Computational Social Science Summer School on Democratic Debate › view all
Research Incubators on Data-driven Modeling of Conflicts, Migration, Social Cohesion, and Democratic Debate
The BIGSSS-CSS Summer School on Democratic Debate takes place from July 3 – 12, 2023 at the Constructor University (Bremen, Germany).
The summer school is conceptualized as a research incubator bringing together experts in computational social sciences, experts on the topic, and junior scholars to advance research by using data-driven modeling approaches. As for previous BIGSS-CSS summer schools, teams of young scientists and experienced scholars are formed to work through the entire research process on a specific topic. Find the exciting line-up of projects and experts here. The results of former summer schools were published as edited volumes and in scientific journals.
For more information, please visit the summer school website.
We expect democracy to enable us to utilize collective intelligence such that our collective decisions build and enhance our common welfare. In return, we accept their distributive and normative consequences. Collective decisions are produced by voting and/or dialogue. Voting procedures are those that somehow aggregate individual preferences and judgments. Individual preferences and judgments change as their underlying attitudes, values, and opinions change through discourse, discussion, and deliberation. In societies, these dynamics go beyond the scope of the individual – giving rise to emergent macroscopic phenomena, like consensus formation, bipolarization, issue alignment, and collective radicalization. Some of these dynamics may undermine democratic pluralism and may destabilize democratic institutions.
The projects involve data-driven modeling of opinion dynamics and democratic decisions using methods of data exploration, simulation, or prediction. They contribute to a deeper understanding of democratic debate and democratic decisions or look into the causes and consequences of political (dis)agreement. All projects have some relation to data and methods of computational social science such as social-network analysis, natural language processing, agent-based modeling and simulation, analysis of digital trace data or machine learning.