Abstract for: On addressing grand challenges and complex issues: dealing with more than just dynamic complexity

System Dynamics has been used to study grand challenges and complex issues, and test policies to address them. However, many grand challenges and complex issues still need to be addressed urgently. Most challenges and issues are characterized by much data and information but also by uncertainty, by clear causes but also by random events, by interconnectedness but also by important geo-spatial differences, by dynamic complexity but also by actor, institutional, and detail complexity. In order to address these challenges and issues, all important characteristics need to be dealt with. We will show many recent innovations and developments in the field of modelling and simulation that make this possible, including: new hybrid types of modelling, modelling and simulation of randomness and uncertainty, the use of new sampling approaches and machine learning techniques to generate and identify typical behaviors, data-rich and scenario-rich simulation, and new ways to visualize outcomes of simulation models. To illustrate these recent innovations and developments, we will use cases we have been working on recently, including the 2015-2016 refugee crisis in Europe, state instability of rentier states due to climate mitigation policies, climate change induced food scarcity across 175 countries, potential resource scarcity, and the spreading of Zika and Ebola.