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 project has researched the consequences of blockchain technology on the
expected future role of banks. And furthermore, how banks can respond to these effects to improve the innovation strategy and remain their future relevance.

The use of machine learning systems has great potential to better predict probabilities of default for credit underwriting. Despite this advantage, herewith there exists the substantial risk of discrimination. Moreover, machine learning models with the highest prediction-accuracy are often the least explicable (i.e. explainable). Nonetheless, explicability is needed to create accountability of automated credit decisions by machine learning systems. Furthermore, there exists a regulatory need for explicability of machine learning systems in the General Data Protection Regulation (GDPR) and the Consumer Credit Directive (CCD). Besides that, an ethical- and societal need exists for explicability. Within the exploration of literature, it becomes clear that research lacks on how to move from a high-level principle like explicability, towards a prospective assessment of a machine learning use case on this principle, it lacks a multi-disciplinary perspective, and it misses an assessment framework that can guide decision-makers within machine learning use cases, aligned with a multi-organizational development lifecycle. This research aims to design a prospective pragmatic assessment framework that can guide decision-makers, within machine learning applications in European credit underwriting cases from the point of view of explicability. To accomplish this, the Design Science Research Methodology (DSRM), complemented with the Value Sensitive Design (VSD) approach, is utilized. To this end, the Explicability Assessment Framework (EAF) was developed. This framework is adapted to the context- and explanation characteristics of the case, and aligns with the CRISP-DM development lifecycle. It was found in two case studies that the framework helps with the decision-making whether a machine learning system is sufficiently explicable or not. Lastly, a wide range of future research areas is identified that needs attention: empirical validation and expansion of the framework, the relevance for automated explanation creation, the scalability to other context and a large amount of explanations, and the practical perspective regarding adoption in the industry.

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.

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.