Dynamic modeling of public and private decision-making for hurricane risk management including insurance, acquisition, and mitigation policy
dc.contributor.author | Guo, Cen | |
dc.contributor.author | Nozick, Linda | |
dc.contributor.author | Kruse, Jamie | |
dc.contributor.author | Millea, Meghan | |
dc.contributor.author | Davidson, Rachel | |
dc.contributor.author | Trainor, Joseph | |
dc.date.accessioned | 2022-08-04T16:28:33Z | |
dc.date.available | 2022-08-04T16:28:33Z | |
dc.date.issued | 2022-06-08 | |
dc.description | This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. | en_US |
dc.description.abstract | We develop a computational framework for the stochastic and dynamic modeling of regional natural catastrophe losses with an insurance industry to support government decision-making for hurricane risk management. The analysis captures the temporal changes in the building inventory due to the acquisition (buyouts) of high-risk properties and the vulnerability of the building stock due to retrofit mitigation decisions. The system is comprised of a set of interacting models to (1) simulate hazard events; (2) estimate regional hurricane-induced losses from each hazard event based on an evolving building inventory; (3) capture acquisition offer acceptance, retrofit implementation, and insurance purchase behaviors of homeowners; and (4) represent an insurance market sensitive to demand with strategically interrelated primary insurers. This framework is linked to a simulation-optimization model to optimize decision-making by a government entity whose objective is to minimize region-wide hurricane losses. We examine the effect of different policies on homeowner mitigation, insurance take-up rate, insurer profit, and solvency in a case study using data for eastern North Carolina. Our findings indicate that an approach that coordinates insurance, retrofits, and acquisition of high-risk properties effectively reduces total (uninsured and insured) losses. | en_US |
dc.description.sponsorship | Wiley Open Access Account | en_US |
dc.identifier.citation | Guo, C., Nozick, L., Kruse, J., Millea, M., Davidson, R., &Trainor, J. (2022). Dynamic modeling of public and private decision‐making forhurricane risk management including insurance, acquisition, and mitigation policy.RiskManagement and Insurance Review, 25, 173–199.https://doi.org/10.1111/rmir.12215GUOET AL.|199 | en_US |
dc.identifier.doi | 10.1111/rmir.12215 | |
dc.identifier.uri | http://hdl.handle.net/10342/10976 | |
dc.language.iso | en | en_US |
dc.relation.uri | https://onlinelibrary.wiley.com/doi/10.1111/rmir.12215 | en_US |
dc.title | Dynamic modeling of public and private decision-making for hurricane risk management including insurance, acquisition, and mitigation policy | en_US |
dc.type | Article | en_US |
ecu.journal.issue | 2 | en_US |
ecu.journal.name | Risk Management and Insurance Review | en_US |
ecu.journal.pages | 173-199 | en_US |
ecu.journal.volume | 25 | en_US |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Risk Manage Insurance Review - 2022 - Guo - Dynamic modeling of public and private decision‐making for hurricane risk.pdf
- Size:
- 4.91 MB
- Format:
- Adobe Portable Document Format
- Description:
- Article