Tipping points are found in ecosystems, economies and even bodies. But they’re usually recognized in retrospect, when it’s too late for anything but regret. Now a growing body of research suggests there are telltale mathematical signals. If scientists can figure out how to detect them, they may be able to forecast tipping points ahead of time.”
So starts a 2009 Wired article on prediction and catastrophes. Using the Nobel Prize winning work of Kenneth Wilson on sudden, non-linear changes in dynamic physcial systems, increasing numbers of scientists have been able model catastrophic changes in many other contexts. These include:

The researchers involved in these different examples argue that all critical transitions are preceded by the same basic mathematical patterns. Some specific examples of catastrophe signals are:

  • Stresses in the feedback loops that are evident in complex systems such as ecosystems
  • Systems take longer to recover from fluctuations that they normally are resilient to
  • Representations of system – visual or mathematical – that become jagged rather than smooth

The key to prediction of such events is then simply “to figure out what sort of data to look for, and then how to make sense of it.” As one of the authors of a Nature study put it:

….We are repeatedly blindsided by disasters that come out of the blue. If we had better tools for anticipating those events, we could avoid some of them…”

And a co-author of the same study:

…The fact that the patterns seem to recur in so many different circumstances suggests that the mechanisms underlying them may have universal characteristics…”

However, there is considerable scepticism about whether such prediction is possible.

Not only is the data gathering extremely time-intensive and expensive, but causality of catastrophic events is complex and there may be many interacting feedback loops and threshold points. Relevance may not be determinable ahead of time.

This debate is a vital one for policy makers and practitioners, and it illustrates the spectrum of approaches evident among those who would seek to make use of the complexity sciences.

On one end of the spectrum, discontinuous, non-linear change is being presented as a predictable property of complex systems, by those who seem to be suggesting that it is possible to exert greater control over real-world systems.

On the end of the spectrum, there are those that see interconnectedness of complex systems leading to inherent unpredictability. Two unlikely advocates for this perspective are the present and former Chairmen of the US Federal Reserve. As Alan Greenspan (whose evident affinity with complexity sciences was explored here a few weeks back) argued in recent evidence to Congress in the context of the financial crisis:

History tells us [we] cannot identify the timing of a crisis, or anticipate exactly where it will be located or how large the losses and spillovers will be… Nor can they fully eliminate the possibility of future crises.

His successor, Ben Bernanke, has linked such attempts to the futility of predicting the weather:

As an economist and policymaker, I have plenty of experience in trying to foretell the future, because policy decisions inevitably involve projections of how alternative policy choices will influence the future course of the economy… over the years, many very smart people have applied the most sophisticated statistical and modeling tools available to try to better divine the economic future.  But the results, unfortunately, have more often than not been underwhelming.  Like weather forecasters, economic forecasters must deal with a system that is extraordinarily complex, that is subject to random shocks, and about which our data and understanding will always be imperfect… To be sure, historical relationships and regularities can help economists, as well as weather forecasters, gain some insight into the future, but these must be used with considerable caution and healthy skepticism (emphasis added)

Stephen Strogatz sums up a pragmatic perspective on this debate very neatly:

It’d be very nice if it were true that there were precursors for tipping points in all these diverse systems. It’d be even nicer if we could find these precursors. I want to believe it, but I’m not sure I do”

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Join the conversation! 3 Comments

  1. On this topic, I found a book called Ubquity: Why Catastrophes Happen, by March Buchanan (2002) to be a very good primer. I highly recommend. I tend to agree with Strogatz, but you never know – there may be some approaches that can reduce uncertainty to some degree that may be useful.

    Reply
  2. […] has blogged about whether catastrophes can be predicted – and reckons the jury is still out. He quotes Ben Bernanke’s reflections, which are hard to argue […]

    Reply
  3. […] has blogged about whether catastrophes can be predicted – and reckons the jury is still out. He quotes Ben Bernanke’s reflections, which are hard to argue […]

    Reply

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About Ben Ramalingam

I am a researcher and writer specialising on international development and humanitarian issues. I am currently working on a number of consulting and advisory assignments for international agencies. I am also writing a book on complexity sciences and international aid which will be published by Oxford University Press. I hold Senior Research Associate and Visiting Fellow positions at the Institute of Development Studies, the Overseas Development Institute, and the London School of Economics.

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Conflict and peace building, Evolution, Financial crisis, Innovation, Institutions, Knowledge and learning, Natural disasters