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Integrated Modeling Environment | |||||||||
Coping with Endogenous Uncertainty and Risks | |||||||||
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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:
A sample of publications from policy oriented studies:
Responsible for this page: Amalia Priyatna |
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