Stochastic Network Modelling as Generative Social Science - Dr Christian Steglich
Stochastic models of sociocentric networks were developed for testing hypotheses about micro-level dependencies (e.g., clustering, preferential attachment, or homophily) on the basis of empirical network data. Due to the complex nature of sociocentric networks, parameter estimates of these models are typically obtained by simulation-based inference. This opens up the possibility of using these models as simulation tools, and study emergent macro-level phenomena with them. The combination of fitting the models to empirical data sets and using them to explain macro-level outcomes renders these models powerful tools for sociological inquiry into interdependent social systems. In this presentation, the use of exponential random graph models and stochastic actor-oriented models as generative models for such networked social systems is discussed. As illustration, the case of achievement segregation in a highly competitive setting of tertiary education will be investigated, paying special attention to the relative contributions of peer influence processes and partner selection processes to the overall segregation level.
Christian Steglich is a recognised world expert on the joint analysis of Social Influence (contagion) and Network dynamics and wrote the seminal paper “Dynamic Networks and Behavior: Separating Selection from Influence”. He has joint positions at the Institute of Analytical Sociology, IAS, (Sweden) and the University of Groningen (Netherlands). His work is broadly concerned with the study of ‘social mechanisms’ and the link between micro-processes and macro outcomes, a field inspired by the work by the Nobel-prise laureate in Economics, Thomas Schelling. Christian is one of the core developers of stochastic actor-oriented models (SAOM) for longitudinal network analysis and their implementation in the RSiena R-package. Currently he is working on a long-awaited introductory book on SAOM with Tom Snijders.
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