New Algorithms May Give Keys to Predicting the Future
New research is surfacing that could provide a mathematical “crystal ball” to prevent and predict calamities. Two studies have surfaced this month that take two divergent approaches to reach similar results: identifying system tipping points where order turns into calamity.
Led by a team at the University of Sussex, the first study is born from the field of neuroscience, where researchers say they’ve developed a model in which mathematics and detailed computer simulations to pinpoint when information flow between disparate systems reaches a peak just before a system moves from a healthy state to an unhealthy state. The potential breakthrough, say the researchers, could have far reaching implications in bioscience (including brain and immune system), financial networks, and climate science.
“The key insight,” explained Dr. Lionel Barnett in a statement, “is that the dynamics of complex systems – like the brain and the economy – depend on how their elements causally influence each other; in other words, how information flows between them. And that this information flow needs to be measured for the system as a whole, and not just locally between its various parts.”
According to a Wired.co.uk article, the equation, which measures when an overwhelming number of nodes have caused an integrated change too big to remain stable was tested using a construct call the “Ising model,” which physicists use to predict phase transitions in atoms. According to Wired, the team performed more than 200 simulations using supercomputers to model a variety of different systems sizes, and found that one measure, called “global transfer entropy flow,” repeatedly reached a peak on the disordered side of the transition, just before the tipping point.
“For example, the ability to predict the imminent onset of an epileptic seizure could allow a rapid medical intervention (perhaps via brain stimulation) which would change the course of the dynamics and prevent the seizure,” explained Professor Anil Seth, of the Sussex Sackler Center for Consciousness Science. “If similar principles apply to financial markets, climate systems, and even immune systems, similar interventions might be possible.”
In a parallel study, researchers at Duke University say that they’ve developed a model born out of chaos theory to pinpoint what they refer to as “dragon king” events – extreme events that represent significant deviations from the norm that can lead to system crashes.
According to a statement made by Duke University, the model was born out of research that Professor Daniel Gauthier has been doing with electrical circuits that he calls “chaos generators.” In the study, Gauthier found that identical circuit boards, which theoretically are supposed to oscillate in synch, would experience subtle variations in behavior. During long runs of the experiment, researchers were able to use data to identify extreme events which would destabilize the system, with the circuits suddenly, and temporarily loosing synch.
Researchers found that by injecting tiny amounts of current into one of the circuits at just the right time prevented a “dragon king” event from happening, thus keeping the system stable and in synch. “We’re trying to open up people’s thinking to the possibility that systems that change are constantly evolving in time,” he said. “We’re trying to show that there’s a wider, richer set of systems that express extreme events,” Gauthier said, “and that they might be controlled.”
The implications of the research efforts could prove significant as systems around the globe increasingly become more and more connected through an ever-expanding push to “sensorize” the world. Such research could be the precursor to increasingly sophisticated real-time big data systems, including personal systems in which the body’s various systems are collected and measured in real-time for preventative interventions. Imagine the stroke that never happened, or an immune system boost just before a disease sets in.
While the computing power needed for such systems would be staggering, the theoretical implications give a glimpse at a potential future where financial and physical systems may be steady and predictable.
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