ECAL 2013: Political Self-Organization in Twitter + Ultrastable CTRNNs

I just got back from the 12th European Conference on Artificial Life, where a presented two different contributions. As well, I attended to some very interesting contributions. One of them was the ‘Artificial Life in Massive Data Flow‘ workshop, where there was presented a very interesting perspective about analyzing huge amounts of data with vast number of dimensions from a complex systems perspective, beyond the simplifications of ‘Big Data’ perspectives.

My first contribution, Quantifying Political Self-Organization in Social Media. Fractal patterns in the Spanish 15M movement on Twitter, with Ignacion Morer, Xabier Barandiaran and Manuel Bedia, was based in the work we have been doing with the Datanalysis15M research network. We have tried to quantify the levels of political self-organization in Twitter data related with the 15M movement by characterizing bursts of self-organized criticality.

The second contribution, Analysis of ultrastability in small dynamical recurrent neural networks, with Eduardo Izquierdo and Randall Beer, is a very interesting research line Eduardo is developing exploring some issues about implementing Ashby’s Ultrastability in a biologically plausible way. I had the opportunity to collaborate with him for this paper during my research visit at Indiana University.

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About maguilera0

Miguel Aguilera is a Postdoctoral Research Fellow at the IAS Research Center for Life, Mind and Society at the University of the Basque Country. He has been a visiting researcher at the Cognitive Science Program at Indiana University and the Ikegami Lab in the Department of General Systems Studies at the University of Tokyo, and a postdoctoral fellow at the University of the University of Zaragoza and the University of the Balearic Islands. His research focuses on autonomy in biological and social systems from an interdisciplinary perspective, integrating insights from cognitive science, theoretical neuroscience, computational modeling, adaptive behaviour, and complex systems. It combines nonlinear and dynamical models, evolutionary algorithms, and mathematical analysis from dynamical systems, network and information theory, to generate and understand situated and embodied models of agency in the realms of artificial life and evolutionary robotics, computational neuroscience, collective intelligence practices and socio-technical systems.
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