An international team of researchers from the University of Amsterdam (UvA) and the University of Geneva has shown that information processing features can accurately predict the emergent behaviour of complex systems. The results were obtained using elementary cellular automata and were recently published in the international journal Complexity. UvA researcher Rick Quax: ‘If we are able to predict behaviour of complex systems more accurately, then we can anticipate and proactively act upon possible changes, instead of being overtaken by events such as a financial crisis’.
A single flying bird creates a trajectory which can be studied and predicted. However when birds become part of a flock then the behaviour of the whole system changes qualitatively, in this case in the form of mesmerizing large shapes and complex trajectories. The behaviour of such a system does not only depend on its individual elements but also on their non-linear relationships to one another. This is called ‘emergent behaviour’.
There are many dynamical systems – like bird flocks, neural networks, the economy, or social networks – that show emergent behaviour and greatly impact our daily lives. An international team of researchers from the University of Amsterdam and the University of Geneva has conducted a theoretical study using information theoretic features to investigate how to detect and predict the dynamic behaviour of complex systems.
UvA researcher Rick Quax (Informatics Institute and Institute for Advanced Study): ‘Our research shows that only a few local information processing characteristics can accurately predict the long-term behaviour of elementary cellular automata. These automata are the simplest dynamical systems which generate emergent behaviour such as deterministic chaos.’ The research team has subsequently applied this new method to study the behaviour of two important financial markets (foreign exchange market and interest-rate swaps). They show that the method is able to detect changing behaviour months in advance of the 2008 crisis, making it a promising candidate as an early-warning signal.
Additional study of information processing features of increasingly complex theoretical models will result in better predictions to anticipate instabilities in socio-economic systems.
Rick Quax, Gregor Chliamovitch, Alexandre Dupuis, Jean-Luc Falcone, Bastien Chopard, Alfons G. Hoekstra and Peter M. A. Sloot: ‘Information processing features can detect behavioral regimes of dynamical systems’, in: Complexity (16 April 2018).