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Mathematical Sociologist Jane Sell Texas A&M ([email protected]) VOLUME 16, ISSUE 1 FALL/WINTER 2012 - 2 0 1 3

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Mathematical Sociologist Jane Sell Texas A&M (J-Sell@Tamu.Edu) VOLUME 16, ISSUE 1 FALL/WINTER 2012 - 2 0 1 3 Section Officers: Chair Noah Friedkin University of California, Santa Barbara ([email protected]) Chair-Elect Mathematical Sociologist Jane Sell Texas A&M ([email protected]) VOLUME 16, ISSUE 1 FALL/WINTER 2012 - 2 0 1 3 Past Chair Katherine Faust University of California, Irvine Comments from the Chair...Noah Friedkin [email protected] The Section Chair is given the op- t+1 opinion is taken as an average Secretary-Treasurer John Skvoretz portunity to make introductory of the individual’s own opinion University of South Florida ([email protected]) comments. I did this once before and those other individuals’ time t in our Spring 2003 Newsletter. I opinions, which are within the Council Members find that a particular concern ex- individual’s confidence bound. Alison Bianchi University of Iowa pressed then remains a concern. I ([email protected]) wrote, after noting the influx of There is a substantial empirical Matthew Brashears natural scientists into research on literature on the relationship of Cornell University opinion differences and interper- ([email protected]) social networks, “Never before has there been a more pressing sonal influences. Social psycholo- Peter Burke University of California, demand for sociologists who com- gists have been examining the imaginations. My experience Riverside relationship since the 1950s. Eu- ([email protected]) bine strong mathematical skills with models is captured by a with deep substantive grounding gene Johnsen and I took a close lithograph which shows a log- James D. Montgomery ger, ax in hand, looking at a University of Wisconsin-Madison in particular fields. Mathematical look at this literature (Friedkin & ([email protected]) Johnsen 2011). Obviously, the tree that he has just cut models of sociological phenome- through: the tree, instead of Matthew J Salganik development of models of opinion Princeton University na are unlikely to make an endur- toppling over, has floated ([email protected]) ing contribution if they are not dynamics is greatly simplified if upwards and the logger is Arnout van de Rijt informed by the refined substan- the information in a group’s time t standing dumbstruck at the SUNY Stony Brook University opinion vector suffices to specify result. An interaction of theo- (Arnout.VanDeRijt@ tive sensibility that usually comes ry and experiment is useful in stonybrook.edu ) from systematic reading in sociol- the group’s time t influence net- disciplining mathematical ogy and intense involvement with work. It is not surprising that Student Member modelers have been attracted to imagination. Ashton Verdery data.” University of North Carolina such a specification for over half a Chapel Hill INSIDE THIS ISSUE: ([email protected]) The field of opinion dynamics, century. The empirical evidence with which I’m associated, is rap- does not support the threshold Comments from the 1 Newsletter Editor idly growing. An increasingly assumption of bounded confi- Chair Pamela Emanuelson North Dakota State University prominent line of work in this dence models. The evidence sup- 2012 Section Awards 2 ([email protected]) field deals with opinion dynamics ports a weighted averaging as- Donna Lancianese under bounded confidence; e.g., sumption in which the weights Highlights 3-4 University of Iowa ([email protected]) Hegselmann & Krause (2002). The accorded to particular persons are Joint Japan-North Ameri- 5-7 bounded confidence assumption independent of opinion differ- Webmaster is that an interpersonal influence ences. The line of work on opin- Matthew Brashears Cornell University relation “v influences u” will exist ion dynamics under bounded con- Section Report 8-12 ([email protected]) if and only if the time t opinion fidence is based on a false prem- Conference 13 difference of u and v does not ise. Announcements exceed a fixed threshold value for u. Hence, the influence network There is no lack of imagination in Call For Nominations 14-15 the field in constructing models. may change as its members’ opin- However, there is a severe lack of Editor’s Comments 16 ions change. An individual’s time discipline on investigators’ VOLUME 16, ISSUE 1 P A G E 2 John Skvoretz University of South Florida Committee Members: Jane Sell (chair), Arnout van de Rijt, Bob Hanneman Andreas Wimmer and Kevin Lewis “Beyond and Below Racial Homophily: ERG Models of Friendship Networks Documented in Facebook” American Journal of Sociology Committee Members: Noah Friedkin (chair), Matthew Salganik, Damon Centola, Emma Spiro David Melamed and Scott Savage “Predicting Social Influence: A Model at the Intersection of Sociology and Psychology” Committee Members: Alison Bianchi (chair) Will Kalkhoff, Omar Lizardo P A G E 3 HIGHLIGHTS: Outstanding Article Award Winners Andreas Wimmer Most of my work over the past 20 years deals with pro- cesses of ethnic group formation and nation building. While much of the ethnicity literature has been pre- occupied with ontological/definitional issues (is it a matter of identity or interest, is it stable or contextually fluid, etc.), I try to develop a systematic comparative account that focuses on variation in the properties of ethnic categories (see my new book “Ethnic Boundary Making”, forthcoming at Oxford University Press). In order to do this properly, we need to disentangle ethnic group formation processes (the consequences of in- Andreas Wimmer and Kevin Lewis group preferences and the discrimination against oth- accept the outstanding article ers) from other social processes. This is also the gist of award, Denver, CO 2012 the paper co-authored with Kevin Lewis. We try to show that the racial homogeneity of social networks shouldn’t automatically and uncritically be associated with same-race preference (or racial homophily). Ra- ther, there are a host of processes that can lead to homogenous networks that are analytically and empirical- ly distinct from racial homophily. Once you bring these other network formation processes into the picture, you realize that racial homophily is not the main force of network formation—contrary to what much of the literature on homophily assumes. Kevin and I were part of a team of researchers at Harvard (where I was teaching as a visiting professor) that developed a new dataset on the Facebook pages of a cohort of college students. Kevin, at that time a gradu- ate student in the department, did the heavy lifting in creating the dataset. I teamed up with him to do a study on ethnic and racial homophily, and we had research assistants code the ethnic and racial background of all students in the dataset. Both Kevin and I were novices when it comes to network methods. Kevin delved deeply into the matter and found the Exponential Random Graph approach and statnet as the corre- sponding program. We then learned how to produce and interpret results. It was a fantastic discovery, since so many of the things we wanted to do could be done with this approach: disentangling homophily on vari- ous, segmentally nested levels of categorization from each other (ethnic groups nested into racial catego- ries), disentangling homophily from other network formation processes (sorting and self-selection into op- portunities to meet, triadic closure and reciprocity) that influence levels of homogeneity, and so on. We worked on this for five years and were extremely pleased and proud when we were noticed that we won the best paper award from the Mathematical Sociology Section. All along the way, we received a lot of support, advise, and technical help from many, many section members, including the producers of statnet. Since we were newcomers to the field and did not have any friends to rely upon or favors waiting to be returned, we were amazed by the generosity and openness with which mathematical sociologists reacted to our many re- quests and demands—thanks indeed! VOLUME 16, ISSUE 1 Kevin Lewis P A G E 4 My path to mathematical sociology has been a circuitous one. Inspired by math teachers in high school, I de- clared a double major in math and sociology my freshman year at UC San Diego and hoped to become a high school teacher myself. Unfortunately, it turns out math is a lot more fun when you understand it: I breezed through lower-division calculus courses, but the upper-division stuff was completely over my head. Mean- while, I became more and more interested in sociology and in teaching at the college level. So, at age 19, I switched to a sociology/philosophy double major; downgraded my math ambitions to a minor; and, tired of numbers, decided I would go to grad school to become a qualitative sociologist (my undergraduate thesis, for instance, was an interview-based study of cosmetic surgery in California). Now, 10 years later, Andreas and I are winning this award. What happened??! I met Andreas shortly after arriving at Harvard, where he was visiting and taught my cohort contemporary theory. Meanwhile, Jason Kaufman and Marco Gonzalez invited me to join a nascent project on taste subcultures among college stu- dents, drawing on data from Facebook (which was then only a couple years old). Andreas was independently interested in working with Facebook, but instead to look at ethno-racial homophily. Our teams joined forces to create the dataset—and, several years later, each strand of research finally bore fruit: the paper with An- dreas on racial homogeneity (AJS 2010) and a paper with Jason and Marco on selection and influence (PNAS 2012). My return to numbers, then, was motivated more by necessity than interest; if we were going to look at friendship formation we would need to use network tools. I continue to be grateful to Ann McCranie for in- troducing me to ERGMs during an ICPSR summer session, which appeared to be the sole method capable of adequately disentangling the issue Andreas and I were interested in (how is racial homogeneity generated?).
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