Research on system simulation approach under deep uncertainty
Complex decision analyses are often faced with the high-level uncertainty beyond the normal range of common understanding,which is so-called"deep uncertainty".Generally,the system characterized with deep uncertainty cannot been or has not been known well,and it usually has many components and mechanisms that would interact in a variety of ways and change over time.Deep uncertainty brings unexpected difficulties to system simulation and forecast.In recent years,the research on the simulation approach under deep uncertainty has been becoming one of the important directions in the field of system science.This paper firstly investigates the state of the art in the concept understanding and its cognition development from uncertainty to deep uncertainty,and then summarizes the key features and constraints for system simulation under deep uncertainty.The current mainstream simulation approaches including their modeling thoughts and implementations are also elaborated by systematically classifying the published literature and outlining main trends in modelling uncertain system.On this basis,a dynamic exploratory simulation approach based on data-and-model hybrid is proposed and applied into traffic simulation and forecast.Simulation experiment results show that this approach is a useful pathway to enhance computing system's adaptability to uncertainties and complex changes of real system.
uncertaintydeep uncertaintysystem simulationdata and model hybrid
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