In my work, I explore the dynamics of the emergence of shared understandings by quantifying both direct (network-related) and indirect sources of information about local social order. I draw on cultural and information theory, cognitive science, and dynamical systems research, and have used formal modeling, social network analysis, behavioral experiments, and computational text analyses. My dissertation work focuses on the consequences of indirect, extra-network sources of information about others’ activities. The first paper mathematically links a canonical model of self-organization to the evolutionary concept of niche construction to show that the addition of indirect, niche-facilitated communication allows for more complex social dependencies to emerge. The second paper quantifies the informational value of an indirect source in a large-group coordination experiment, finding it can help enable successful coordination and yields more information than additional weak ties. Finally, I use a cutting-edge form of topic modeling to find evidence that users of an online book review-and-discussion forum collectively produce the public meanings of works of literary fiction. The manuscript from the second project is currently under review and I have work published in Sociological Methods and Research and the general science journal PLOS One.
Building on the above research, I am working on several other projects. Along with Beth Redbird, I have access to the last 18 months of cellphone geotracking data for an approximately-representative sample of 5% of the population of the United States. My next step is to construct the individuals-to-places (i.e. ecological) networks to estimate the degree to which our use of physical space crosscuts—in the sense of Blau and Schwartz—other forms of affiliation such as race and class. The more crosscutting exists, the more potential for individuals to interact indirectly and learn about the behaviors of otherwise socially distant individuals. Similar data sources have been used to understand patterns of human mobility, but only in isolation and not in reference to macroscopic social patterns. Future extensions will focus on crosscutting in contexts like workplaces and public spaces, such as parks and commercial districts.
Another major area of research of mine in the coming years will be clarifying the usefulness of textual data for understanding mental representations in individuals and groups. Pragmatist philosophers and linguists have argued there is a complex relationship between the representations individuals have and the text they produce; text and speech most often expounds on what is not obvious to readers and interlocutors, with obvious or otherwise shared meanings often omitted. There is also a great deal of heterogeneity in representations when looking at populations of the size typically used in computational analyses of text. These two features of cognition and language are extremely important for how sociologists think about culture and cognition, but are downplayed in current computational approaches to text. With Lynette Shaw, I am developing a formal model of these features and an experimental protocol for assessing the relationship between individuals’ representations and the text they produce. The protocol will link survey items and text from a group interactive text-producing process and, I believe, provide important insights into the strengths and weaknesses of textual data for inferring the mental representations of both individuals and groups.
I use a variety of methods to carry out this research agenda. It is very much motivated by insights I've gain through mathematical and agent-based modeling, but the natural limitations of those approaches have lead me to use behavioral experiments, more traditional statistical analyses, and newer data science techniques. I've found that each approach has informed the others and I am now a strong proponent of methodological eclecticism.
The research map on my homepage provides an overview of my current projects and I will soon add more details here.
I have experience teaching innovative, hands-on courses in computational social science. I started developing these materials when I taught my own undergraduate course at the University of Michigan and have honed them through standalone workshops and a graduate short-course I led this summer at Northwestern. I am really excited about teaching workshops on computational skills because I see them having a role in everyone's workflow and as useful for all sorts of research approaches.
I have also been a teaching assistant for a range of undergraduate courses and can teach undergraduate and graduate courses in culture and knowledge, economic sociology and organizational theory, complex systems, statistics, social networks, experimental design, and agent-based modeling.
I did my doctorate in sociology at the University of Michigan, where I was an NSF IGERT fellow with the Center for the Study of Complex Systems. I am now a Data Science Scholar at Northwestern University. My interests are ever-evolving, but I generally study the social dimensions of cognition and the dynamic emergence of social order using computational and mathematical techniques. I studied Math, Economics and Philosophy at the University of Wisconsin and worked in the restaurant industry before starting my career.
My dissertation committee was Mark Mizruchi (Sociology, co-chair), Elizabeth Bruch (Sociology, co-chair), John Padgett (Political Science, University of Chicago), Robert Savit (Physics), and Scott E. Page (Complex Systems and Economics).
My email is atwell at northwestern.edu
I don't really tweet as jon_atwell