@article{WolfFuerstGeigesetal.2023, author = {Wolf, Sarah and F{\"u}rst, Steffen and Geiges, Andreas and Laublichler, Manfred and Mielke, Jahel and Steudle, Gesine and Winter, Konstantin and Jaeger, Carlo}, title = {The Decision Theatre Triangle for societal challenges - an example case and research needs}, volume = {394}, journal = {Journal of Cleaner Production}, doi = {10.1016/j.jclepro.2023.136299}, pages = {136299}, year = {2023}, abstract = {Tackling societal challenges relating to sustainability requires both an understanding of the underlying complex socio-ecological systems and participation of scientists as well as relevant stakeholders, such as practice experts, decision makers, and citizens. This paper introduces the Decision Theatre Triangle, a method which combines empirical information, mathematical modelling and simulation, and a format for dialogue between scientists and stakeholders. While it builds on previous Decision Theatre work, the new structuring into these three elements emphasizes what is needed for setting up a Decision Theatre for a given challenge. Based on experience with a specific example - sustainable mobility in Germany - it is argued that agent-based models are particularly suitable for Decision Theatres and that the method is useful not only for decision support but also for science communication and co-creation of a deeper knowledge of the system under discussion. As a step towards facilitating a broader use of the Decision Theatre Triangle method, the paper then sketches research needs for each of its three elements, with a focus on mathematical modelling and simulation.}, language = {en} } @article{SteudleWinkelmannFuerstetal.2024, author = {Steudle, Gesine and Winkelmann, Stefanie and F{\"u}rst, Steffen and Wolf, Sarah}, title = {Understanding Memory Mechanisms in Socio-Technical Systems: the Case of an Agent-based Mobility Model}, volume = {27}, journal = {Advances in Complex Systems}, doi = {10.1142/S0219525924400034}, year = {2024}, abstract = {This paper explores memory mechanisms in complex socio-technical systems, using a mobility demand model as an example case. We simplified a large-scale agent-based mobility model into a Markov process and discover that the mobility decision process is non-Markovian. This is due to its dependence on the system's history, including social structure and local infrastructure, which evolve based on prior mobility decisions. To make the process Markovian, we extend the state space by incorporating two history-dependent components. Although our model is a very much reduced version of the original one, it remains too complex for the application of usual analytic methods. Instead, we employ simulations to examine the functionalities of the two history-dependent components. We think that the structure of the analyzed stochastic process is exemplary for many socio-technical, -economic, -ecological systems. Additionally, it exhibits analogies with the framework of extended evolution, which has previously been used to study cultural evolution.}, language = {en} }