Our interdisciplinary research spans six interconnected domains, investigating how data technologies and artificial intelligence are reshaping innovation, creativity, and economic systems. From data access and scientific discovery to the economics of digital platforms and pharmaceutical innovation, we examine the profound implications of the data revolution for society.
Our research in this area examines how data availability and access fundamentally shape research processes, scientific discovery, and knowledge production across disciplines. We investigate the barriers and facilitators to data access, studying how restricted access to confidential microdata affects the scope and quality of economic research. Our work explores the democratizing effects of improved data access on scientific participation, examining how opening data repositories can diversify the researcher community and accelerate scientific progress. We also analyze the adoption patterns of new data sources and their diffusion through academic communities, providing insights into how data infrastructure investments can maximize scientific impact.
This research stream focuses on understanding the capabilities, limitations, and broader implications of artificial intelligence for knowledge creation, scientific research, and innovation processes. We examine how large language models and other AI technologies are transforming research methodologies, from hypothesis generation to data analysis and theory development. Our work investigates the competitive dynamics in the AI industry, analyzing how openness and control mechanisms affect innovation trajectories. We also study the economic value of data in AI systems, exploring how different data sources contribute to model performance and the implications for data markets and intellectual property rights.
Our research in this domain examines how digital platforms and online communities facilitate knowledge sharing, collaboration, and innovation processes. We investigate the mechanisms through which platforms enable collective knowledge production, studying cases like Wikipedia, OpenStreetMap, and crowdsourcing platforms. Our work explores how platform design features, incentive structures, and community governance affect participation and knowledge quality. We analyze the impact of AI technologies on these platforms, examining how automated systems change the dynamics of human collaboration and the nature of work performed on crowdsourcing platforms.
This research stream investigates the spatial dimensions of innovation and knowledge creation, examining how geographic factors influence research productivity, collaboration patterns, and technological development. We study the role of location-based resources, regional innovation ecosystems, and spatial clustering in driving innovation outcomes. Our work analyzes how physical proximity affects knowledge spillovers, the formation of innovation networks, and the concentration of innovative activities in specific regions. We also examine how digital technologies and data infrastructure can reshape geographic advantages in innovation, studying cases where satellite data, mapping technologies, and location-based services have democratized access to geographic information and enabled new forms of spatial analysis.
Our research in this area examines how intellectual property rights, copyright laws, and legal frameworks shape innovation processes, creativity, and the diffusion of knowledge across society. We investigate the complex balance between providing incentives for innovation through exclusive rights and ensuring broad access to knowledge for cumulative innovation and social benefit. Our work analyzes the effects of copyright protection on creative reuse, studying how digitization and new technologies challenge traditional IP frameworks. We examine patent systems across different historical periods and countries to understand how IP institutions evolve and affect innovation patterns. This research stream also explores the intersection of AI technologies with IP law, investigating questions around AI-generated content, data rights, and the ownership of knowledge produced by machine learning systems.
This research stream focuses on understanding how data-driven approaches are transforming pharmaceutical innovation, drug discovery processes, and health research methodologies. We examine how large-scale biomedical datasets, genomic information, and computational tools are reshaping the landscape of medical innovation. Our work investigates the role of data availability in directing research attention, studying how access to different types of biomedical data influences which diseases receive research focus and which therapeutic approaches are pursued. We analyze the impact of data-driven search strategies on scientific discovery, examining cases where big data approaches have led to unexpected insights or novel therapeutic targets. This research also explores the organizational and strategic aspects of pharmaceutical innovation, including how firms respond to data quality issues, how venture capital influences experimental approaches in drug development, and how regulatory frameworks adapt to data-intensive innovation processes.