Global change and rapid technological growth raise scientifically challenging problems requiring new concepts and approaches. These problems are characterized by inherent endogenous uncertainties and risks, large temporal-spatial scales and heterogeneities, interdependencies and nonlinear interactions that may potentially lead to abrupt changes with irreversible catastrophic impacts.

Traditionally, scientific approaches to uncertainty rely on observations, repetitive experiments and predictions. However, for new problems historical data may not be available and experiments may be extremely costly and dangerous, leading to poor evaluations and predictions.

A key task in these cases is to design robust policies with respect to uncertainties and risks on various temporal and spatial dimensions. In particular, an important task is the development of integrated stochastic models that combine reduced spatial catastrophe generators, multiagent accounting frameworks, risk reducing and risk spreading decisions together with adaptive Monte Carlo optimization. These models allow for the design of robust policies which take into account uncertainties in an explicit and consistent way by using “hard” data from historical observations, the results of possible experiments, model simulations, “soft” expert opinions and perspectives of future learning.

A sample of basic methodological publications:

• Y. Ermoliev, T. Ermolieva, G. MacDonald and V. Norkin, 2000, Stochastic Optimization of Insurance Portfolios for Managing Exposure to Catastrophic risks, Annals of Operations Research, vol. 99, pp. 207-225.
• Y. Ermoliev, T. Ermolieva, G. MacDonald and V. Norkin, 2001, Problems of catastrophic risk management, Kibernetika i sistemnyi analiz (Cybernetics and System Analysis), N 2, 99-110.
• Y. Ermoliev and V. Norkin, 1997, On nonsmooth and discontinous problems of stochastic systems optimizations, European Journal of Operational Research, vol. 101, pp. 230-244.
• Y. Ermoliev and R. Wets (Eds.), 1988, Numerical Techniques for Stochastic Optimization, Springer verlag.
• M. Makowski, 2005, Mathematical Modeling for for Coping with Uncertainty and Risk, T. Arai (ed.), Systems and Human Science - For Safety, Security, and Dependability, Elsevier, Amsterdam, Holland, pp. 622-630.
• M. Makowski, 2003, Model-based Support for Risk Management, Knowledge and Systems Sciences: Towards Meta-Synthetic Support for Decision Making, J. Gu, Y. Nakamori, Z.Wang and X. Tang (Eds), Lecture Notes in Decision Sciences, vol. 3, Global-Link Publisher, Hong Kong, London, Tokyo, pp. 87-94, ISBN 962-8286-33-1.
• K. Marti, Y. Ermoliev, G. Pflug (Eds.), 2004, Dynamic Stochastic Optimization, Springer Verlag.

A sample of publications from policy oriented studies:

• O. Godal, Y. Ermoliev, G. Klaassen, M. Obersteiner, 2003. Carbon Trading with Imperfect Observable Emissions, Environmental and Resource Economics, 25: 151-169.
• T.Ermolieva, Y.Ermoliev, C.Hepburn, S.Nilsson, M.Obersteiner, 2003. Induced Discounting and Its Implications to Catastrophic Risk Management, IR-03-029.
• T.Ermolieva, Y.Ermoliev, G.Fischer, I.Galambos, 2004. Role of Financial Instruments in Integrated Catastrophic Flood Management, Multinational Financial Journal, forthcoming.
• G. Fischer, T.Ermolieva, H. Van Velthuizen and Y.Yermoliev, 2004. On Sequential Downscaling Methods for Spatial Estimation of Production Values and Flows. Workshop on Data Assimilation and Recursive Estimation, September 20-21, Venice, Conference proceedings.
• A. Gritsevskii, Y. Ermoliev, 1999. An Energy Model Incorporating Technological Uncertainty, Increasing Returns and Economic and Environmental Risks. Proceedings of International Association for Energy Economics 1999 European Energy Conference "Technological progress and the energy challenges", 30 September - 1 October, Paris, France.

Responsible for this page: Amalia Priyatna
Last updated: 17 Nov 2011