Abstract

Abstract for: Seven Years of Trying to Help Understand and Improve Systems : Lessons Learned from Practicing System Dynamics in the Public Sector

Introduction After 14 years of teaching SD and being involved in SD research, I left the ivory tower of academia and ventured with a small group of like-minded researchers into the real world, practicing what I had preached for many years, learning many lessons. It is time to share some of the lessons we learned.
Approach From the start on, our SD approach was largely quantitative, mostly data-rich, focused on uncertain system dynamic challenges, aka grand challenges, which are usually dealt with by public sector agencies or governments. The approach to this paper is reflective, across a multitude of projects, and distill lessons that are useful for current and future practitioners.
Results The results we present here are systems insights, suggested adaptations to the SD method (that seemed to be required to have an impact), and lessons learned – especially from failure – about practicing SD outside of academia and research institutes.
Discussion Finally, we discuss the state of the art of current SD practice and future directions for SD practice – in an age of massive data, AI, misinformation, and geo-political uncertainty.

Introduction	After 14 years of teaching SD and being involved in SD research, I left the ivory tower of academia and ventured with a small group of like-minded researchers into the real world, practicing what I had preached for many years, learning many lessons. It is time to share some of the lessons we learned.
Approach	From the start on, our SD approach was largely quantitative, mostly data-rich, focused on uncertain system dynamic challenges, aka grand challenges, which are usually dealt with by public sector agencies or governments. The approach to this paper is reflective, across a multitude of projects, and distill lessons that are useful for current and future practitioners.
Results	The results we present here are systems insights, suggested adaptations to the SD method (that seemed to be required to have an impact), and lessons learned – especially from failure – about practicing SD outside of academia and research institutes.
Discussion	Finally, we discuss the state of the art of current SD practice and future directions for SD practice – in an age of massive data, AI, misinformation, and geo-political uncertainty.