Originally published in our April 2020 newsletter (Issue 3)
We are excited to announce our inaugural MSD working group on the representation of human systems in MSD models. Our group is working to address what we consider a critical gap in MSD research: the limited representation of dynamic, adaptive human decision making and action in response to changing environmental and socioeconomic conditions in MSD models. For example, many MSD models often simplistically assume human resource demands that are fixed relative to exogenous drivers such as population or technology input and that are uniform across demographic characteristics. Additionally, MSD models tend to largely ignore or highly abstract institutional dynamics in resource systems, commonly adopting fixed allocation rules that implicitly presume centralized, non-adaptive management of resources and infrastructure.
These critical gaps translate into a set of inter-related science questions that drive our discussions: How does individual human adaptation, which is embedded in broader institutional and societal contexts, influence the co-evolution of coupled human-natural systems? How can human actions, defined broadly from short-term consumer adaptation to long-term societal transitions, be effectively represented in multi-sector models? How should the representation of these human decisions be tailored for the specific use-case of the model (e.g., whether the model is intended for prescriptive versus descriptive purposes)?
How sensitive are our modeling outcomes and diagnoses of vulnerability to the representation of these human decisions? To address these science questions, our working group is exploring state of the art modeling methods that can improve representation of human decision making and adaptation in the MSD context, drawing from advances across disciplines. We are investigating a range of modeling techniques, including agent-based, bioeconomic, equilibrium, computable general equilibrium, game-theory, dynamic spatial simulation (e.g., network and cognitive mapping), stochastic optimization / dynamic programming, and cellular automata approaches towards simulating human decisions in multi-sector systems.
In 2020, our primary working group activity centers around a monthly interactive webinar, in which we are working towards the development of a white paper setting forth a new typology for characterizing coupled human-natural system (CHNS) applied in the MSD arena, highlighting the human decision making aspects of the models and the interface of those decisions with the physical system. The goal of the typology is to facilitate systematic discussion around the use of CHNS models in MSD, elucidating the unique capability of and complementary insights gained from different modeling approaches and identifying opportunities for enhancing the state of knowledge in MSD through diverse yet concerted CHNS model development and application.
As part of the webinar series, we welcome members of the MSD community to present their own research on CHNS models, particularly sharing their work in the context of an initial working typology. Our goal will be to use these discussions to iterate and improve upon the design of the typology itself as we trial its utility in describing a wide range of human systems modeling approaches adopted in MSD research. If you’re interested in presenting your work at a webinar or participating in group discussions, please contact us via the MSD website or e-mail one of us directly. We also encourage you to stay on the lookout for the MSD Human Systems Modeling session at AGU and welcome your submissions once announced. We look forward to engaging with you all as we explore an exciting research frontier in human systems modeling in MSD!
Jim Yoon (jim.yoon@pnnl.gov) is a scientist at Pacific Northwest National Laboratory in the Hydrology group. His research focuses on the development and application of advanced modeling techniques to simulate coupled human-natural systems, with a particular focus on complex water and coastal system dynamics under both short and long-term change.
Nathan Urban (nurban@lanl.gov) is a staff scientist at Los Alamos National Laboratory in the Computational Physics and Methods group. His research interests include coastal natural-human systems modeling, decision making under uncertainty, integrated Earth system predictive uncertainty quantification, information fusion, machine learning, and large-scale stochastic optimization.