Full Session with Abstracts
In recent years, natural hazards with high consequences have made governments and local policy makers increasingly aware of the need for resilient communities. For instance, Hurricane Katrina resulted in more than 1,800 fatalities and affected 90,000 square miles of the United States, and as a post-disaster recovery effort, the federal government spent $105 billion on repair and reconstruction of civil infrastructure facilities in the communities. Due to such excessive burden resulting from human and economic losses following natural hazards, the federal government and local policy makers have paid an increasing attention to disaster risk management in order to improve community resilience.
Global climate change and increasing structural vulnerability, coupled with population growth and urbanization in potentially hazardous areas, pose even more serious and increasing risks to communities. In addition to the combined dynamics of hazard, vulnerability, and exposure, communities often are subjected to shifts in social expectations, unpredictable political circumstances, restricted budgets, and technological advances. Uncertainties arising from multiple evolving conditions may raise additional concerns about risk management problems.
Catastrophe risk insurance is an important tool to mitigate disaster risks and to expedite recovery of disaster-impacted buildings and the communities that they support, but its quantitative impact on community resilience has yet to be explored in any depth. Multiple evolving conditions further complicate quantitative assessment of the role of catastrophe risk insurance in community resilience: due to additional uncertainties introduced by evolving conditions, insurers tend to estimate significantly higher risk premiums than the expected losses. Moreover, increasing risks may lead to distorted risk perception and the associated purchasing behavior of individual homeowners.
This paper proposes a quantitative assessment of the role of catastrophe risk insurance in community resilience when communities are subjected to multiple evolving conditions. First, the possible situational dynamics of communities are explored. Based on empirical evidence, we then investigate their effects on risk insurance premium, spatial distribution of insured properties, risk perception and behavior in purchasing insurance in the expected utility framework. Agent-Based Model is utilized to explicitly model such spatiotemporal characterization of homeowners’ behavioral biases and purchasing actions and assess their impact on post-disaster recovery of a community. The proposed framework is used to simulate insured property distributions and recovery of Miami-Dade County, Florida, subjected to multiple evolving conditions to test the feasibility and practicability of the framework.