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

Policy of Multi-Actor Systems is an introduction into the art of craft of problem exploration and problem structuring. It positions policy analysis as a scientific discipline focused on systems analysis in a multi-actor context to support better informed decision-making. The approach presented in this book is considered to be the cornerstone of the curricula of the Faculty of Technology, Policy and Management of Delft University of Technology and underlies the research on (the governance of) socio-technical systems. Systems thinking applied in a multi-actor environment and its inherent multi-disciplinary character is what makes this work stand out from traditional hard- and soft systems approaches. The core of the book is dedicated to systems analysis, actor- or stakeholder-analysis and discusses methods for dealing with uncertainty. These analytical activities combined lead to a rich problem description and to plans for further research. Due to the stepwise approach this book serves as a basis for any problem analysis both for our bachelor and master students, our alumni worldwide and any interested practitioners.@en

Intestinal campylobacteriosis, caused by Campylobacter ingestion, is the most reported zoonosis in the EU; it is societally costly and can lead to more severe sequelae. To reduce Campylobacter infections, biosecurity measures at both farms and slaughterhouses are warranted. However, the potential improvements achieved by these interventions have not been quantified. We used a systems science approach to develop a simulation model, synthesizing information from interviews with stakeholders in the Dutch broiler production industry and the current literature. The model includes both farms and slaughterhouses in a “system of systems,” helping to clarify the complexity of interrelated components of these systems and analyse the impact of various interventions. Insects, transportation crates, farm personnel, and catchers were identified as potential Campylobacter sources and modelled as elements of feedback loops. Insect control, farm hygiene, visitor control, thinning, and transportation control interventions were analysed. The model was shown to accurately describe the seasonality of Campylobacter, which supports its validity. Model simulation revealed that insect control interventions had the strongest impacts, followed by combined farm hygiene and visitor control, and combined thinning and transportation control. Insect control interventions alone reduced the peak percentage of contaminated chickens from 51% to 26% and the peak percentage of highly contaminated (>1000 CFU/g) neck samples of chicken carcasses from 13% to 8%. Implementing all interventions concurrently reduced the peak percentages of contaminated chickens to 5% and highly contaminated chicken neck samples to 2%. These results suggest that multiple biosecurity measures must be implemented to reduce Campylobacter contamination.@en

In his paper on antifragility in supply chains, Größler (2020) explains the concept of antifragility, provides a managerial operationalisation of antifragility and investigates the implications of antifragility in a supply chain setting by applying it to a system dynamics model of a four‐tier supply chain. In these Comments, I take a look at each of these three aspects, i.e., the definition of antifragility, the way in which it has been put into operation, and the implications of the current research, and add some thoughts and questions that came to mind when reading the paper. @en

The PrESTO system is a peer review and peer assessment tool that allows large numbers of students to practice open-ended assignments, while keeping the workload of the teaching staff manageable. PrESTO was originally developed for a quantitative modeling course, but is applicable to any course with open-ended assignments that can be divided into successive steps. The software organizes the peer review and peer assessment workflow, and guides the students through the activities required in each step: review, give feedback on and assess the work of an anonymous predecessor, and then improve and extend it with a next step.

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Policy analysis is a multifaceted field in which a variety of different activities and ambitions have found a place. Some policy analysts conduct quantitative or qualitative research, while others reconstruct and analyze political discourse or set up citizen fora. Some policy analysts are independent researchers, some are process facilitators, while others act as political advisers. The debate on the discipline—for example on its foundations, underlying values and methods—is conducted in a fragmented way. This chapter provides a framework for positioning the different perspectives and for highlighting the implications of choosing a perspective when designing or evaluating a policy analysis project.@en

Residential energy efficiency improvements often have a smaller effect than expected. Although there is agreement on the existence of this effect, called the rebound effect, there is no agreement on the size of the effect. The objective of this study was to investigate the potential of using serious games to assess this effect. We used a game in which participants play home owners who manage their households in terms of energy consumption. Results of experiments with 50 players showed signs of the rebound effect when players with a low efficiency house reduced their energy consumption more than players with a high efficiency house. In addition, some issues related to previous studies were addressed, such as the possibility to perform an ex-ante assessment and to conduct the study in a controlled environment. Calculations of the size of the rebound effect depended on the approach used to determine the expected effect and showed differences between appliances.

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This paper presents a novel simulation model that shows the dynamic and complex nature of the innovation system of vehicle automation in a quantitative way. The model simulates the innovation diffusion of automated vehicles (AVs) on the long-term. It looks at the system of AVs from a functional perspective and therefore categorizes this technology into six different levels. Each level is represented by its own fleet size, its own technology maturity and its own average purchase price and utility. These components form the core of the model. The feedback loops between the components form a dynamic behavior that influences the diffusion of AVs. The model was applied to the Netherlands both for a base and an optimistic scenario (strong political support and technology development) named “AV in-bloom”. In these experiments, we found that the system is highly uncertain with market penetration varying greatly with the scenarios and policies adopted. Having an ‘AV in bloom’ eco-system for AVs is connected with a great acceleration of the market take-up of high levels of automation. As a policy instrument, a focus on more knowledge transfer and the creation of an external fund (e.g. private investment funds or European research funds) has shown to be most effective to realize a positive innovation diffusion for AVs. Providing subsidies may be less effective as these give a short-term impulse to a higher market penetration, but will not be able to create a higher market surplus for vehicle automation.@en

Several model-based, analytical approaches have been developed recently to deal with the deep uncertainty present in situations for which futures studies are conducted. These approaches focus on covering a wide variety of scenarios and searching for robust strategies. However, they generally do not take the multiplicity of stakeholder perspectives into account in analytic terms, which could bring in diverse opinions and views, not only on possible futures but also on values and interests. In this study, we present an approach to incorporate stakeholder perspectives into model-based scenarios for exploring the future dynamics of the Dutch gas sector. The results demonstrate that the scenario space can be demarcated according to the perspectives. This allows for a systematic comparison of the perspectives and provides a basis for identification of robust strategies. Also, the analysis shows that incompatible elements between the model and perspectives, or within perspectives can be identified. This provides insights about the problem complexity and potential barriers to the futures envisioned by the perspectives. Future research can strengthen this approach by involving stakeholders in modelling and in the model-based representation of the perspective narratives to enhance learning and credibility, and can extend the analysis to identify (socially) robust policies.

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To contribute to existing research on the influence of various factors on household car ownership in the Netherlands, this study addressed the question whether and to what extent the influence of economic, sociodemographic, and spatial factors on the number of cars owned by households has changed over time. There seems to be an absence of studies investigating the changing influence of these factors on car ownership in recent decades, and in the Netherlands. The study used the statistical method of ordered logistic regression on household mobility data on 162,593 households, collected by the National Traffic Survey of the Netherlands in 1987, 1991, 1995, 1999, 2003, 2010, and 2014. The results show that the influence of household income, size, composition, gender, age, education, working status, and suburbanization levels on car ownership changed substantially between 1987 and 2014. The strong influence of household income on household car ownership diminished quite remarkably between 1987 and 2014, whereas the influence of household size grew significantly during the period. The results could serve as a first step toward a predictive model that endogenously estimates household car ownership levels in the Netherlands.@en