1 Lecture I: Modeling the dynamics of complex systems: Incorporating AI and HPC into dynamic modeling.

Abstract Why do things change over time? Why is it difficult to identify the potential consequences of the implementation of policies in dynamic systems? Why do so many actions designed to correct problems do not work or make things worse? Change and complex interaction are the norm in real-world systems. Actors in such complex systems face challenges and problems when they try to accomplish activities geared toward meeting their goals. Decision-making processes are at the core of how organizations, and individuals, deal with the causes and consequences of complexity and change in complex systems. Because of the inherent unpredictability of complex systems, and because of multiple non-linear effects and time delays in how complex systems respond, decisions made to address or to prevent problems are often the reason why problems persist over time or emerge in the future. Linear and traditional analytic approaches (such as statistics or econometrics) often fall short in helping understand, and change, problematic behavior in complex systems. The system dynamics approach, based on feedback and control theory, is well suited for tackling such complex and dynamic phenomena. In this presentation, the basic principles underlying dynamic feedback systems and the use and applications of system dynamics modeling will be reviewed. Also, general insights related to the use of AI and HPC in dynamic modeling of complex systems will be discussed.