Many evolutionary and self-organization pressures can be characterized information-theoretically not only because it’s an approximation useful in designing biologically- inspired systems, but also because numerous optimal structures evolve/self-organize in nature when information dynamics approach critical points. The talk will focus on information dynamics of computation within spatiotemporal systems in terms of three fundamental operations: information storage, transfer, and modification, quantifying these operations on a local scale in space and time. The methods will be exemplified in different contexts, including modular robotics, swarms, computational neuroscience, and random Boolean networks. In addition, we shall/may discuss a relation between Fisher information and phase transitions / order parameters, drawing from both thermodynamics and statistical estimation theory.
Bio: Prof. Mikhail Prokopenko leads the University of Sydney’s Centre for Complex Systems and its new postgraduate program in Complex Systems. Mikhail has a strong international reputation in complex self-organizing systems, with over 150 publications, patents, and edited books, including “Guided Self-Organization: Inception” (Springer, 2014). He received a PhD in Computer Science (2002, Australia), MA in Economics (1994, USA), and MSc in Applied Mathematics (1988, USSR). Over the last decade, Prof. Prokopenko has co-organized and co-chaired the series of International Workshops on Guided Self-organization (GSO); was a keynote speaker at The 2013 IEEE Symposium on Artificial Life; The 3rd International Workshop on Computation in Cyber-Physical Systems (Mexico, 2012), and other events. Currently, Mikhail is the Chief Editor for Computational Intelligence section of Frontiers Robotics and AI journal, having served in the past as an editor of special issues on Complex Networks (Artificial Life), and Guided Self-organization (HFSP; Theory in Biosciences; Advances in Complex Systems, Entropy), and a section editor for Encyclopedia of Machine Learning (Evolutionary Computation). He is also a senior member of IEEE and an Executive Committee member of the international RoboCup Federation. The general objective of Mikhail’s research is to analyze and model various critical dynamics, aiming to increase robustness and resilience of complex real-world systems, by identifying required interventions during technological, socio-ecological, and socio-economic crises. The approach is strongly motivated by the search for a fundamental theory of non-equilibrium information thermodynamics in systems capable of complex computation.