Manuel Cebrian is a Max Planck Research Group Leader at the Max Planck Institute for Human Development, where he leads the Digital Mobilization Research Group. Previously, Manuel held positions at the Massachusetts Institute of Technology, CSIRO, and the University of California, San Diego. Manuel's research examines computational methods to create incentives that mobilize large groups of people to collaborate in solving major social challenges under time pressure. His current focus is on networked and crowdsourced responses to time-critical threats such as biological weapon attacks, terrorism, misinformation, radicalization campaigns, and cyberwarfare. His published papers appear in Science, Nature, the Proceedings of the National Academy of Sciences of the USA, and other peer-reviewed journals and proceedings in computer science and computational social science.

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This seminar explores network and crowds' physical, behavioral, and computational limits for solving time-critical problems. I describe several real-world episodes in which we used social networks to mobilize the masses to deter threats of unprecedented complexity. From finding red weather balloons hidden across the United States, to tracking down thieves in a global hunt, to reconstructing shredded classified documents, to disabling a harmful Artificial Intelligence, the potential of crowdsourcing is real, but so are the exploitation, sabotage, and polarization dynamics that undermine the power of crowds. Keeping our communities, organizations, and institutions safe depends on harnessing the networks and crowds' power and talent while defending themselves from their aggressors.

Giulia Iori is Professor of Economics at City University, London. Giulia has been a leading contributor to the development of an approach to complexity in financial markets and economic networks derived from the tools of statistical mechanics and has been a pioneer in applying Agent Based Models to Economics. Recent contributions include the analysis of the mechanisms that lead to large fluctuations in financial markets, the investigation of the role of the interbank market for the stability of the banking system, the analysis of high frequency financial time series, and some aspect of option pricing and hedging.

Her research has been funded by the European Commission, the EPSRC, and the British Academy. She was awarded the Lamfalussy Fellowship by the European Central Bank in 2003. She is the author of more than 50 publications on leading journals in Economics, Finance and Physics and regularly presents her work at international conferences as a keynote or invited speaker. She is the Co-Editor of Journal of Economics Dynamics and Control  and Journal of Economic Interaction and Coordination and Associate Editor of Journal of Statistical Physics. She is a member of the London Mathematical Society and of the Institute of Physics.

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In this talk I will presents results from recent work, with my co-authors, which explores experimentally how private information percolates through Over the Counter (OTC) markets. Despite the relevance of OTC markets, very few studies have explored how the distribution of trading links affetcs the way in which uninformed traders learn the private information of insiders, influencing in turn the efficiency and fairness properties of these markets. We constrain trading to happen on three predetermined network structures, namely the ring, the small-world, and the Erdos-Renyi random network. This allows us to introduce heterogeneity in nodes centrality and clustering, as well as to change the network average diameter, while keeping the density of the network fixed. In doing so we can isolate the effect of changes in the network structure on the trading behaviour of market participants, on traded prices, and on the distribution of traders’ profits.

We complement laboratory experiments with extensive computational experiments via a behavioural agent-based model (ABM). By calibrating the ABM model to the experimental data, we can specify the learning rule and shed light on roles of clustering and centrality on learning.

Our experiments provide support to the theory that information diffusion is affected by the network structure and by the number and position of insiders.

We show that learning is a collaborative, network process, enabled by synergistic interactions rather than by independent, pairwise interactions with better informed agents, and eased by clustering rather than by node degree or centrality. While regular, clustered, networks strengthen learning locally around the insiders, the higher closeness centrality of random networks better supports its diffusion. The competition between the speed of individual learning and the speed at which information spreads to others determines which market is more efficient and fair. We show that clustered networks can dominate random networks in some cases.

We find that information remains more localized, and is more accurate, around the insider in clustered networks, and more dispersed, but less precise, in random networks. As a result, markets with lower average misbelief may lead to more efficiency but not necessarily to more equality in profits.

I am ICREA research professor (the Catalan Institute for research and Advanced Studies) currently working at the Universitat Pompeu Fabra, where I'm the head of the Complex Systems Lab located at the PRBB. I teach undergraduate courses on Biomathematics, Biological Design and cComplex Diseases. I completed degrees in both Physics and Biology at the University of Barcelona and received my PhD in Physics at the Polytechnic University of Catalonia. I am also External Professor of the Santa Fe Institute (New Mexico, USA), Fellow of the European centre for Living Technology (Venice, Italy) and external faculty of the Vienna Complexity Hub. I have been awarded with grants from the James McDonnell Foundation and Advanced ERC grants. 

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The evolution of life in our biosphere has been marked by several major innovations. Such major complexity shifts include the origin of cells, genetic codes or multicellularity to the emergence of language, cognition or even consciousness. Understanding the nature and conditions for evolutionary innovation is a major challenge for evolutionary biology. Along with data analysis, phylogenetic studies and dedicated experimental work, theoretical and computational studies are an essential part of this exploration. With the rise of synthetic biology, evolutionary robotics, artificial life and advanced simulations, novel perspectives to these problems have led to an emerging new synthesis, where evolutionary innovations can be understood in terms of phase transitions, as defined in physics. Such mapping (if correct) would help in defining a general framework to establish a theory of evolutionary change and the role played by chance and constraints in shaping living complexity.