Talk Title: Network Effects on Outcomes and Unequal Distribution of Resources
Watch Eaman’s Research Lightning Talk
Talk Abstract: We study how networks affect different groups differently and provide pathways to reinforce existing inequalities. First we provide observational evidence for differential network advantages in access to information: individuals from the low status group receive lower marginal benefit from networking than the high status group. Second, we provide causal evidence for differential diffusion of a new behavior in the network, mainly driven due to homophily and slight initial advantages of a group. Third, we develop a theoretical network model that captures the network structure of unequal access to opportunities. We show that any departure from the uniform distribution of links to information sources among members of a group limits the diffusion of information to the group as a whole. Fourth, we develop an online lab experiment to further study the network mechanisms that widen inter-group differences and yield different returns on social capital to different groups. We recruit individuals to play an online collaborative game in which they have to find and dig gold mines and in the process can pass information to their network neighbors. By changing the network structure and composition of groups with low and high initial advantage, we generate the processes that lead to unequal distribution of opportunities, beyond what’s expected by individual differences. Finally, we contribute to the literature on network structure and performance and propose the concept of bandwidth-diversity matching: individuals who match the tie strength to their contacts with their information novelty achieve truly diverse networks and better outcomes.
Bio: I am a PhD candidate in the Social and Engineering Systems program at MIT IDSS, under supervision of Prof. Pentland and Prof. Eckles. I am also receiving a second PhD in Statistics from the Statistics and Data Science Center at MIT. I received my Bachelor’s and Master’s degrees in Computer Science both from the University of Michigan – Ann Arbor.
My PhD research is focused on micro-level structural factors, such as network structure, that contribute to unequal distribution of resources or information. As a computational social scientist, I use methods from network science, statistics, experiment design and causal inference. I am also interested in understanding the collective behavior in institutional settings, the institutional mechanisms that promote cooperative behavior in networks, or in contrast lead to unequal outcomes for different groups.
In a previous life, I worked at Google New York City as a software engineer from 2011 to 2015. Currently, I am also a research contractor at Facebook working on how networks affect economic outcomes.