This is a series on Decision-Making under Deep Uncertainty (DMDU). The first article is a thorough introduction to complexity modeling and DMDU. The second article builds upon these basics and focuses on its application to the field of AI governance. Together, these articles are intended for AI governance researchers who want to extend their tool kit with computational tools. I show how we can support decision-making with simulation models of socio-technical systems while embracing uncertainties in a systematic manner. The technical field of DMDU offers a wide range of methods to account for various parametric and structural uncertainties while identifying robust policies in a situation where we want to optimize for multiple objectives simultaneously.