The Delft Method for System Dynamics (SD) is a proven method for learning basic SD. The method focuses on learning by doing: first you try to work through an exercise, and if you do not understand something, then you can look up the theory. The book contains exercises on topics such as causal loop diagrams, delays, and when SD is an appropriate methodology. It also contains modelling exercises that show students how to build low to medium complexity models, and how to use these models for policy analysis. The theory chapters cover all phases of the modelling cycle: problem articulation, conceptualisation, formulation, evaluation (including validation and scenario analysis), and policy analysis. This book is intended for students and teachers in large or small System Dynamics courses, and for motivated students that want to learn SD at their own pace.@en

Mesopelagic fishes are a vital component of the biological carbon pump and are, to date, largely unexploited. In recent years, there has been an increased interest in harvesting the mesopelagic zone to produce fish feed for aquaculture. However, great uncertainties exist in how the mesopelagic zone interacts with the climate and food webs, presenting a dilemma for policy. Here, we investigate the consequences of potential policies relating to mesopelagic harvest quotas with a dynamic social-ecological modeling approach, combining system dynamics and global sensitivity analyses informed by participatory modeling. Our analyses reveal that, in simulations of mesopelagic fishing scenarios, uncertainties about mesopelagic fish population dynamics have the most pronounced influence on potential outcomes. The analysis also shows that prioritizing the development of the fishing industry over environmental protection would lead to a significantly higher social cost of climate change to society. Given the large uncertainties and the potential large impacts on oceanic carbon sequestration, a precautionary approach to developing mesopelagic fisheries is warranted.

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Tin is an important metal for society with a high risk of supply disruptions. It is, therefore, classified as a critical material in many parts of the world. An exception is the European Union, for which tin was classified as a non-critical material in 2023. However, there are many discrepancies in the literature regarding the definitions and values of the indicators used to determine tin criticality in general, and recycling indicators in particular. Values for end-of-life recycling rate (EoL RR) range between 20% and 75%, and values for end-of-life recycling input rate (EoL RIR) range between 11% and 32%. In this paper, we critically assess the circularity and criticality indicator values for tin and calculate new values using material flow analysis. The new values for tin recycling indicators are lower than those used in most previous research, with a global EoL RR of 16% and an EoL RIR of 11% in 2017. Based on the updated recycling values, combined with a highly concentrated supply, high import reliance, and difficult substitution, we argue that the European Union should classify tin as a critical material. This reclassification can lead to more policy attention for tin, which can help reduce the impact of future supply disruptions and increase the resilience of the European and global tin supply chains.

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Background: Health systems worldwide struggled to obtain sufficient personal protective equipment (PPE) and ventilators during the COVID-19 pandemic due to global supply chain disruptions. Our study’s aim was to create a proof-of-concept model that would simulate the effects of supply strategies under various scenarios, to ultimately help decision-makers decide on alternative supply strategies for future similar health system related crises. Methods: We developed a system dynamics model that linked a disease transmission model structure (susceptible, exposed, infectious, recovered (SEIR)) with a model for the availability of critical supplies in hospitals; thereby connecting care demand (patients’ critical care in hospitals), with care supply (available critical equipment and supplies). To inform the model structure, we used data on critical decisions and events taking place surrounding purchase, supply, and availability of PPE and ventilators during the first phase of the COVID-19 pandemic within the English national health system. We used exploratory modelling and analysis to assess the effects of uncertainties on different supply strategies in the English health system under different scenarios. Strategies analysed were: (i) purchasing from the world market or (ii) through direct tender, (iii) stockpiling, (iv) domestic production, (v) supporting innovative supply strategies, or (vi) loaning ventilators from the private sector. Results: We found through our exploratory analysis that a long-lasting shortage in PPE and ventilators is likely to be apparent in various scenarios. When considering the worst-case scenario, our proof-of-concept model shows that purchasing PPE and ventilators from the world market or through direct tender have the greatest influence on reducing supply shortages, compared to producing domestically or through supporting innovative supply strategies. However, these supply strategies are affected most by delays in their shipment time or set-up. Conclusion: We demonstrated that using a system dynamics and exploratory modelling approach can be helpful in identifying the purchasing and supply chain strategies that contribute to the preparedness and responsiveness of health systems during crises. Our results suggest that to improve health systems’ resilience during pandemics or similar resource-constrained situations, purchasing and supply chain decision-makers can develop crisis frameworks that propose a plan of action and consequently accelerate and improve procurement processes and other governance processes during health-related crises; implement diverse supplier frameworks; and (re)consider stockpiling. This proof-of-concept model demonstrates the importance of including critical supply chain strategies as part of the preparedness and response activities to contribute to health system resilience.

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Different leader-follower behaviors may be observed in models, such as group gathering, backtracking, and changing between groups. However, a comparison of these behaviors resulting in possible substantially different estimates of optimal evacuation procedures is lacking. Hence, we developed an agent-based model in combination with exploratory modeling to compare backtracking, group gathering, and followers changing leaders and investigate their influence on the evacuation and response time. The simulation results showed that backtracking and changing of groups increased the evacuation time. Whereby group gathering increase the response time. In addition, the combination of behaviors increases the influence on evacuation and response time. Further research needs to test these results with empirical studies and investigate the impact of other leader-follower behavior. The found insights may be utilized in evacuation research for modeling this behavior and they provide a valuable basis for designing policies in buildings with a high distribution of leader-follower groups.@en

Urban form develops in close feedback with different modes of transportation. The introduction and adoption of automated vehicles (AVs) are expected to have an impact on the development of cities as well, as the use of AVs may, for example, lead to more efficient road use and less need for parking spaces. In order to study those impacts, we developed a geospatially disaggregated system dynamics (SD) model, through the use of subscripts, of the Copenhagen metropolitan region. We used this SD model to explore the consequences of 12 main uncertainties related to the introduction of AVs on urban development and develop future scenarios following the exploratory modelling and analysis methodology. Our analysis led to two distinct scenarios. In one scenario, AVs lead to more vehicle use, which leads to more urban sprawl and more congestion as a consequence. In the other scenario, more shared use of cars leads to less traffic and more open space in the city.

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Scenario Discovery is a widely used method in model-based decision support for identifying common input space properties across ensembles of exploratory model runs. For model runs with behavior over time, these properties are identified by reducing each run to a single value, which obscures potentially decision-relevant dynamics. We address the problem of considering dynamics in Scenario Discovery by applying time series clustering to the ensemble of model runs, and then finding the common input properties for each cluster. This separates the input space into multiple scenarios, each corresponding to a distinct model dynamic. Policy interventions can be targeted at different scenarios by analyzing overlap of these subspaces. Our work expands Scenario Discovery by improving consideration of system behavior over time, which is highly relevant for the management of complex nonlinear systems such as ecosystems or technical infrastructure.

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Infrastructure asset managers are challenged by rapid changes around the world such as urbanisation and climate change. They have to deal with multiple complex and dynamic infrastructure systems that can influence each other and that can be influenced by various uncertain factors. Traditional planning methods are not well suited to complex and dynamic infrastructure environments. However, there are various alternative methods that can be used. This paper presents a simulation model for adaptive long-term infrastructure planning. For this model, a combination of planning and simulation methods is used that can cope with complex and dynamic (infrastructure) environments (i.e. system dynamics, exploratory modelling and analysis and adaptive pathways). This methodology is illustrated with a case consisting of a road system and a lock system in the Netherlands. The approach should be tested further in other cases, but it shows promise in improving the support of infrastructure decision-making in a complex, dynamic and uncertain infrastructure environment.@en

Infrastructures are subjected to rapidly changing future developments. These future developments, in combination with the complex nature of infrastructure systems, involves assessing multiple variables and their underlying relations. It is hard for asset managers to incorporate this dynamic uncertainty and complexity in their long-term plans. Moreover, it is perceived as challenging to translate the effects from network-level interventions to operation level and thereby develop a shared policy for an infrastructure network in which all stakeholders are involved. Since infrastructures are the backbones of our economies, insight is required in the magnitude and time of occurrence of future developments in order to optimize asset performances. This study proposed a multivariate simulation approach, as a strategic decision-support tool. Insight was created in the challenges associated with the long-term planning within complex and dynamic infrastructure systems to implement interventions on a more substantial and informed basis.@en

Simulation models are increasingly used for exploring the consequences of deep uncertainty in complex societal issues. The complexity of societal grand challenges, often characterised by the interrelatedness of different elements in the systems underlying these challenges, often renders mental simulation impossible, necessitating the use of simulation models to assist human reasoning. In addition, these grand challenges are typically also subject to deep uncertainty, making it, for example, impossible to come to a shared understanding of parts of the system and exogenous inputs to it, or even a shared problem definition. Under deep uncertainty, simulation models can be used to explore the consequences of different combinations of assumptions about uncertain factors or attributes of the problem situation and the underlying system. This type of simulation model use was introduced in 1993 as Exploratory Modelling and Analysis (EMA). In more recent years, this approach has become a major underpinning of the Decision Making under Deep Uncertainty (DMDU) field. The treatment of deep uncertainty in much DMDU research can be improved, however. In most DMDU research to date, pre-existing models are used. These models were generally developed for ‘consolidative’ use: the modellers tried to unify existing knowledge to come a single, ‘best’ model. While most modellers will agree that these models are not perfect representations of reality, and often agree that they as such cannot be validated in the strict sense of the word, these modellers and their models do not acknowledge deep uncertainty. The use of consolidative models is arguably problematic if one agrees that the issue at hand is characterized by deep uncertainty. Therefore, models are needed that are explicitly developed for ‘exploratory’ use: models that explicitly incorporate deep uncertainty potentially relevant for the research question or questions at hand. However, little experience and guidance exists regarding development and use of specifically exploratory models. In this dissertation, a first attempt is made to identify, and provide guidance for, the critical choices made during the development and use of exploratory models.@en

During the first months, the 2014 outbreak of the Ebola virus (EBOV) in West Africa was characterised by inadequate intervention capacities. In this paper, we investigate (1) the influence of limited but dynamic intervention capacities and their effect on the effective reproduction number, and (2) the effects of proactive versus reactive intervention approaches. We use a transmission model extended with dynamical intervention capacities. Taking into account a bandwidth for potential over- and under-reporting in reported Ebola virus disease cases, the model is used to generate ensembles of plausible scenarios. Next, it is used for testing the effectiveness of more proactive approaches in extending intervention capacities across these scenarios. We show that reactive approaches in extending intervention capacities can lead to continued under-capacity, and, consequently, to an increase of the effective reproduction number and to accelerated EBOV transmission. Proactive approaches, which take deployment delays, doubling times of diseases, and potential under-reporting of the number of cases into account, help in limiting the total number of cases and deaths if the effective reproduction number in isolation is lower than the effective reproduction number outside of isolation. If the effective reproduction number in isolation is higher, proactive intervention policies still outperform reactive intervention policies.@en

While the shale revolution was largely a US’ affair, it affects the global energy system. In this paper, we look at the effects of this spectacular increase in natural gas, and oil, extraction capacity can have on the mix of primary energy sources, on energy prices, and through that on internal political stability of rentier states. We use two exploratory simulation models to investigate the consequences of the combination of both complexity and uncertainty in relation to the global energy system and state stability. Our simulations show that shale developments could be seen as part of a long term hog-cycle, with a short term drop in oil prices if unconventional supply substitutes demand for oil. These lower oil prices may lead to instability in rentier states neighbouring the EU, especially when dependence on oil and gas income is high, youth bulges are present, or buffers like sovereign wealth funds are too limited to bridge the negative economic effects of temporary low oil prices.

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This paper introduces an approach to compare simulation runs from multiple System Dynamics simulation models. Three dynamic hypotheses regarding the uncertain evolutions of long-term copper availability are introduced and used to illustrate the new approach. They correspond to three different perspectives on the copper system (global top-down, global bottom-up, and regional top-down). Although each of these models allows to generate a wealth of behavioural patterns, the focus in this paper is on the differences in trajectories caused by different models for identical values of shared parameters and identical settings of other assumptions, not on differences in behavioural patterns caused by each of the models. Hence, differences in trajectories between the three models are identified, quantified, and classified based on a quantified measure of difference. For these models, small differences between the trajectories are only found in stable runs, while the alternative perspectives are largely responsible for medium to large differences. Hence, it is concluded that multiple dynamic hypotheses may have to be modelled when dealing with uncertain issues.@en