s work (21, 22). It also helps better engage more diverse and creative science when a global scientific community can share this knowledge. Next generation modeling needs to support close integration between representation of social, biological, ecological, and physical systems and the feedbacks between them (23–26). Because such feedbacks telecouple local to global processes, future modeling environments also need to represent multi- and cross-scale dynamics (13, 27). New technologies like containerization and web services can facilitate such integration if there are agreed on ontologies and protocols for applying them (28–31).

Within the social context of scientific practice, research is commonly evaluated against community metrics of dissemination through publication or presentation at conferences; sufficiently clear description of procedures and findings, and potential replicability; and the use of widely accepted and vetted protocols. Even the most ground-breaking research results are met with skepticism if not achieved through standards-based scientific practice. This has supported rigorous, evidence-based science. Similar evaluation metrics need to be applied to scientific code (32). Community-wide adoption of standards for open accessibility of model code; documentation of modeling goals and the algorithms used; usability across multiple computing platforms; and potential interoperability for integrative modeling will encourage robust scientific computation. It will also facilitate knowledge scaffolding that accelerates scientific research, catalyzing rapid innovation in modeling. Such innovation in modeling (from concept to coding) is essential for building scientific resilience to address issues of socio-environmental system dynamics we have not yet imagined. Simultaneously, community-wide standards provide a way to award professional recognition to scientists who practice standards-based modeling. The role of computation has become so pervasive, and modeling so important for Earth system science that the scientific community needs to apply the same kinds of standards-based evaluation that has long benefitted other research practice, and similar professional incentives for scientists to employ them. Hence, we urge the modeling science community to work together across disciplinary and political boundaries to establish standards for next generation modeling that we need to support evidence-based policy making for humanity’s future.

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