The 4Cs of Disaster Partnering: Communication, Coordination, Cooperation and Collaboration

Public, nonprofit and private organisations respond to large-scale disasters domestically and overseas. Critics of these assistance efforts, as well as those involved, often cite poor interorganisational partnering as an obstacle to successful disaster response. Observers frequently call for ‘more’ and ‘better’ partnering. We found important qualitative distinctions existed within partnering behaviours. We identified four different types of interorganisational partnering activities often referred to interchangeably: communication, cooperation, coordination and collaboration—the Four Cs. We derived definitions of the Four Cs from the partnering literature. We then tested them in a case study of the response to the 2010 Haiti earthquake. We suggest that the Four Cs are distinct activities, that organisations are typically strong or weak in one or more for various reasons, and that the four terms represent a continuum of increased interorganisational embeddedness in partnering activities.

Computing probable maximum loss in catastrophe reinsurance portfolios on multi-core and many-core architectures

In the reinsurance market, the risks natural catastrophes pose to portfolios of properties must be quantified, so that they can be priced, and insurance offered. The analysis of such risks at a portfolio level requires a simulation of up to 800 000 trials with an average of 1000 catastrophic events per trial. This is sufficient to capture risk for a global multi-peril reinsurance portfolio covering a range of perils including earthquake, hurricane, tornado, hail, severe thunderstorm, wind storm, storm surge and riverine flooding, and wildfire. Such simulations are both computation and data intensive, making the application of high-performance computing techniques desirable.

In this paper, we explore the design and implementation of portfolio risk analysis on both multi-core and many-core computing platforms. Given a portfolio of property catastrophe insurance treaties, key risk measures, such as probable maximum loss, are computed by taking both primary and secondary uncertainties into account. Primary uncertainty is associated with whether or not an event occurs in a simulated year, while secondary uncertainty captures the uncertainty in the level of loss due to the use of simplified physical models and limitations in the available data. A combination of fast lookup structures, multi-threading and careful hand tuning of numerical operations is required to achieve good performance. Experimental results are reported for multi-core processors and systems using NVIDIA graphics processing unit and Intel Phi many-core accelerators.

Development of Seismic Fragility Functions for a Moment Resisting Reinforced Concrete Framed Structure

Understanding the seismic vulnerability of building structures is important for seismic engineers, building owners, risk insurers and governments. Seismic vulnerability defines a buildings predisposition to be damaged as a result of an earthquake of a given severity. There are two components to seismic risk; the seismic hazard and the exposure of the structural inventory to any given earthquake event. This paper demonstrates the development of fragility curves at different damage states using a detailed mechanical model of a moment resisting reinforced concrete structure typical of Southern Europe. The mechanical model consists of a complex three-dimensional finite element model of the reinforced concrete moment resisting frame structure and is used to define the damage states through pushover analysis. Fragility curves are also defined using the HAZUS macroseismic methodology and the Risk-UE macroseismic methodology. Comparison of the mechanically modelled and HAZUS fragility curve shows good agreement while the Risk-UE methodology shows reasonably poor agreement.