Themes
Our themes reflect the range of approaches and advanced methods employed across the institute in social and political research.
This theme explores how complex social structures emerge, evolve, and shape outcomes across contemporary societies. It focuses on understanding how relationships between individuals, groups, organisations, and institutions produce patterns of inequality, power, and opportunity, and how these dynamics are being reshaped in an increasingly digital and AI-mediated world.
As digital technologies and artificial intelligence become more embedded in everyday life, the theme explores how these developments are transforming how we communicate, organise, and participate in society. It asks how we can support fairer, more inclusive systems—particularly as new forms of power and inequality emerge in digital environments.
It brings together insights from sociology, political science, and organisational studies to examine how networks, institutions, narratives, and technologies co-evolve over time. By treating meanings, classifications, and discourse as central components of social structure, the theme captures how interpretation and interaction shape both stability and transformation.
Methodologically, the theme advances innovative quantitative and computational approaches capable of capturing complex, multi-level dynamics. These include network analysis, simulation modelling, AI-informed statistical methods, and integrated mixed-method designs combining relational, textual, and survey data. Such approaches enable researchers to link micro-level interactions with broader social structures and long-term change.
This theme is led by Philip Leifeld.
This theme focuses on improving how we understand populations by bringing together different types of data, including surveys, administrative records, census data, and emerging digital sources. By combining these in thoughtful and responsible ways, the theme aims to produce more accurate, inclusive, and timely insights into social and demographic change.
A key priority is addressing the limitations of existing data systems. Declining survey response rates, under-representation of minority and hard-to-reach groups, missing data, and challenges in linking data sources all affect the quality and reliability of population statistics. As traditional census approaches evolve, future population estimation systems are likely to rely more heavily on linked administrative data and advanced statistical modelling.
Within this context, surveys remain central. Rather than being replaced, they are repositioned as essential tools for anchoring, validating, and interpreting complex data infrastructures. Surveys provide crucial insight into people’s attitudes, identities, and experiences—dimensions that are often absent from administrative and digital data. At the same time, linked data can enhance coverage, reduce burden on respondents, and enable more timely and detailed analysis.
This theme also places strong emphasis on improving how diverse populations are captured in data. It seeks to develop approaches that are sensitive to differences in ethnicity, gender, sexuality, disability, migration status, and family structure, and to address the systematic under-representation of marginalised groups in conventional data sources.
This theme is led by Francesco Rampazzo.
This theme addresses the methodological and societal challenges arising from the rapid growth of online/process-produced digital data and from the embedding of AI systems in everyday social life.
The theme recognises that digital traces, texts, and platform data are not neutral reflections of behaviour, but socially embedded products of interaction, institutional design, and symbolic systems. It therefore seeks to develop computational approaches that are explicitly sensitive to context, meaning, power, and inequality, and that can be integrated with surveys, networks, and qualitative materials within understanding–explanatory designs.
At the same time, this theme places the social study of AI at its core, examining how AI systems reshape communication, labour, governance, knowledge production, and social relations, and how humans and institutions adapt to, contest, and co-evolve with these technologies. But also how AI systems are themselves communication systems and how they implement social and dialogic forms of learning and knowledge creation.
This theme is led by Yan Wang.