My core research interest is in social spillovers – the role of an initial interaction on downstream social connections – which I approach from two distinct but interconnected areas: human-robot interactions and social networks (presented separately below). Looking at individuals and one-on-one interactions alone cannot and will not provide a comprehensive perspective on the behavior and choices of the people who form networks. As a sociologist who specializes in computational social science, my work uses experimental methods to collect primary data that allows me to explore a wide variety of social spillovers in a standardized and replicable environment that allows for strong causal inference.
Human-robot interaction research entails the study of the complex interplay of human behavior with, and in the presence of, robots and other similar technologies. The role of robots, and AI in particular, is increasingly pervasive. Yet much of the conversation around the use of these technologies is focused on the direct benefits of collaborations between humans and machines. The spillovers of these interactions are frequently overlooked. These projects explore how robots can not only influence how humans interact with robots directly, but how they can affect human-human interactions. My research uses experimental methods to explore the effect of adding AI, (ro)bots in particular, to networks of humans to observe how they shape downstream human-human interactions in hybrid systems of humans and machines. For instance, one of my papers examined whether robot utterances were able to change the verbal communication between human team members. The robot either made vulnerable, neutral, or no statements at the end of each round of the game. (Video of the experimental design can be found below.) We found that groups with a vulnerable robot spoke to each other twice as much, distributed their speech somewhat more equally, and described their groups as being more positive. This research was published in 2020 in Proceedings of the National Academy of Sciences and was chosen for the cover.
Ashley L. Harrell†, Margaret L. Traeger†. “Evidence of spillovers from (non)cooperative human-bot to human-human interactions.” Forthcoming, iScience. †Equal Authorship
Harrell, Ashley, and Margaret L. Traeger. “Reputation-based reciprocity in human–bot and human–human networks.” PNAS nexus 4, no. 5 (2025): pgaf150.
Traeger, Margaret L., Sarah Strohkorb Sebo, Malte Jung, Brian Scassellati, Nicholas A. Christakis. “Vulnerable Robots Positively Shape Human Conversational Dynamics in a Human-Robot Team.” Proceedings of the National Academy of Sciences 117, no. 12 (2020): 6370-6375.
Strohkorb Sebo, Sarah, Margaret Traeger, Malte Jung, and Brian Scassellati. “The Ripple Effects of Vulnerability: The Effects of a Robot's Vulnerable Behavior on Trust in Human-Robot Teams.” In Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 178-186. ACM, 2018
Network science is both a method and a theory for understanding and interpreting how connections (ties) between entities (nodes) influence network function. We are all in interacting with, and are exposed to, multiple interconnected aspects of our social worlds simultaneously. My social networks research, which was partly funded by the National Science Foundation, explores how social networks, and where they are positioned in space, influences health outcomes. To do this, I have predominately used data from a novel, longitudinal randomized controlled trial of nearly 25,000 respondents in 176 villages in the mountainous countryside of western Honduras. These villages vary in many ways, including in their population size, access to transportation, wealth, average age of respondents, and health. My dissertation analyzed the structure and function of geographically contextualized real-world social networks. It is inevitable that the spatial context of one’s social environment will impose constraints on social interactions, and I attempted to establish how spatial and social worlds jointly and separately affect health and well-being.
Shakya, Holly B., Jessica M. Perkins, Margaret Traeger, Alexander C. Tsai, David R. Bangsberg, Bernard Kakuhikire, and Nicholas A. Christakis. “Social network correlates of IPV acceptance in rural Honduras and rural Uganda.” SSM-Population Health no. 4 (2018): 236-243.
Bromage, Sabri, Enkhmaa Gonchigsumlaa, Margaret Traeger, Bayarbat Magsar, Qifan Wang, Jorick Bater, Hewei Li, and Davaasambuu Ganmaa. “Awareness and Attitudes Regarding Industrial Food Fortification in Mongolia and Harbin.” Nutrients 11, no. 1 (2019): 201.
Bromage, Sabri, Bernard Rosner, Janet Rich-Edwards, Davaasambuu Ganmaa, Soninkhishig Tsolmon, Zuunnast Tserendejid, Tseye-Oidov Odbayar, Margaret Traeger, and Wafaie Fawzi. “Comparison of Methods for Estimating Dietary Food and Nutrient Intakes and Intake Densities from Household Consumption and Expenditure Data in Mongolia.” Nutrients 10, no. 6 (2018): 703.