Abstract: Institutionalized social learning can lead to cumulative cultural evolution and collective intelligence. Science is perhaps the signature example of this distinctly human strategy. In this talk, I develop a view of science as the social production of collective intelligence. Using data from millions of scientific papers, I illustrate how scientists use social cues to select research problems and show how these heuristics lead to more (and less) efficient discovery. I then show how diverging jargon and interests create “cultural holes” that modulate the flow of ideas between scientific fields. Finally, I describe a simple game-theoretic model of scientific problem choice and use it to show that taken-for-granted features of scientific institutions (like the publication of partial results) can have unexpected collective consequences. I draw together these results using ideas from computational learning theory to suggest how scientists’ strategies, though objectively adapted to social goals and human limitations, nonetheless support robust collective learning about the natural world. This view of science substantially complicates the task of crafting science policy, while highlighting the critical contributions that computational social science and formal modeling can make to the vitality of this essential institution.

Bio: Jacob G. Foster is an Assistant Professor of Sociology at the University of California, Los Angeles. He is a computational sociologist interested in the evolution and population dynamics of ideas. His work blends computational methods with qualitative insights from science studies to probe the strategies, preferences, and social processes that shape the production and persistence of scientific ideas. He also develops formal models of the structure and evolutionary dynamics of ideas and institutions. He is the co-founder of the Metaknowledge Research Network, funded by the John Templeton Foundation. His work has appeared in American Sociological Review, Science, Proceedings of the National Academy of Sciences, Sociological Science, and Social Networks.