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A Decision-Support Tool for Coastal Community Resilience: Future Impacts from Sea Level Rise and Self-Learning Agents

Published

Author(s)

Dylan Sanderson, Therese McAllister, Jennifer Helgeson, Rithika Dulam

Abstract

Chronic hazards associated with a changing climate may necessitate difficult adaptation decisions in many coastal communities. Computational tools that account for uncertainties in future climate conditions, impacts, and human responses are needed to assist in decision-making processes towards community resilience. This presentation describes a novel decision-support tool for simulating household and business responses to sea-level rise. The model considers sea-level rise and its local impacts on building exposure, electric power outages, and increases in travel times. Households and businesses are represented as agents with bounded rationality who respond to these impacts by taking risk mitigation actions that include relocating, elevating, installing solar panels, or doing nothing. Households and businesses evaluate their options by considering the following information, both at their building and in their neighborhood, on an annual basis: (1) days per year exposed, (2) days per year without electricity, (3) days per year with increases in travel time, and (4) neighborhood migration. Businesses additionally consider available workforce, customers, and costs. Reinforcement learning is used to simulate agent learning and the decision-making process. The use of this decision-support tool is demonstrated using Galveston, Texas, USA, as a testbed community, and interactions between households and businesses can be studied. The primary equations and theoretical framework behind the agent decision-making process are simple to understand, thus making this framework useful for cross-disciplinary collaborations and community engagement.

Citation

Sanderson, D. , McAllister, T. , Helgeson, J. and Dulam, R. (2025), A Decision-Support Tool for Coastal Community Resilience: Future Impacts from Sea Level Rise and Self-Learning Agents, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=959174 (Accessed April 3, 2025)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created April 1, 2025