Search

Dynamic modeling of public and private decision-making for hurricane risk management including insurance, acquisition, and mitigation policy

<?xml version="1.0" encoding="UTF-8"?><modsCollection xmlns="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-8.xsd">
<mods version="3.8">
<titleInfo>
<title>Dynamic modeling of public and private decision-making for hurricane risk management including insurance, acquisition, and mitigation policy</title>
</titleInfo>
<typeOfResource>text</typeOfResource>
<genre authority="marcgt">periodical</genre>
<originInfo>
<place>
<placeTerm type="code" authority="marccountry">esp</placeTerm>
</place>
<dateIssued encoding="marc">2022</dateIssued>
<issuance>serial</issuance>
</originInfo>
<language>
<languageTerm type="code" authority="iso639-2b">spa</languageTerm>
</language>
<physicalDescription>
<form authority="marcform">print</form>
</physicalDescription>
<abstract displayLabel="Summary">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.

</abstract>
<note type="statement of responsibility">Cen Guo...[et.al.]</note>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080591182">
<topic>Gerencia de riesgos</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080551254">
<topic>Huracanes</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080588434">
<topic>Toma de decisiones</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080586447">
<topic>Modelo estocástico</topic>
</subject>
<classification authority="">7</classification>
<relatedItem type="host">
<titleInfo>
<title>Risk management & insurance review</title>
</titleInfo>
<originInfo>
<publisher>Malden, MA : The American Risk and Insurance Association by Blackwell Publishing, 1999-</publisher>
</originInfo>
<identifier type="issn">1098-1616</identifier>
<identifier type="local">MAP20077001748</identifier>
<part>
<text>06/06/2022 Tomo 25 Número 2 - 2022 , p. 173-199</text>
</part>
</relatedItem>
<recordInfo>
<recordContentSource authority="marcorg">MAP</recordContentSource>
<recordCreationDate encoding="marc">220624</recordCreationDate>
<recordChangeDate encoding="iso8601">20220624132109.0</recordChangeDate>
<recordIdentifier source="MAP">MAP20220018609</recordIdentifier>
<languageOfCataloging>
<languageTerm type="code" authority="iso639-2b">spa</languageTerm>
</languageOfCataloging>
</recordInfo>
</mods>
</modsCollection>