This article is part of a series on Decision-Making under Deep Uncertainty (DMDU). Building on an introduction to complexity modeling and DMDU, this article focuses on the application of this methodology to the field of AI governance. If you haven't read the previous article, I strongly recommend pausing reading this...
This post is based on my introduction talk at EAGxBerlin 2022. It is intended for policy 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...
This article is part of a series on Decision-Making under Deep Uncertainty (DMDU). Building on an introduction to complexity modeling and DMDU, this article focuses on the application of this methodology to the field of AI governance. If you haven't read the previous article, I strongly recommend pausing reading this article and going through the introduction first. Many aspects will only appear clear if we share the same basic terminology. I might be biased, but I think, it's worth it.
I have written up this article because of two reasons. The 1st reason is that various people have approached me, asking how I envision the application of the introduced methodology to... (read 8656 more words →)
This post is based on my introduction talk at EAGxBerlin 2022. It is intended for policy 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 decision-making under deep uncertainty offers a wide range of methods to account for various parametric and structural uncertainties while identifying robust policies where we want to optimize for multiple objectives simultaneously for particularly vulnerable scenarios.