Scientists at the University of Warwick’s Physics Department’s Centre for Fusion, Space and Astrophysics have developed a technique that could spot ordered patterns emerging in seemingly random systems.
Applications of the method could include finding patterns in plasma in the solar wind or fusion reactors, crowds of people, flocks of birds or even unusual patterns in stock market behaviour.
The researchers were initially investigating ways to study the behaviour of plasma. Like other complex systems such as crowds of people, or flocks of birds, plasma can suddenly move from a disordered random state to an ordered one.
University of Warwick physicist Robert Wicks hit upon the idea of using a computing tool called mutual information that can detect patterns and correlations from a very small set of points, typically just 10 within a large system.
His theory was tested by incorporating ideas from the work of Hungarian researcher Tamás Vicsek, an expert in the flocking behaviour of birds and insects.
By applying their technique to a Viscek model sampling the signal from a small number of points within the model, they found that in terms of error rate their mutual information based technique was four times better than traditional methods in understanding how and when systems moved from disorder to order.
The technique has uncovered patterns in the clumping of particles, movements from order to disorder, and correlating the performance of several points within a dynamic system. Taken together, if the technique was applied to stock market shifts it could uncover patterns of clumping in the moving of different stocks. This could help market analysts uncover new and unexpected market connections and mutual dependencies between companies that had no obvious connection yet seem to share similar movements in share price.