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Researchers find the tipping point between resilience and collapse in complex systems

Researchers find the tipping point between resilience and collapse in complex systems

via Northeastern University
via Northeastern University
Hon­ey­bees have been dying in record num­bers, threat­ening the con­tinued pro­duc­tion of nutri­tious foods such as apples, nuts, blue­ber­ries, broc­coli, and onions. Without bees to pol­li­nate these crops, the envi­ron­mental ecosystem—and our health—stands in the bal­ance. Have we reached the tip­ping point, where the plant-pollinator system is due to collapse?

There was no way to cal­cu­late that—until now.

Using sta­tis­tical physics, North­eastern net­work sci­en­tist Albert-László Barabási and his col­leagues Jianxi Gao and Baruch Barzel have devel­oped a tool to iden­tify that tip­ping point—for every­thing from eco­log­ical sys­tems such as bees and plants to tech­no­log­ical sys­tems such as power grids. It opens the door to plan­ning and imple­menting pre­ven­tive mea­sures before it’s too late, as well as preparing for recovery after a disaster.

The tool, described in a new paper pub­lished on Wednesday in the pres­ti­gious journal Nature, fills a long­standing gap in sci­en­tists’ under­standing of what deter­mines “resilience”—that is, a system’s ability to adjust to dis­tur­bances, both internal and external, in order to remain functional.

The failure of a system can lead to serious con­se­quences, whether to the envi­ron­ment, economy, human health, or tech­nology,” said Barabási, Robert Gray Dodge Pro­fessor of Net­work Sci­ence and Uni­ver­sity Dis­tin­guished Pro­fessor in the Depart­ment of Physics. “But there was no theory that con­sid­ered the com­plexity of the net­works under­lying those systems—that is, their many para­me­ters and com­po­nents. That made it very dif­fi­cult, if not impos­sible, to pre­dict the sys­tems’ resilience in the face of dis­tur­bances to those para­me­ters and components.”

Our tool, for the first time, enables those pre­dic­tions,” said Barabási, who is also a leader in Northeastern’s Net­work Sci­ence Institute.

Taking a system’s temperature

Barzel, a post­doc­toral fellow in Barabasi’s lab who col­lab­o­rated on the research and is now at Bar-??Ilan Uni­ver­sity, draws an ele­gant analogy between the role of tem­per­a­ture in iden­ti­fying that tip­ping point in a pot of water and the single parameter—a tem­per­a­ture equiv­a­lent, as it were—that their tool can uncover to iden­tify the tip­ping point in any com­plex system.

Con­sider: 100 degrees Cel­sius is the tip­ping point for water changing from liquid to vapor. Think of liquid as the desir­able state for the system and vapor as the unde­sir­able one, sig­ni­fying col­lapse. Mil­lions of para­me­ters and com­po­nents quan­tify what is going on within that pot of water, from the rela­tion­ship of the water mol­e­cules to one another to their speed and the chem­ical bonds linking their elements.

As the water heats up, those para­me­ters and com­po­nents con­tin­u­ally change. Mea­suring those mul­ti­tudi­nous changes over time—a micro­scopic approach to assessing the water’s state—would be impos­sible. How, then, are we to know when the water is reaching the threshold that divides the desir­able (liquid) state from the unde­sir­able (vapor) state?

Simple: Using a single parameter—temperature. As the water in the pot reaches, say, 99 degrees Cel­sius, alarms go off and we know to remove it from the heat.

Sta­tis­tical physics has found that you can crunch down all of these mil­lions of para­me­ters and com­po­nents into one number—the tem­per­a­ture,” said Barzel. “We take it for granted now, but that was a tremen­dous sci­en­tific achievement.”

The researchers’ tool sim­i­larly crunches down all the para­me­ters and com­po­nents of any com­plex system into a single cru­cial number. It enables us, essen­tially, to take the system’s “tem­per­a­ture” to deter­mine its health and respond accordingly.

Learn more: Researchers find the tipping point between resilience and collapse in complex systems

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