Develop a coupled agent-based modeling approach for uncertain water management decisions

Abstract

Abstract. Managing water resources in a complex adaptive natural-human system is subject to a challenging task due to the difficulty of modeling human behavior and decision uncertainty. The interaction between human-engineered systems and natural processes needs to be modeled explicitly, and a formal approach is required to characterize human decision-making processes and quantify the associated uncertainty caused by incomplete/ambiguous information. In this study, we two-way coupled an agent-based model (ABM) with a river-routing and reservoir management model (RiverWare) while ABM uses a bottom-up approach that allows individual decision makers to be defined as agents – each able to make their own decisions based on their objectives and confidence in the acquired information. The human decision-making processes is described in the ABM using Bayesian Inference (BI) mapping joined with a Cost-Loss (CL) model (BC-ABM). Incorporating BI mapping into an ABM allows an agent's internal (psychological) thinking process to be specified by a cognitive map between decisions and relevant preceding factors that could affect decision-making. The associated decision uncertainty is characterized by a risk perception parameter in the BI mapping representing an agent's belief on the preceding factors. Integration of the CL model addresses an agent's behavior caused by changing socioeconomic conditions. We use the San Juan River Basin in New Mexico, USA to demonstrate the utility of this method. The calibrated BC-ABM-RiverWare model is shown to capture the dynamics of historical irrigated area and streamflow changes. The results suggest that the proposed BC-ABM framework provides an improved representation of human decision-making processes compared to conventional rule-based ABMs that does not take uncertainties into account. Future studies will focus on modifying the BI mapping to consider direct agents' interactions, up-front cost, joint human and natural uncertainty evaluation, and upscaling the watershed ABM to the regional scale.