Difficulties arise in the making of predictions, since ideas and views on the future are often based on current conditions and, with only minor adjustments, it is simply expected that those conditions will continue into the future. With the goal of supporting experts in their scenario analysis this thesis is part of a pilot between the analysis network on national security (Analistennetwerk Nationale Veiligheid) of the Netherlands and the Technical University Delft. It explores if system dynamics is an useful approach in the modelling and simulation of changes in state power dynamics. It does so by researching literature on national power and through the analysis multi-scale geopolitical model to simulate over 217 countries. Exploratory modelling and analysis has been performed on the power dynamics of The United States, China, and Russia. This thesis concludes that system dynamics is an appropriate approach in the modelling and simulation of geopolitics and shifts in power dynamics. But, that uncertainties in the relationships between variables, missing data, and the modelling choices made to overcome these issues make it very difficult to built a good causal model to support expert scenario analysis.

This research examined the effect of an improved road network on the factors economic growth, road safety, emissions, education, health, employment and tourism in developing countries. A case study is conducted on the country Namibia in Southern Africa. A System Dynamics (SD) model is developed to assess the effects of future scenarios in terms of road infrastructure on the mentioned factors. The SD model is based on a literature study on developing countries in general and a data analysis on Namibia in specific. The identified scenarios are (i) Namibia becoming the logistics hub of Southern Africa by 2022 and (ii) enhance tourism by improving road infrastructure between the major tourism attractions. Both scenarios lead to improvements in health, tourism and employment. The primary school enrolment rates remained the same over time for both scenarios. Road safety increases for both scenarios due to upgrades to bitumen, but the extent to which it increases depends on the extra measures that are taken with regards to road safety. It can be increased more by campaigns or more controls. Emissions go up in both scenarios, because emissions are a direct result of constructing and upgrading roads. The tourism scenario attracts more tourists because the roads between the major attractions are upgraded to bitumen. Both scenarios are realistic and might happen in the future. The scenarios can and might be combined.

The Netherlands is becoming increasingly diverse, amongst other things, due to ethnicity, religion, and urbanity. These diverse societies cluster in districts all across the Netherlands. There is an increase in tension between social groups, especially between people with and without migration background. Data analysis is used to measure the public opinion of each district in the Netherlands based on election results. System dynamics is used to model the public opinion over time and the polarizing behavior within districts. Polarization is a thought construct and does not need physical evidence to occur so the public debate plays a large role because it creates fuel for polarization. Even though there are national trends regarding public opinion, it is advised to look at a district in specific to understand why polarization may occur there.

This study has been performed in cooperation with TNO and set out to analyze the robustness of Dutch energy transition policies under deep uncertainty for the scope of the built environment sector. Open data is gathered which is used in a System Dynamics model to allow exploration under deep uncertainty. The Adaptive Robust Design methodology has been adopted to understand how energy transition policies can be designed to be more robust under deep uncertainty for the period of 2019-2050. Subsidy-based policy variants have been created, inspired by promising policy instruments from current policy documents. Static, dynamic and mission-oriented policy variants have been simulated to show effects on annual CO2-eq emissions, renovated houses and awarded subsidies. Adaptive policies have shown to be promising to curb undesired influence of uncertainties. However, this study showed that subsidy percentage, alone, does not ensure that policy targets for 2050 are reached. Policies should also ensure ample increase in renovation capacity to keep up with rising demand. The limitation of merely subsidy-based policies resulted in less significant differences between the policy variants. To benefit of the adaptive nature of the climate plan, policies should include ample adjustment mechanisms within policies to realize their goals by adapting to changing circumstances.

What happens when we treat our policy messages more like mythology and less like rhetoric? How can we even begin to structure policy as a mythology? What is a myth anyway? This thesis endeavors to explore the mysterious world of stories, through an extensive literature review of literary analysis, and a exemplary case study about the impact of global plastics production on the future. This study leads to an experiment, where four different forms of communication: the best of rhetoric and stories, are tested against each other for effectiveness in shifting the mood, transferring information, and effecting the audience's experience. The results show that even with a small sample size the impact of stories as effective tools of policy communication are real. They result in a drastically different message than the other forms tested, hinting at the possibility of a future where stories are central to shaping policy.

This study approached migration-related problems from a global perspective. The inter- connected nature of migration means that migration problems accumulate and intensify throughout the world. To capture these effects, a data-rich model component was devel- oped for global migration dynamics. To that extent, this study has developed the following innovations on a methodological level: a technique to comprehensively implement spatial phenomena into System Dynam- ics, a method to implement multi-scale System Dynamics through subscript mapping, and a semi-automated data acquisition process to obtain and structure a great variety of data sets on a country level. The nature of the developed model component is generic. That is, the component is scalable, and resolution-independent, as it is initialized through exter- nal databases. Therefore, a significant part of this study comprised the development of a semiautomated data acquisition process. The model component was then connected to a root-causes model component to analyze future migration scenarios on a global scale. It was found that migration is expected to increase in line with population growth. Policies were implemented and tested on a global, regional, and national scale. In the global policy case, the location of additional shelter and coping capabilities was found to greatly influence migration dynamics across the world. Additional shelters in low-income countries greatly reduced the number of migrants traveling towards the more wealthy coun- tries. The regional policy—closure of the European borders when a certain threshold is reached—had significant effects on the countries near the border, but failed to significantly improve the situation in the rest of Europe. This can be attributed to the delayed effects of such policies in these countries. National policies were tested for the Netherlands. Effects of these policies were insignificant, due to the inertia of the migration system. The number of migrants in Germany—a neighboring country—did not significantly change, and even the effect on the number of migrants in the Netherlands was negligible. Therefore, the first advice from this study towards policy-makers is that it might be more effective to address the root causes of migration, rather than just mitigating its detrimental effects. In addition, the inertia of the migration system requires globally aligned, large-scale policy implementation to prevent and mitigate migration-related problems. The delay in these policies should not be neglected, so it is important to identify potential problems as early as possible and take appropriate action.

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

Personnel shortage will become a major public health problem in many countries in the near future, and one of the main causes of this problem is societal aging (Burke et al., 2013). The goal of this study is to discover the effect and the future affordability of societal aging on health care systems across the OECD, to investigate which policies are needed to overcome this problem and to shed new light on new debates of new policies. A model-based policy analysis using a multi-region dynamic modeling method has been performed to reach this goal. In this research, a stakeholder analysis is executed to examine which stakeholders are involved in the decision making process concerning societal aging. Furthermore, a system dynamics model is built which represents the health care systems across the OECD. Within this model, four policies have been tested to examine which policies are suited to be implemented to close the future gap between the health supply and the health demand. The first policy is the automation-policy, which decreases the administrative tasks of nurses, through technological advancements. The second policy that is tested is the eHealth-policy, which supports health care institutions to make use of eHealth applications. The third policy is the additional-students-policy, which calculates the inflow of nursing students using the gap of needed nurses. The last policy is the euthanasia-policy, which helps people suffering from dementia to commit euthanasia. The results of the research show a decrease in the gap between the demand for health care and the health care supply when the policies are implemented. The implementation of the additional-students-policy performs best in terms of closing the gap, but the costs of this implementation are high. Therefore governments should make trade-offs between the costs and the quality of health care. However, these trade-offs may include ethical dilemmas like the example of the committing-euthanasia-policy.

The thesis is under embargo as it contains confidential models and data.

Technological progress and innovation have significantly contributed to global economic growth, societal advancement, and higher living standards. However, there is growing concern over the future that lies ahead because of increasing robotisation and progress in artificial intelligence (AI), which are feared to cause significant loss of labour demand. The body of posterior economic scientific work addressing this topic mostly concludes on a positive note. Namely, recent technological advancements have resulted in a net increase in labour demand, but this demand is redistributed to different tasks and occupations. Yet, future oriented research, most notably by Frey and Osborne (2017), has sparked a debate on the future of work due to estimations that indicate that over 40% of jobs will become automatable in the next 20 years. Therefore, the societal question remains: Robots and Artificial Intelligence, the new economic motor or downfall of the working class?...

Policymaking often involves different parties such as policymakers, stakeholders, and analysts each with distinct roles in the process. To assist policymakers, policy analysts help in structuring the problem, designing, and evaluating policy alternatives. Analysts face many challenges, like complexity and uncertainty in a system of interest, while supporting the policymaking process. Frequently, analysts rely on mathematical models that represent the key features of the system. Assumptions made during modelling introduce a significant level of uncertainty in the models, and forecasting based on models is therefore always bound by this uncertainty. Instead of focusing on limited best-estimate predictions under uncertainty, exploring a plethora of plausible futures by using mathematical models can help supporting decision-making. In current practice, uncertainty analysis for decision-making is mostly limited to technical and shallow uncertainties but not focused on deep uncertainty. This thesis contributes to a solution for enhanced handling of deep uncertainty to support policymaking. We have developed a new methodological approach for improving analytical support for policymaking under deep uncertainty, and demonstrated each analytical advancement stage with case studies. This thesis proposes to improve analytical support for policymaking to better handle deep uncertainty. Building upon the existing pragmatic practice, a systematic approach for designing adaptive policies under uncertainty is developed. The Adaptive Robust Design (ARD) approach in combination with multi-objective robust optimization will improve the support for policymaking under deep uncertainty. The effectiveness of ARD for developing adaptive robust policies under deep uncertainty is shown by illustrative case studies. @en

The global society is facing a number of Grand Challenges, problems that are often ill- defined and for which time is running out. Finding potential solutions requires the use of Modelling and Simulation (M&S) tools that can facilitate large scale collaborative and multi-perspective efforts. Ideally this is done by building across contexts on each other’s insights and contributions as if they were bricks. However, current M&S software does not support context independent bricks due to a variety of rigid bindings; the bricks are essentially glued to a context and cannot easily be reused. In this research we combine the fields of M&S and Computer Science to determine under which circumstances context independent bricks can exist. We took the current concepts about components and applied a low coupling high cohesion attitude to create a new flexible structure, the Brix; we formally defined this structure in the Brix Formalism. Furthermore we built a proof-of-concept system named BrixMS in JavaScript that can simulate models built in context independent bricks. BrixMS offers little in the way of features, yet it has the potential to simulate any computable system with an unmatched flexibility and reusability. While hard limits exist in terms of how we can represent Grand Challenges and how much we can compute, BrixMS proves that context independent bricks are a practical reality that we can use to more effectively find solutions to our Grand Challenges.