Design of Flood-loss Sharing Programs in the Upper Tisza Region, Hungary: A dynamic multi-agent adaptive Monte Carlo approach

Ermolieva, T., Ermoliev, Y., Linnerooth-Bayer, J., Vari, A., & Amendola, A. (2002). Design of Flood-loss Sharing Programs in the Upper Tisza Region, Hungary: A dynamic multi-agent adaptive Monte Carlo approach. In: Proceedings of the Second Annual IIASA-DPRI Meeting "Integrated Disaster Risk Management: Megacity Vulnerability and Resilience", 29-31 July 2002.

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Abstract

Losses from human-made and natural catastrophes are rapidly increasing. The main reason for this is the clustering of people and capital in hazard-prone areas as well as the creation of new hazard-prone areas, a phenomenon that may be aggravated by a lack of knowledge of the risks. This alarming human-induced tendency calls for new integrated approaches to catastrophic risk management. This paper demonstrates how flood catastrophe model and adaptive Monte Carlo optimization can be linked into an integrated Catastrophe Management Model to give insights on the feasibility of a flood management program and to assist in designing a robust program. As a part of integrated flood risk management, the proposed model takes into account the specifics of the catastrophic risk management: highly mutually dependent losses, the lack of information, the need for long-term perspectives and geographically explicit models, the involvement of various agents such as individuals, governments, insurers, reinsurers, and investors. Therefore, the integrated catastrophe management model turns out to be an important mitigation measure in comprehending catastrophes. As a concrete case we consider a pilot region of the Upper Tisza river, Hungary. Specifically, we analyze the demand of the region in a multipillar flood-loss sharing program involving a partial compensation by the central government, a voluntary private property insurance, a voluntary private risk-based insurance GIS-based catastrophe models and specific stochastic optimization methods are used to guide policy analysis with respect to location-specific risk exposures. To analyze the stability of the program, we use economically sound risk indicators.

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