New technologies are enabling social communication and coordination on unprecedented scales. The world is becoming more economically, politically and socially integrated and previously local issues are now becoming global. Counterintuitively, instead of homogenizing society, the excess of connections seems to be increasingly differentiating and fragmenting it. Recent electoral processes have shown that the Internet can increase polarization instead of reducing it and that the way globalization has been implemented is fraught with conflict and economic distress. These seemingly unrelated phenomena have similar causes: the complex dynamics of social systems and the non-linear effects of increasing inter-dependencies among complex systems’ parts.
Policy and decision makers are hardly able to understand or cope with the current world. They have failed to foresee ongoing social changes and have not been able to effectively respond to them. This is partly because of the unpredictable nature of complex systems, but mainly because of the limitations of available models and analytical tools. These are not adequate for understanding the complexity of social systems, especially in a global context.
The opportunities available from big data could solve this problem but we must analyze the data properly. The right framing of understanding and analytical toolsets could could enhance our understanding of social systems and enable the design of better technologies and intervention strategies. Ultimately, we could benefit from the complexity of social systems, rather than being endangered by it.
The goal of this satellite session is to manifest the behavior of social systems in the context of increasing communication and globalization and to show new ways to model social systems and treat big data in order to formulate the right questions and retrieve the relevant information. Our scope is to show the value of modeling and data analytics for understanding complex social systems and to explore new ways to analyze the data, taking into account the complexity of underlying systems. We expect to raise awareness about interventions in complex systems, the risk we face when societies become global, the opportunities that are created, and the role of complexity in data analytics.
The satellite will cover the following aspects in particular:
Half-day program (4 hours) including 4 invited speakers for 30 minutes each and 8 contributions of 15 minutes each. The invited speakers are:
Yaneer Bar-Yam, NECSI (Confirmed in person): The complexity of global systems.
Nassim Taleb, NYU (To be confirmed, probably remote): Global risks and fat tailed distributions.
Sandy Pentland, MIT (confirmed remote): Why democracy fails, privacy and data for policy.
Hiroki Sayama, Binghamton U. (confirmed in person): Mechanistic modeling of social systems.
Alfredo J. Morales
New England Complex Systems Institute
Massachusetts Institute of Technology
NECSI advances the development of complex systems science and its applications to real world problems, including social policy matters. We study how interactions within a system lead to its behavioral patterns and how the system interacts with its environment. Our researchers study data science, networks, agent-based modeling, multi-scale analysis and complexity
Alfredo contributes to building a better understanding of social systems by developing computational and analytical methods based on complex systems science and data science. His work is at the intersection of computer science, statistics, applied physics and artificial intelligence. He analyzes large datasets that result from human activity on social media, internet, mobile phones or purchases in order to retrieve unstructured patterns of collective behaviors that explain large scale societal properties, such as social dynamics, urban dynamics, segregation, political engagement, political polarization and social influence.
Rosa M. Benito
Technical University of Madrid
Rosa heads the Complex Systems Group at The Technical University of Madrid (UPM) and is also Professor of Applied Physics at UPM. Prof. Benito's work focuses on understanding and characterizing the structure and dynamics of different complex systems by using Complex Network Theory and Data Science. In particular she has proposed a new formalism to model complex networks topology, and she has been working with big data to determine the individual and collective behavior of users in specific online conversation on Twitter, and human mobility patterns through mobile phone data. She has lead many research projects and has participated in several Challenge for Development using mobile phone data from African's countries (Ivory Coast and Senegal). Her work has been published in many academic publications. She head the PhD Program in Complex Systems and has supervised several PhD Thesis. She has been awarded for Excellence in her Academic Career and for Innovation Education from UPM.
MIT Media Lab
Juan Carlos Losada
Universidad Politecnica de Madrid