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Title: Using a coupled agent-based modeling approach to analyze the role of risk perception in water management decisions

Abstract

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 providesmore » 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.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2];  [3]
  1. Lehigh Univ., Bethlehem, PA (United States). Dept. of Civil and Environmental Engineering
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  3. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1524329
Report Number(s):
NREL/JA-6A20-74032
Journal ID: ISSN 1607-7938
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Hydrology and Earth System Sciences (Online)
Additional Journal Information:
Journal Name: Hydrology and Earth System Sciences (Online); Journal Volume: 23; Journal Issue: 5; Journal ID: ISSN 1607-7938
Publisher:
European Geosciences Union (EGU)
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; 42 ENGINEERING; 58 GEOSCIENCES; water resources; agent-based model; RiverWare

Citation Formats

Hyun, Jin-Young, Huang, Shih-Yu, Yang, Yi-Chen Ethan, Tidwell, Vincent, and Macknick, Jordan. Using a coupled agent-based modeling approach to analyze the role of risk perception in water management decisions. United States: N. p., 2019. Web. doi:10.5194/hess-23-2261-2019.
Hyun, Jin-Young, Huang, Shih-Yu, Yang, Yi-Chen Ethan, Tidwell, Vincent, & Macknick, Jordan. Using a coupled agent-based modeling approach to analyze the role of risk perception in water management decisions. United States. doi:10.5194/hess-23-2261-2019.
Hyun, Jin-Young, Huang, Shih-Yu, Yang, Yi-Chen Ethan, Tidwell, Vincent, and Macknick, Jordan. Fri . "Using a coupled agent-based modeling approach to analyze the role of risk perception in water management decisions". United States. doi:10.5194/hess-23-2261-2019. https://www.osti.gov/servlets/purl/1524329.
@article{osti_1524329,
title = {Using a coupled agent-based modeling approach to analyze the role of risk perception in water management decisions},
author = {Hyun, Jin-Young and Huang, Shih-Yu and Yang, Yi-Chen Ethan and Tidwell, Vincent and Macknick, Jordan},
abstractNote = {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},
journal = {Hydrology and Earth System Sciences (Online)},
number = 5,
volume = 23,
place = {United States},
year = {2019},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Figures / Tables:

Figure 1 Figure 1: The flow chart of agent decision-making process inside the two-way coupled ABM–RiverWare model (ABM.exe in Fig. S1). Agents make their decisions with uncertainty based on the method developed in this paper (joint BI mapping and CL model), and RiverWare runs the simulation based on these decisions.

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