Tilburg University researchers receive grants worth 4.5 million from European Research Council

Belief systems, ideologies, values, and worldviews can inspire protest, certain voting behavior, prejudice, populist uprisings, and more. This research aims to understand political belief systems so that we can understand how belief systems contribute to these phenomena and how belief systems are likely to rise and fall, and change overtime. Brandt will use a new type of network analysis designed for psychological data that can estimate the internal structure of political belief systems. This means that we can observe how the different attitudes, values, ideals, and goals that make up a belief system fit together and how this structure differs over time, between people, and across countries. With this knowledge in hand, we may understand why some belief systems are more prone to radicalization than other ones, why belief systems develop differently in some countries, but similarly in others, why some belief systems tend to overlap, and how different historical events (e.g., recessions) change belief systems in a country. It may even be possible to encourage the benefits of belief systems and curtail the worst excesses.

Relational event history data are becoming increasingly available due to new technical developments. These data contain detailed information about who interacted with whom in a network and when. For example, interactions between colleagues, between teachers and students, and between criminal gangs in city districts. This new type of data has the potential to greatly contribute to our understanding of dynamic social networks by providing new insights about speed, rhythm, duration, and lag in social interactions. However a crucial problem is that statistical tools for analyzing such data are currently underdeveloped. Mulder will develop a new statistical framework for the analysis of relational event histories and make it freely available to ensure general utilization among social scientists. It will allow research into, for instance, how fast integration occurs among teams with workers from different cultures, how long it takes to develop respect in the classroom, or when violent interactions between criminal gangs will occur.

Places and populations that were previously digitally invisible are now part of a ‘data revolution’ that is being hailed as a transformative tool for human and economic development. Yet this unprecedented expansion of the power to digitally monitor, sort and intervene is not well connected to the idea of social justice, nor is there a clear concept of how broader access to the benefits of data technologies can be achieved without amplifying misrepresentation, discrimination and power asymmetries. We therefore need a new framework for data justice integrating data privacy, nondiscrimination and non-use of data technologies into the same framework as positive freedoms such as representation and access to data. This project will conceptualize data justice along three dimensions of freedom: (in)visibility, autonomy with regard to technology, and combating data-driven discrimination. The framework will then be tested and further shaped by debates held in nine locations worldwide.