Using a Coupled Agent-Based Modeling Approach to Analyze the Role of Risk Perception in Water Management Decisions

TitleUsing a Coupled Agent-Based Modeling Approach to Analyze the Role of Risk Perception in Water Management Decisions
Publication TypeJournal Article
Year of Publication2019
AuthorsHyun, Jin-Young, Huang Shih-Yu, Yang Yi-Chen Ethan, Tidwell Vincent, and Macknick Jordan
JournalHydrology and Earth System Sciences
Volume23
Number5
Pages2261-2278
Date Published05/2019
Abstract / Summary

Managing water resources in a complex adaptive natural–human system is a challenge due to the difficulty of modeling human behavior under uncertain risk perception. The interaction between human-engineered systems and natural processes needs to be modeled explicitly with an approach that can quantify the influence of incomplete/ambiguous information on decision-making processes. In this study, we two-way coupled an agent-based model (ABM) with a river-routing and reservoir management model (RiverWare) to address this challenge. 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 psychological thinking process to be specified by a cognitive map between decisions and relevant preceding factors that could affect decision-making. A risk perception parameter is used in the BI mapping to represent 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 do not take risk perception into account. Future studies will focus on modifying the BI mapping to consider direct agents' interactions, up-front cost of agent's decision, and upscaling the watershed ABM to the regional scale.

URLhttp://doi.org/10.5194/hess-23-2261-2019
DOI10.5194/hess-23-2261-2019
Funding Program: 
Journal: Hydrology and Earth System Sciences
Year of Publication: 2019
Volume: 23
Number: 5
Pages: 2261-2278
Date Published: 05/2019

Managing water resources in a complex adaptive natural–human system is a challenge due to the difficulty of modeling human behavior under uncertain risk perception. The interaction between human-engineered systems and natural processes needs to be modeled explicitly with an approach that can quantify the influence of incomplete/ambiguous information on decision-making processes. In this study, we two-way coupled an agent-based model (ABM) with a river-routing and reservoir management model (RiverWare) to address this challenge. 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 psychological thinking process to be specified by a cognitive map between decisions and relevant preceding factors that could affect decision-making. A risk perception parameter is used in the BI mapping to represent 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 do not take risk perception into account. Future studies will focus on modifying the BI mapping to consider direct agents' interactions, up-front cost of agent's decision, and upscaling the watershed ABM to the regional scale.

DOI: 10.5194/hess-23-2261-2019
Citation:
Hyun, J, S Huang, Y Yang, V Tidwell, and J Macknick.  2019.  "Using a Coupled Agent-Based Modeling Approach to Analyze the Role of Risk Perception in Water Management Decisions."  Hydrology and Earth System Sciences 23(5): 2261-2278.  https://doi.org/10.5194/hess-23-2261-2019.