Horizons in Social Science 2013 July 11, 2013 – IMT Lucca,Italy

Nowadays there is an ongoing intense scientific debate around the definition of the foundational concepts as well as about the most appropriate methodological approaches to deal with the understanding of social dynamics. The challenge of understanding human behaviors is complex and intricate. Humans are intentional (and not necessarily rational) and the dynamics of social behavior are influenced by multitude of factors. In particular, with the advent of the Big Data era– i.e. the explosion of available datasets from technological mediated communication – that challenge has increased its complexity. If on the one hand we can have access to an enormous set of observable social and mobility traces, on the other hand there is a lack of theoretical concepts to ground and interpret data as an expression of individual and social behavior. The event is intended to gather the most proficient scientists and companies working at the edge of the computational social science and big data to detail the new frontiers and challenges with an interdisciplinary, tight and non reductionist approach.The symposium is open to all researchers, scientists and practitioners.

Invited Speakers

Alessandro Vespignani is Sternberg Distinguished Professor at Northeastern University in Boston, where he leads the Laboratory for the Modeling of Biological and Socio-technical Systems. He is fellow of the American Physical Society, member of the Academy of Europe, and fellow of the Institute for Quantitative Social Sciences at Harvard University. He is also serving in the board/leadership of a variety of journals and the Institute for Scientific Interchange Foundation. He is president elected of the Complex Systems Society. Vespignani is focusing his research activity in modeling diffusion phenomena in complex systems, including data-driven computational approaches to infectious diseases spread.

David Lazer is a Professor in Northeastern University’s Department of Political Science and the College of Computer and Information Science. His work focuses on the nexus of social networks, computational social science, and collaborative intelligence. He is a reviewing editor for Science, and his research has been published in such journals as Science, Proceedings of the National Academy of Science, the American Political Science Review, and the Administrative Science Quarterly.

Irena Vodenska is a Professor at Boston University. She is an expert in international finance and investments, with teaching and research interests in statistical finance and applications of quantitative methods in financial modeling. Her research is focused on quantitative methods for modeling volatility, global interdependence of financial markets, banking system dynamics, global financial crises, and studies of extreme events such as bubbles and crashes. She is principal investigator of several research projects focusing on the implementation of quantitative methods for forecasting crises. Prof. Vodenska is a Chartered Financial Analyst with experience in creating and actively managing hedge fund portfolios, specializing in risk arbitrage and convertible fixed income securities. Her extensive background in finance includes portfolio management, financial analysis, and security trading on Wall Street and on European Markets.

Nicola Santoro is Distinguished Research Professor at Carleton University’s School of Computer Science. Initially interested in philosophy, he is one of the first computer science graduates in Italy (Laurea – Pisa 1974), discovering the beauty of algorithms and data structures. During his PhD on information structure at Waterloo (Ph.D. – Waterloo 1979), he discovers the net (then called ARPANET) and email, and starts thinking in distributed terms. Involved in distributed computing since the beginning, he contributes seminal papers focusing on the algorithmic aspects. He is the author of the book Design and Analysis of Distributed Algorithms (Wiley 2007) and co-author of the forthcoming book Distributed Computing by Oblivious Mobile Robots (Morgan & Claypool 2012). He has been awarded the 2010 SIROCCO Prize for Innovation in Distributed Computing. His current research interests are distributed computations by mobile entities (agents, robots, sensors) and in time-varying networks (delay-tolerant, vehicular).

Program

10.30 – 11.15 Alessandro Vespignani – “Modeling and forecast of socio-technical systems in the data-science age”

11.30 – 12.00 Coffee Break

12.00 – 12.45 Irena Vodenksa – “Multiplex Financial Networks Dependecies”

14.00 – 14.45 Nicola Santoro – “On Distributed Computing and Social Networks”

15.00 – 15.45 David Lazer – “Big data big insights: the coming age of computational social science”

16.00 – .. Coffe Break for free discussion and lobbing

Abstracts

Alessandro Vespignani

Modeling and forecast of socio-technical systems in the data-science age

In recent years the increasing availability of computer power and informatics tools has enabled the gathering of reliable data quantifying the complexity of socio-technical systems. Data-driven computational models have emerged as appropriate tools to tackle the study of contagion and diffusion processes as diverse as epidemic outbreaks, information spreading and Internet packet routing. These models aim at providing a rationale for understanding the emerging tipping points and nonlinear properties that often underpin the most interesting characteristics of socio-technical systems. Here I review some of the recent progress in modelling contagion and epidemic processes that integrates the complex features and heterogeneities of real-world systems.

Irena Vodenksa

Multiplex Financial Networks Dependecies

Coupled networks have gained increased research attention lately. In a system of interdependent networks the dynamics of one network depends on another. Moreover, multiplex networks have been shown to be more fragile to shocks compared to single networks. Here, we study the dependencies of a stock market index network on one hand and foreign exchange rate network on the other. We examine daily returns of stock market indices and foreign exchange rates between 1999 and 2012 for sixty countries. We create two networks where nodes represent countries and connectivity links are defined as probabilities of contagion derived from correlations between the nodes weighted by the countries’ Gross Domestic Products. Using two correlation computational approaches, partial correlation and Pearson correlation, we show that during financial crisis periods, the correlations within the stock market network increase, while the correlations within the currency markets show a decline. Moreover, the correlations between the stock market and foreign exchange layers during crises periods become negative. We develop a model for systemic risk propagation through the stock market and foreign exchange coupled networks to study the dynamics of this multiplex system, and to assess the systemic importance of countries for the overall complex financial structure investigated in this study. We initially introduce a shock into the system by either damaging a stock market index node or a foreign exchange rate node to observe how risk propagates through the interdependent financial network and find that certain countries are more efficient in spreading financial crisis across both network layers compared to others. While the Pearson correlation identifies the UK and the US as countries with largest influence, partial correlation shows weaker influence of the US in spreading the crisis when initially a stock market node is shocked. This may be due to the emergence of negative partial correlation between the US and other countries within the stock market index network, indicating that correlations in this network may be dominated by the global stock market trends. In addition, while the model indicates that, in general, smaller countries have lower influence in spreading systemic risk, the results show that Greece, for example, exhibits significant influence in crisis propagation to other countries, and its systemic importance for the global financial system is not marginal.

Nicola Santoro

On Distributed Computing and Social Networks

Social networks have stimulated the latent interest in the time-varying structures created by the dynamic interactions occurring within a “population”. To study the properties of such structures in relation to the nature of the interactions is an ambitious but inevitable task, whose undertaking is active not only within the social sciences and the complex systems communities but also within the distributed and communication communities. Indeed, in the latter communities, a wealth of analytical tools and methods has been developed over the years in a variety of contexts, ranging from infrastructure-less highly dynamic networks (e.g., ad-hoc wireless networks, vehicular networks, mobile sensor networks, etc), to structured systems where the semantic of the behaviour is highly dynamic (e.g., repetitive polling systems, network contamination/decontamination processes, etc). In these investigations, the common keyword is “locality”, the tools are discrete, the approach is algorithmic, the methodology is axiomatic. Some of these methods, tools and techniques can be usefully employed in the study of other time-varying systems, social networks in particular, providing a different outlook, perhaps shedding a light on aspects not (easily) identifiable by other computational models. Aim of this talk is to provide an introduction to this subject.

David Lazer

Big data big insights: the coming age of computational social science

We live an increasing fraction of our lives interacting via technologies that capture minute details of our behavior– what we are saying, to whom, and where we are. The resulting data have the potential to revolutionize our understanding of large scale, complex human systems. The objective of this talk is to examine some of those possibilities, offering a few examples from mobile phone and social media data, as well as pointing to challenges that we confront in these early days of computational social science.