Most complex systems like stock markets, climate systems, human body and neurons in the mammalian brain, give out the signal that it's reaching a tipping point and if that can be identified, many catastrophic events could be avoided.
With the early detection of such signals of the impending transition, appropriate management strategies could be implemented, according to an Indian Institute of Technology, Madras research paper that encompasses both pure and applied sciences.
The research has been underlined by Dominic Cummings, Chief Special Adviser to UK Prime Minister Boris Johnson, for revamping the decision-making strategies there.
According to an IIT statement, the paper 'Early warning signals for critical transitions in a thermoacoustic system', published in the international journal Scientific Reports of Nature, was the first research cited by Cummings.
The research looked at early warning systems in physics, which could be applied to finance to epidemics.
According to the IIT, Cummings substantiated his stand for applying these novel technologies for the strategy building exercise. He hopes to create a set of experts, comprising physicists, mathematicians and data scientists, to deal with the major social and economic issues of the UK.
Highlighting applications envisaged in the research, R.I. Sujith, lead researcher and Chair Professor, Department of Aerospace Engineering, IIT Madras, said, "We used analytics to detect transitions that lead to failure in engineering systems, like gas turbine engines.
"Other applications, which we envisaged, were transitions that happen in financial markets, instabilities in power grids leading to blackout and sudden failure of vital organs in the human body."
"It's heartening to see the open-mindedness and courage shown by a country, like the UK, to embrace the cutting-edge tools from physics and mathematics. It will open up a lot of opportunities for young talent. We can avail such technologies in India also to improve socio-economic systems," Sujith said.
The researchers realised that many complex systems, like stock markets, climate systems, human body and neurons in the mammalian brain, exhibit sudden change in qualitative behaviour by transiting into an alternate state termed as 'tipping'.
This alternate state need not always be desirable as in the cases, like a stock market crash, extinction of a species in an ecosystem, sudden collapse of a healthy person. This catastrophic nature of tipping demands development of precursory measures, which will forewarn these transitions.
In the paper, researchers investigated the efficacy of early warning measures based on 'critical slowing down', where the system responds slowly to perturbations as the system approaches transition.
The research paper was published by a Sujith-led team and comprised A. Gopalakrishnan (Assistant Professor, CEN, Amrita Vishwa Vidyapeetham), Tony John (PhD student, Georgia Institute of Technology), Partha Sharathi Dutta (Associate Professor, Department of Mathematics, IIT Ropar) and Yogita Sharma (Post-Doctoral Researcher, University of California, Berkeley).
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