Pattern Based Evaluation of Blockchain Technology as a Catalyst for Business Model Innovation: Exploratory Research with Focus on the Potential Implications for e-Health

As blockchain technology became more established in the financial arena, federal health IT officials, vendors and developers started looking at its potential and its use in the healthcare sector. Lately, there has been a major hype around the potential of the technology. It is described as a disruptive technology that could enable business model...

Global Migration Dynamics: An Exploratory Study Integrating Multi-Resolution Modeling Techniques with Semiautomatic Data Acquisition

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.<br/>To that extent, this study has developed the...

Societal Aging and Health Care Systems: A model-based policy analysis across OECD countries

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...

Polarization in the Netherlands: System dynamics and data science to get a deeper understanding of public opinions in social groups

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...

Towards Explaining Automated Credit Decisions: The design of an Explicability Assessment Framework (EAF) for Machine Learning Systems

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,...