ABSTRACT: Understanding complex physical systems requires knowledge of the interactions among their constituents, in order to be able to predict emergent behaviors (i.e., of the system as a whole). In the last few years, the same approach is being applied to the study of complex social systems, in an effort to gain insight on their behavior from a viewpoint that complements the traditional social sciences one. This kind of research involves a sensible combination of experimentation, data analysis and (agent-based) modeling. In this talk I will focus on the experimentation and data analysis aspects of this new computational social science, by presenting examples of work carried out in my group that make clear the influence of informational and social contexts on the interaction. In particular, I will study dynamic social networks, including the possibility of having a fake reputation; hierarchical social systems, where a comparison with non human primates can be made, and classification of individual behaviors for later use in modeling and simulation. In this latter case, I will show how promising advances can be made towards a theory by identifying consistent behavior across social dilemmas. I will conclude by indicating lines for current and future research in this general field.

ABSTRACT: In this talk, I will outline some recent highlights of our research, addressing two questions. Firstly, can big data resources provide insights into crises in financial markets? By analysing Google query volumes for search terms related to finance and views of Wikipedia articles, we find patterns which may be interpreted as early warning signs of stock market moves. Secondly, can we provide insight into international differences in economic wellbeing by comparing patterns of interaction with the Internet? To answer this question, we introduce a future-orientation index to quantify the degree to which Internet users seek more information about years in the future than years in the past. We analyse Google logs and find a striking correlation between the country's GDP and the predisposition of its inhabitants to look forward. Our results illustrate the potential that combining extensive behavioural data sets offers for a better understanding of large scale human economic behaviour.

Preis, T., Moat, H. S., Stanley, H. E. & Bishop, S. R. Quantifying the Advantage of Looking Forward. Sci. Rep. 2, 350 (2012). http://www.nature.com/srep/2012/120405/srep00350/pdf/srep00350.pdf

Preis, T., Moat, H. S. & Stanley, H. E. Quantifying trading behavior in financial markets using Google Trends. Sci. Rep. 3, 1684 (2013). http://www.nature.com/srep/2013/130425/srep01684/pdf/srep01684.pdf

Moat, H. S., Curme, C., Avakian, A., Kenett, D. Y., Stanley, H. E. & Preis, T. Quantifying Wikipedia usage patterns before stock market moves. Sci. Rep. 3, 1801 (2013). http://www.nature.com/srep/2013/130508/srep01801/pdf/srep01801.pdf

Curme, C., Preis, T., Stanley, H. E., Moat, H. S. Quantifying the semantics of search behavior before stock market moves. PNAS 111, 11600 (2014). http://www.pnas.org/content/111/32/11600.full.pdf

25-29 January 2016, Universidad Carlos III de Madrid, Spain

Deadline for applications Nov 13th, 2015

A prominent epigenetic mark in mammals is DNA methylation. There, phenotypical changes are tied to modifications of the cytosine-guanine (CpG) dinucleotide sequence. Such modifications occur through the addition of a methyl group to cytosine. The DNA methylation state, i.e. the presence or absence of methylation at CpG sites, has been shown to be copied faithfully from cell to cell upon replication. Importantly, dense regions of CpG sites, so-called “CpG islands”, have been associated with gene expression. CpG islands were frequently found to occur either in a predominantly unmethylated state (<5% of CpG sites methylated) or predominantly methylated state (>80%), whereas the genome average methylation is ~70%. Furthermore, while the overall methylation state of CpG islands is preserved from cell to cell, fluctuations of the state of individual CpG sites do occur, indicating the presence of noise.

Together, these findings have led to a dynamical model for DNA methylation, where the methylation state is described as a bistable system, i.e. methylation of CpG sites induces further methylation at CpG sites in the surroundings and vice versa for methylation-free CpG sites. The combination of an asymmetric potential with noise can lead to non-trivial dependence of the system state on the level of noise. This is prominently illustrated by “Parrondo's Paradox”, a discrete version of the classical Brownian ratchet mechanism. Here we explore the effect of noise on a bistable, correlated system, namely the methylation state of a sparsely connected network of CpG sites. We speculate on the possible influence of noise on the appearance of hypo- and hypermethylated genomal regions in disease. 

Abstract:

A common key feature of many biological invasions is the existence of rest phases during which individuals reproduce. Since this process alternates with phases of movement it is necessary to make use of a general description where both processes are coupled, i.e., cannot occur separately. However, some recent studies propose reaction-diffusion equations that lead to behaviours that cannot be physically accepted. In this talk I will show the reasons why these equations cannot be used and will present mesoscopic models adequate for biological invasions modelling.

Physics and Public Health Policy Design: It is a Two-way Street (abstract)

Babak Pourbohloul
Director, Division of Mathematical Modeling, British Columbia Centre for Disease Control, Vancouver, Canada
Director, W.H.O. Collaborating Centre for Complexity Science for Health Systems
Associate Professor, School of Population & Public Health, Faculty of Medicine, University of British Columbia

Krista English
Associate Director, Division of Mathematical Modeling, British Columbia Centre for Disease Control, Vancouver, Canada
Associate Director, W.H.O. Collaborating Centre for Complexity Science for Health Systems

Location: Facultat de Física, room 3.20

The determination of the most central agents in complex networks is important because they are responsible for a faster propagation of information, epidemics, failures and congestion, among others. A challenging problem is to identify them in networked systems characterized by different types of interactions, forming interconnected multilayer networks. In the first part of the talk, I will describe a mathematical framework that allows us to calculate centrality in such networks and rank nodes accordingly, finding the ones that play the most central roles in the cohesion of the whole structure, bridging together different types of relations. These nodes are the most versatile in the multilayer network. We investigate empirical interconnected multilayer networks and show that the approaches based on aggregating—or neglecting—the multilayer structure lead to a wrong identification of the most versatile nodes, overestimating the importance of more marginal agents and demonstrating the power of versatility in predicting their role in diffusive and congestion processes. In the second part of the talk, I will show that our framework can be applied to the analysis of interconnected multilayer networks of citations and disciplines, to assess scholars, institutions and countries interdisciplinary importance. The proposed method could be used by funding agencies, universities and scientific policy decision makers to complement existing methods to rank universities and countries, and for hiring and funding purposes.

28, 29 i 30 d’octubre de 2015 a Barcelona se celebra la segona edició de la Assemblea General de la Associació Europea de Ciència Ciutadana (ECSA, European Citizen Science Association).