Thursday, October 1, 2015
8:30 am – 5:00 pm Location
Javier Borge-Holthoefer, Qatar Computing Research Instituet, Hoha, Qatar
Giovanni Luca Ciampaglia, IUNI, Indiana University at Bloomington, USA
Emilio Ferrara, IUNI, Indiana University at Bloomington, USA
Alessandro Flammini, CnetS, Indiana University at Bloomington, USA
Marton Karsai, ENS de Lyon, INRIA, France
Taha Yasseri, University of Oxford, Oxford Internet Institute, UK
Lingfei Wu, CBIE, Arizona State University, USA
An intense ongoing scientific debate is focusing on the definition of the foundational concepts of social dynamics and on the appropriate methodological approaches to understand it. The challenge is to describe collective human behavior in its complexity, driven by intentional — but not necessarily rational — decisions, and influenced by a multitude of exogenous factors.
To cope with uncertainty, communicationbased social systems require individual agents to interact in order to acquire information, and thus deeply rely on its accuracy and completeness. Perceptions, knowledge, beliefs, and opinions about the world and its evolution are in fact informed and modulated through the information people have access to. Individual agents can react by accepting, refusing, elaborating, and changing the received information. Such access typically happens on online and offline networked systems, on which information diffusion exhibits highly nonlinear behavior, thus adding an additional layer of complexity to the response of agents.
Technologymediated social collectives are thus taking an important role in the design of social structures. Yet our understanding of the complex mechanisms governing networks and collective behaviour is still deplorably shallow. Fundamental concepts of on and offline networks such as power, authority, leaderfollower dynamics, consensus emergence, information sharing, conflict, and collaboration are still not well defined and investigated. These are all crucial to illuminate the advantages and pitfalls of collective decisionmaking, which can cancel out individual mistakes, but also spiral out of control.
In recent endeavours, data from Twitter, Facebook, Google+, Wikipedia, and weblogs have been shown to strongly correlate to, and even predict, elections, opinions, attitudes, movie revenues, and oscillations 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. However, a deeper understanding of these phenomena, is still very much on demand. In parallel, and even preceding the surge in interest towards social media, the area of agentbased 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.
This is a proposal for the third edition of the satellite session on Computational social Science. The last two editions were convened it Barcelona (2013) and Lucca (2014). The aim of this satellite is to address the question of ICTmediated social phenomena emerging over multiple scales, ranging from the interactions of individuals to the emergence of selforganized global movements.