The Science of the Predicted Human Talk Series: Professor Dean Eckles

Network structure can affect when, where, and how widely new ideas, products, and behaviors are adopted. Classic work in the social sciences has emphasized that “long ties” provide access to novel and advantageous information. In our empirical work, we show how particular life events (migration, education) are associated with forming long ties and how having long ties is associated with beneficial economic outcomes. Counties in the United States with more long ties (and more strong long ties) have higher incomes, lower unemployment, and more economic mobility, even after adjusting for other measures of social connections. These stylized facts are consistent with some models of contagion. In widely-used models of biological contagion, interventions that randomly rewire edges (generally making them “longer”) accelerate spread. However, there are other models relevant to social contagion, such as those motivated by myopic best-response in games with strategic complements, in which individuals adopt if and only if the number of adopting neighbors exceeds a threshold. Recent work has argued that highly clustered, rather than random, networks facilitate spread of these “complex contagions”. Here we show that minor modifications to this model, which make it more realistic, reverse this result: we allow very rare below-threshold adoption, i.e., rarely adoption occurs when there is only one adopting neighbor. In a version of “small world” networks, allowing adoptions below threshold to occur with order 1/√n probability — even only along some “short” cycle edges — is enough to ensure that random rewiring accelerates spread. Hypothetical interventions that randomly rewire existing edges or add random edges (versus adding “short”, triad-closing edges) in hundreds of empirical social networks reduce time to spread.

 

 

About Dean Eckles

Dean Eckles is a social scientist and statistician. At the Massachusetts Institute of Technology, Dean Eckles is the Mitsubishi Career Development Professor, an associate professor in the Sloan School of Management, and affiliated faculty at 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. He also works on methods for inferring cause–effect relationships and on applied statistics more generally. Dean Eckles’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 and completed five degrees, including his PhD, at Stanford University.

 

 

The Predicted Human

Being human in 2023 implies being the target of a vast number of predictive infrastructures. In healthcare, algorithms predict not only potential pharmacological cures to disease but also their possible future incidence of those diseases. In governance, citizens are exposed to algorithms that predict – not only their day-to-day behaviors to craft better policy – but also to algorithms that attempt to predict, shape and manipulate their political attitudes and behaviors. In education, children’s emotional and intellectual development is increasingly the product of at-home and at-school interventions shaped around personalized algorithms. And humans worldwide are increasingly subject to advertising and marketing algorithms whose goal is to target them with specific products and ideas they will find palatable. Algorithms are everywhere – as are their intended as well as unintended consequences. The series is arranged with generous support by the Villum Foundation and the Pioneer Center for Artificial Intelligence.