Considering risk perception can improve the representation of human decision-making processes in agent-based models.
Modeling water resource management is a challenge because of the interactions between human decisions, the natural hydrologic cycle, and the impact of risk perception on human decision making. A study by scientists at Lehigh University, Sandia National Laboratories, and the National Renewable Energy Laboratory (NREL) showed that risk perception can be addressed via the Theory of Planned Behavior, thus improving model representations of how people make water management decisions.
This approach improves on rule-based risk decision making by considering how previous experiences and new information play a role in the decision-making process. Analysis of how farmers manage annual irrigation acreage demonstrates the dynamic nature of decision making, something that is essential to represent in future research regarding evolving natural factors. The approach also allows for a more flexible representation of real-world decision making that can be further expanded to various spatial scales in the future. Results showed that farm location upstream or downstream of a reservoir will affect farmers’ risk perception regarding water availability and influence their behavior about expanding irrigation areas.
Researchers “two-way” coupled an agent-based model (ABM) with a river-routing and reservoir management model (RiverWare) to address the interaction between human-engineered systems and natural processes while quantifying the influence of incomplete/ambiguous information on decision-making processes. The ABM combines Bayesian Inference mapping with a Cost-Loss model to simulate farmers’ psychological risk-based decision processes under evolving socioeconomic conditions. The San Juan River Basin in New Mexico is used to demonstrate the utility of this method. The calibrated model captures the annual variations of historical irrigated areas. The results suggest that the new approach provides an improved representation of human decision-making processes and outperforms the conventional rule-based ABMs that do not consider risk perception. Future studies will focus on modifying the Bayesian Inference mapping to consider farmer interactions, the up-front costs of farmer decisions, and upscaling this method to the regional scale.
Contacts (BER PM)
U.S. Department of Energy Office of Science, Office of Biological and Environmental Research
Climate and Environmental Sciences Division (SC-23.1)
Ian Kraucunas, Ph.D.
Pacific Northwest National Laboratory
Pacific Northwest National Laboratory
This research was supported by the U.S. Department of Energy, Office of Science, as part of research in Multisector Dynamics, Earth and Environmental System Modeling program.
Hyun, J. Y., S. Y. Huang, Y. C. E. Yang, V. Tidwell, and J. Macknick. “Using a coupled agent-based modeling approach to analyze the role of risk perception in water management decisions.” Hydrology and Earth System Science 23, 2261–2278 (2019). [DOI: 10.5194/hess-23-2261-2019]
SC-23.1 Climate and Environmental Sciences Division, BER
BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER
Aug 24, 2019
New Approach for Studying How Microbes Influence Their Environment
A diverse group of scientists suggests a common framework and targeting of known microbial processes [more...]
Aug 08, 2019
Nutrient-Hungry Peatland Microbes Reduce Carbon Loss Under Warmer Conditions
Enzyme production in peatlands reduces carbon lost to respiration under future high temperatures. [more...]
Aug 05, 2019
Amazon Forest Response to CO2 Fertilization Dependent on Plant Phosphorus Acquisition
AmazonFACE Model Intercomparison. The Science Plant growth is dependent on the availabi [more...]
Jul 29, 2019
A Slippery Slope: Soil Carbon Destabilization
Carbon gain or loss depends on the balance between competing biological, chemical, and physical reac [more...]
Jul 15, 2019
Field Evaluation of Gas Analyzers for Measuring Ecosystem Fluxes
How gas analyzer type and correction method impact measured fluxes. The Science A side- [more...]
List all highlights (possible long download time)