Objective
Extreme events are a key manifestation of complex systems, in both the natural and human world. Their economic and social consequences are a matter of enormous concern. Much of science has concentrated, until recently, on understanding the mean behavior of physical, biological or social systems and their "normal" variability. Extreme events, due to their rarity, have been hard to study and even harder to predict. We propose to develop methods for the description, understanding and prediction of extreme events across a range of natural and socio-economic phenomena. General tools will be developed to extract the distribution of these events from existing data sets. Models that are anchored in complex-systems concepts will be constructed to incorporate a priori knowledge about the phenomena and to reproduce the data-derived distribution of events. These models will then be used to predict the likelihood of extreme events in prescribed time intervals. The methodology will be applied to three sets of pr oblems: (i) natural disasters from the realms of hydrology, seismology, and geomorphology; (ii) socio-economic crises, including those associated with criminality, mass violence, and terrorism; and (iii) rapid, and possibly catastrophic, changes in the interaction between economic activity and climate variability. The proposing team brings together expertise in various branches of the theory of nonlinear and complex systems, as well as in the application areas envisaged. Most importantly, it has a tru ly interdisciplinary, problem-oriented outlook. The work plan has been designed to integrate the expertise across the team and to provide problem-specific, as well as general results that will considerately strengthen the European Research Area in its " tackling complexity in science."
Fields of science
Topic(s)
Call for proposal
FP6-2003-NEST-PATH
See other projects for this call
Funding Scheme
STREP - Specific Targeted Research ProjectCoordinator
Paris
France
Participants (15)
Differdange
Paris
Paris
Roma
Paris
Brussels
Bucharest
Moscow
London
Paris
Roma
Torino
Hamburg
Potsdam
Liege