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Michael Klafft, & Ulrich Meissen. (2011). Assessing the economic value of early warning systems. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: As of today, investments into early warning systems are, to a large extent, politically motivated and “disaster-driven”. This means that investments tend to increase significantly if a disaster strikes, but are often quickly reduced in the following disaster-free years. Such investment patterns make the continuous operation, maintenance and development of the early warning infrastructure a challenging task and may lead to sub-optimal investment decisions. The paper presented here proposes an economic assessment model for the tangible economic impact of early warning systems. The model places a focus on the false alert problematic and goes beyond previous approaches by incorporating some socio-cultural factors (qualitatively estimated as of now). By doing so, it supports policymakers (but also private investors) in their investment decisions related to early warning applications.
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Simone Wurster, & Ulrich Meissen. (2014). Towards an economic assessment approach for early warning systems: Improving cost-avoidance calculations with regard to private households. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 439–443). University Park, PA: The Pennsylvania State University.
Abstract: In recent years, Early Warning Systems (EWS) have proven their value by saving many lives. However, most in-vestments into EWS were motivated directly by experienced disaster events and rarely pro-actively by possible up-coming threats. In order to change that we think that besides ethical and humanitarian reasons also the positive economic effects should be analyzed. EWS also help to protect property, but their contribution is not as obvious in that field due to the lack of quantitative models. This paper presents a disaster-independent formula that shows the benefits of EWS. Additional value to existing approaches is based on its advanced focus on behavioral aspects and the benefits of EWS in comparison to warnings issued via social media. We consider this work as an important contribution for future investments into warning technologies. However, yet this model just provides a theoretical framework for necessary empirical studies that are subject of further research.
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