This event will take place in beautiful Barcelona (Spain) on September 16-20, 2013. Call for contributions http://www.covenant2013.com/?page_id=25
In recent years, analysis of data from social media has provided a wealth of information about phenomena at societal scale, at least to the extent to which interactions, intentions and beliefs measured on-line reflect their real-world counterparts. Data from Twitter, Facebook, Google+, and Weblogs in general have been used to predict elections, opinions and attitudes, movie revenues, andoscillations in the stock market, to cite few examples. Similar data provided insights into the mechanisms driving the formation of groups of interests, topical communities, and the evolution of social networks. They also have been used to study polarization phenomena in politics, diffusion of information and the dynamics of collective attention.
In parallel, and even preceding the surge in interest towards social media, the area ofAgent-based Modeling (ABM) has grown in scope, focus and capability to produce testable hypotheses, going beyond the original goal of explaining macroscopic behaviors from simple interaction rules among stylized agents.
Although the Complex Networks community, that studies social media, and the ABM community, that simulates society as groups of interacting agents, have similar focus and a large overlap in interests, they are still separated from a profound chasm in their methodological approach. Network scientists have strongly leveraged empirical evidence, trying first of all to reach a consistent, concise description of the network of interactions underlying the system of interest. ABM community, instead, has been characterized by a more generative approach that postulates microscopic/local interactions to explore how they reflect in the macroscopic behavior of the social system.
Both communities could potentially greatly benefit from coming together and trying to reconcile their approaches in order to synthesize agent-based models that are strongly informed by empirical facts and able to produce predictions at multiple scales and resolutions, for which empirical data are presently available.