Researchers Devise Mathematical Formula for Predicting Disasters


Jennifer Kim

Working together, researchers at the University of Sussex and Charles Sturt University in Australia have recently pieced together a mathematical equation that could potentially predict disasters and, as a result, prevent them.

According to the team’s findings, published in “Physics Review Letters,” when applied to a larger scale, the equation could help people avoid catastrophic events such as financial crashes in economic systems and epileptic seizures in the brain. It can also monitor climate change and disease control, among many other things.

In-depth research and simulations have shown that “information flow” reaches a peak before a system transitions from a healthy to an unhealthy state. Growing evidence shows that mutual information in a series of systems involving complex interactions between large groups of elements reaches a high at order-disorder phase transitions. In the abstract of the article, the team speculates that by contrast, in active systems, information flow will strictly peak on the disordered side of a phase transition, and as a result, “information dynamics” may be able to predict an imminent transition for a “complex dynamical system in the process of transitioning from disordered to ordered dynamics.”

Until recently, scientists had a limited knowledge of ways to predict phase transitions. Previously, existing tools have only been able to measure peaks at the transition itself, which contributed little to the purposes of prediction. “The key insight in the paper is that the dynamics of complex systems – like the brain and the economy – depend on how their elements causally influence each other,” said lead researcher Dr. Lionel Barnett in the press release. “Information flow needs to be measured for the system as a whole, and not just locally between its various parts.”

Together, the research team found ways to mathematically determine the degree to which the components of a complex system simultaneously behave differently and simultaneously depend on each other. These found measures are the first to consistently predict phase transitions in standard systems such as the Ising Model, which attempts to define the properties of complex systems undergoing random thermal motions by exhibiting the behavior of simple magnets.

Though research for predicting phase transitions is still in its developmental stages, the effects of the work are extensive.

“If the results generalize to other real world systems, we might have ways of predicting calamitous events before they happen, which would open the possibility for intervention to prevent the transition from occurring,” said Anil Seth, co-director of the Sackler Centre for Consciousness Science at University of Sussex, in a press release. “Further research is needed to explore these exciting possibilities.”