"On prediction of critical events in complex systems"
A.A. Soloviev (International Institute of Earthquake Prediction Theory and Mathematical Geophysics, Russian Academy of Sciences)
Abstract:
One of important features of nonlinear complex systems: persistent reoccurrence of abrupt overall changes, called here “critical events (transitions),” is considered. Approaches to prediction of such events are outlined. Specific applications include the following critical events: in the Earth lithosphere – large earthquakes; in economic systems – starts and ends of economic recessions and episodes of a sharp increase in the unemployment rate; in socio-economic urban systems (megacities) – surge of the homicides in a megacity. A “technical” analysis that is a heuristic search of phenomena preceding critical events is used to develop robust and rigidly defined prediction algorithms of the “yes or no” variety indicating at any time moment, whether a critical event should be expected or not within the subsequent time period. The used methodology is pattern recognition of infrequent events developed by the artificial intelligence school of I.M. Gelfand for a study of rare phenomena of highly complex origin, that, by their nature, limit the possibilities of using classical statistical methods. The data used by the prediction algorithms are background seismicity (in the case of the lithosphere), macroeconomic indicators (in the case of economic systems), and statistics of several types of less severe crime (in the case of megacities).