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This article deals with household-level flood risk mitigation. We present an agent-based modeling framework to simulate the mechanism of natural hazard and human interactions, to allow evaluation of community flood risk, and to predict various adaptation outcomes. The framework considers each household as an autonomous, yet socially connected, agent. A Beta-Bernoulli Bayesian learning model is first applied to measure changes of agents' risk perceptions in response to stochastic storm surges. Then the risk appraisal behaviors of agents, as a function of willingness-to-pay for flood insurance, are measured. Using Miami-Dade County, Florida as a case study, we simulated four scenarios to evaluate the outcomes of alternative adaptation strategies. Results show that community damage decreases significantly after a few years when agents become cognizant of flood risks. Compared to insurance policies with pre-Flood Insurance Rate Maps subsidies, risk-based insurance policies are more effective in promoting community resilience, but it will decrease motivations to purchase flood insurance, especially for households outside of high-risk areas. We evaluated vital model parameters using a local sensitivity analysis. Simulation results demonstrate the importance of an integrated adaptation strategy in community flood risk management.
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Economics, Insurance, and Flood Hazards
This article introduces the Symposium Economics, Insurance, and Flood Hazards. We provide background on the magnitude of recent extreme weather events and subsequent flooding in terms of lives and property damage. After a brief background of the National Flood Insurance Program and its renewal, we describe the papers contributed to the symposium and their relationship to the challenges described by the U.S. General Accountability Office (GAO).
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- PAR ID:
- 10087710
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Southern Economic Journal
- Volume:
- 85
- Issue:
- 4
- ISSN:
- 0038-4038
- Page Range / eLocation ID:
- p. 1027-1031
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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