Graduate Workshop in Computational Social Science
Selcan Mutgan is a Ph.D. candidate at the Institute for Analytical Sociology, and an upcoming visiting SCANCOR fellow at Stanford University. She has an economics and demography background, and currently works on projects related to ethnic segregation and income stratification. Her work together with Marc Keuschnigg and Peter Hedström on urban scaling has been published in Science Advances. Mutgan attended the Graduate Workshop in Computational Social Science (GWCSS) in 2018. Read more about her work on her personal website and follow her on Twitter here.
Briefly describe your primary research/academic work or other professional work.
After receiving my B.Sc. in economics, I slowly transitioned to sociology. First, I received my masters in Demography at Stockholm University, and then started my Ph.D. in Analytical Sociology at Linköping University in Sweden. The projects I am currently working on are mostly related to school and residential segregation, as well as income stratification. Working in Sweden has its perks for sociologists, as we have access to full population administrative records that span over five decades. Access to that type of big data provides a unique opportunity to work on the connection between micro level behavior and macro level aggregate outcomes. In different projects, I address this link between micro and macro using agent-based modeling, social network analysis, as well as choice models.
In what ways does the study of complexity science influence your thinking about your current work?
I find complexity quite helpful in my own research. For instance, school and residential segregation is an outcome of a complex social process. There is interdependency between individuals’ actions, interdependency between residential choices and school choices, there is path dependency, emergence, and heterogeneities. As Phil Anderson wrote: “More is different”. That is, the social mechanisms that drive school segregation are not simply reducible to individuals’ school choices or their characteristics. Schelling's model of segregation which shows how even very weak in-group preferences can lead to high levels of segregation still is a wonderful example of the importance of addressing the complex processes through which micro level actions and macro level outcomes are linked to one another.
More personally, the multidisciplinary nature of complexity science fascinates me. I still remember the feeling when I first read Mitchell Waldrop's Complexity: The Emerging Science at the Edge of Order and Chaos and being amazed by how scientists from different fields came together and built an institution that continues to produce impressive research. That kind of freedom in scientific thinking is one of the most inspiring things that I can think of when it comes to complexity science.
How did your experience at GWCSS impact your professional (or personal) perspective?
My time at SFI had a considerable impact on me and my research. I had the chance to spend two weeks with a group of very motivated and brilliant peers with whom I am still in contact. That period of intense creative interaction was one of the most enjoyable times of my Ph.D. education, and it will always stay with me. More tangibly, the discussions I had with our instructors later proved pivotal in shaping the theoretical background for a grant proposal together with Maria Brandén at the Institute for Analytical Sociology for which we eventually received four-year funding from the Swedish Research Council.
What interests do you have that might surprise your colleagues?
I do acrylic fluid art. To me, fluid art feels like doing agent-based models on a canvas. Even small “micro” tweaks can lead to very different paintings.
Thisinterview was conducted in August of 2021