University of Michigan School of Information
301 - Models of Social Information Processing
Models of Social Information Processing --- This course focuses on how social groups form, interact, and change. We look at the technical structures of social networks and explore how individual actions are combined to produce collective effects. The techniques learned in this course can be applied to understanding friend systems like Facebook, recommender systems such as Digg, auction systems such as Ebay, and information webs used by search engines such as Google. This course introduces two conceptual models, networks and games, for how information flows and is used in multi-person settings. Networks or graph representations describe the structure of connections among people and documents. They permit mathematical analysis and meaningful visualizations that highlight different roles played by different people or documents, as well as features of the collection as a whole. Game representations describe, in situations of interdependence, the actions available to different people and how each person's outcomes are contingent on the choices of other people. It permits analysis of stable sets of choices by all the people (equilibrium's). It also provides a framework for analysis of the likely effects of alternative designs for markets and information elicitation mechanisms, based on their abstract game representations. Assignments in the course include problem sets exploring the mechanics of the models and essays applying them to current applications in social computing.