The representation of human decision making in physically-focused energy, water, and land (EWL) models is often highly abstracted and simplified. For example, many EWL management models often assume human resource demands that are fixed relative to exogenous drivers such as population or technology input and that are uniform across demographic characteristics (e.g., income, gender, education). Additionally, physically-focused EWL models tend to largely ignore or abstract institutional dynamics in resource systems (e.g., centralized, polycentric or uncoordinated institutional regimes), commonly adopting fixed allocation rules that implicitly presume centralized, non-adaptive management of resources and treating infrastructure expansion in exogenous fashion. Such treatment ignores the fact that resource availability, allocation, and use stem from a web of interactions between users, groups, and governing bodies with different incentives that adapt to changing environmental and socioeconomic conditions, with the complexity of this web being especially pronounced in multi-sector systems.
To address these gaps in EWL modeling, our working group explores state of the art modeling methods that can improve representation of human decision making and adaptation in the MSD context. We investigate 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. The goal of our working group is to determine how these various modeling methods can be effectively integrated with physically-focused EWL models, enhancing representation of human decision making in EWL models for improved understanding of multi-sector system evolution and vulnerability.