My core research interest is in social spillovers – the role of an initial interaction on downstream social connections – which I approach from three distinct but interconnected areas: human-robot interactions, social networks, and stratification (presented separately below). I examine these questions across both human-human and human-machine interactions, with particular attention to how features of the interaction itself and the environments in which it occurs shape social behavior and connection. My work is motivated by a desire to understand what shapes the structure and function of social interactions, as well as the consequences those interactions have for our relationships with future alters. I predominantly use experimental methods to collect primary data, allowing me to explore social interactions under standardized and reproducible conditions while making strong causal inferences.
In my work on human-robot interactions, I don't focus on how humans use AI and related technologies, but on how these technologies influence the relationships between other actors within groups. In my other work, I study how the geographic and geodesic properties of networks jointly and separately affect health and well-being. This work also naturally extends to the study of stratification, with a particular focus on how social categories are reified and reinforced through organizational and technological structures and practices.
A copy of my CV can be found here.
My published work can be found here.
Data from my work can be found on the Open Science Framework and Research Box.
If there is something you'd like to see but cannot find, please email me at margaret.traeger [at] nd.edu!
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 and additional commentary.