Computational Framework to Support Government Policy-Making for Hurricane Risk Management

Abstract

This paper introduces a computational framework that can be used to identify hurricane risk management solutions based on the operation of the system as a whole. The framework represents interactions among multiple types of stakeholders (homeowners, insurers, government, reinsurers) and several strategies (insurance, retrofit, property acquisition). It supports the following government decisions: (1) how much to spend on mitigation; (2) how to regulate the price of extreme event insurance; (3) how to allocate spending between homeowner retrofit grants and property acquisition; and (4) how to design retrofit grant and acquisition programs. The framework includes four interacting mathematical models—stochastic programming optimization models to represent: (1) government; (2) insurer decisions; (3) empirical discrete choice models of individual homeowner decisions; and (4) a regional loss estimation. It includes a description of how insurers and homeowners are predicted to respond to government policies and what the outcomes will be for each. A full-scale application for Eastern North Carolina suggests it is possible to identify system-wide win-win solutions that are better both for stakeholders individually and for society as a whole.