Learning from rediscovering system dynamics models

This article deals with learning from the exploration of system dynamics models. System dynamics modeling intends to improve judgment and decision, but is very time consuming. Model-based interactive learning environments allow saving time, but critics doubt the effectiveness for deep learning. The question is if there is a third way in-between. Relevant examples from system dynamics are analyzed to identify the key activities that trigger learning; they are organized as a structured exploration process, making learners ask relevant questions, obtain valid responses and correctly interpret them. Based upon this, a process for guided rediscovery is proposed together with guidelines for the functional properties of a “systemic exploratory”. Guided rediscovery enables non-specialists to gain relevant insights into dynamically complex situations and is a tool for decision policy design