Bio – Dean Eckles

Dean Eckles is a social scientist and statistician. At the Massachusetts Institute of Technology, Dean is an associate professor in the Sloan School of Management and associate director of the Institute for Data, Systems & Society in the Schwarzman College of Computing. Much of his research examines how interactive technologies affect human behavior, especially by mediating social influence. Dean also works on methods for inferring cause–effect relationships and on applied statistics more generally. Dean’s empirical work uses observational studies and field experiments involving hundreds of millions of people. His papers appear in Journal of the American Statistical Association, Proceedings of the National Academy of Sciences, Nature, Science, Management Science, and other peer-reviewed journals and proceedings in statistics, computer science, and marketing. He is co-organizer of the Conference on Digital Experimentation (CODE@MIT) and serves as associate editor for two departments at Management Science. He was previously a scientist at Facebook and Nokia. Dean completed five degrees, including his PhD, at Stanford University.

If you want more information, see this recent profile, my CV, and my disclosures.

If I have accepted a speaking invitation, you may use this bio and these photos in connection with that event.

Featured work

Long ties, disruptive life events and economic prosperity. PNAS.
50 years after Granovetter’s “The Strength of Weak Ties”, we provide new evidence of the connection between network structure and economic prosperity. We also show how life events, like switching high schools, can result in more long ties, even decades later. This paper is accompanied by new public data on the Facebook networks of people in US and Mexican zip codes .

Algorithmic transparency and assessing effects of algorithmic ranking. Testimony before the US Senate Subcommittee on Communications, Media, and Broadband.
This addresses the questions: How does algorithmic ranking in social media work? How can we assess algorithmic impact & amplification? What can policy-makers do here?

Seeding with costly network information. Operations Research.
Because of their position in a social network, some people can be much more influential than others, and so marketers and others often want to target them as “seeds”. But, outside of specific cases, one doesn’t fully observe the social network, but must probe it (with costly surveys or crawling). We develop techniques for selecting good seeds in arbitrary networks while limiting how much we need to probe the network.

Providing normative information increases intentions to accept a COVID-19 vaccine. Nature Communications.
When does sharing information about others’ behaviors cause more of that same behavior? This is both a central question in the social sciences and also important for effecting preventative health behaviors, including vaccination. We use a randomized experiment embedded in the international survey on COVID-19 we conducted with Meta and JHU to address this, providing evidence that these descriptive norms increase intentions to get vaccinated.