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The 2007/8 Global Finance Crisis That Lead To The Great Recession Of 2009

Problems appeared in the world finance systems in 2007 related all the way back to mortgages given to poor people in America who were unlikely to be able to repay the loans. These mortgages were packed up, divided and sold into the finance markets across the world.

My intent is not to give a big explanation of what happened and why. If you want more details, I have provided links to Wikipedia. My objective is to help you to think about the future but you may benefit from reminding yourself about the recent past.


In September 2008 after some difficult months problems exploded in America with the saving of Bear Stearns, Fannie Mae and Freddice Mac and the collapse of Lehmans Brothers.


This led to the Great Recession.


I mentioned the efficient market hypothesis earlier. Too much faith in how well it works by policy makers and regulators has been blamed for the build up of the bubbles that caused the crash.

It doesn’t make sense to me how anyone can both interfere with the free market, see prices rise past historic norms but claim there are no bubbles because “this time things are different”, see long term disequilibriums like the seemingly permanent balance of trade deficits in the UK and USA offset by surpluses in China, Japan and Germany and still expect the market to get to a stable economy.

It didn’t work in 2008/9 and it’s not going to work at some time in the future.

To understand how the world finance systems came so close to collapsing in 2008 and how it could happen again, it’s worth stepping into a simple analogy of complexity theory.

The Complexity Of A Pile Of Sand

To gain insights into complexity, you need to model a simple system and see if you can find universal lessons about structured criticality.

The world economy and how everybody and everything is much too complicated.

I’m borrowing from Wikipedia here:

Structured criticality is a property of complex systems in which small events may trigger larger events due to subtle interdependencies between elements. This often gives rise to a form of stratified chaos where the general behavior of the system can be modeled on one scale while smaller- and larger-scale behaviors remain unpredictable.

For example:

Consider a pile of sand. If you drop one grain of sand on top of this pile every second, the pile will continue to grow in the shape of a cone. The general shape, size, and growth of this cone is fairly easy to model as a function of the rate at which new sand grains are added, the size and shape of the grains, and the number of grains in the pile.

The pile retains its shape because occasionally a new grain of sand will trigger an avalanche which causes some number of grains to slide down the side of the cone into new positions.
These avalanches are chaotic. It is nearly impossible to predict if the next grain of sand will cause an avalanche, where that avalanche will occur on the pile, how many grains of sand will be involved in the event, and so on.

However, the aggregate behavior of avalanches can be modeled statistically with some accuracy. For example, you can reasonably predict the frequency of avalanche events of different sizes.

The avalanches are caused when the impact of a new grain of sand is sufficient to dislodge some group of sand grains. If that group is dislodged then its motion may be sufficient to cause a cascade failure in some neighboring groups, while other groups that are nearby may be strong enough to absorb the energy of the event without being disturbed.

Each group of sand grains can be thought of as a sub-system with its own state, and each sub-system can be made up of other sub-systems, and so on. In this way you can imagine the sand pile as a complex system made up of sub-systems ultimately made up of individual grains of sand (yet another sub-system). Each of these sub-systems are more or less likely to suffer a cascade failure. Those that are likely to fail and reorganize can be said to be in a critical state.

Put another way, the likelihood that any particular sub-system will fail (or experience a particular event) can be called its criticality. (See: Self-organized criticality)

So then, the pile of sand can be viewed as a network of interconnected systems, each with its own criticality. The relationships between these groups impose a structure on this network which has a profound effect on the probability and scope of a cascade failure in response to some other event. In other words – structured criticality.

Structured criticality is found just about everywhere. Some other examples are:

  • Landslides.
  • Lightning strikes.
  • Earthquakes.
  • The knots that appear on a twisted rubber band in a model airplane as the propeller is wound up.

I’m Not Making Predictions About The Future

I don’t know what will happen. No one does.

We don’t know where the critical “grain of sand” will land and how the systems will link together.

We can identify fault lines, just like seismologists can recognise fault lines in the earth’s crust that lead to earthquakes.

We can watch them, monitor them and be prepared.

We can try to reduce our risks and find an optimal future in our business lives and our personal lives.

We do that through the strategies and tactics of no regrets, options and big bets moves.

Past Articles In This Series

  1. The Future Of Your Business And Wealth
  2. The Complexity of The 21st Century World – Systems In Systems In Systems


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{ 2 comments… add one }
  • Ziad Abdelnour November 18, 2013, 12:34 pm

    While the financial crisis or Great Recession that began in 2008 dominates our financial consciousness, it strikes me that the events that began in December 2010 and will no doubt continue through the U.S. presidential election in November 2012 may have more of a lasting impact on our collective economic futures

  • Paul Simister December 6, 2013, 12:23 pm

    What happened in December 2010 that bothers you so much?

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