The Snow Avalanche Image
During the good times the snow falls and slowly builds up. Without anyone noticing, the snow reaches a pre-collapse state. It is at this time that avalanches are born. The impossible becomes the inevitable.
The Arab Spring is an example of the snow avalanche concept as applied to societies.
Tarek al-Tayeb Mohamed Bouazizi was a Tunisian street vendor who set himself on fire on 17 December 2010, in protest of the confiscation of his wares and the harassment and humiliation that he reported was inflicted on him by a municipal official and her aides. His act became a catalyst for the Tunisian Revolution and the wider Arab Spring, inciting demonstrations and riots throughout Tunisia in protest of social and political issues in the country. Source: Wikipedia.
How is it possible that a Tunisian fruit vendor could bring down governments through one act of defiance? It simply is not possible unless the countries involved were already in a pre-collapse state. The snow was ready to avalanche and just needed a trigger. The fruit vendor provided the trigger.
The Avalanche Concept Applied to Societies
Stability is not your friend. Controlled instability is your friend. Compare democracies and dictatorships: One has controlled instability – elections, and the other has only stability. The dictatorship model is more stable, until there is a revolution and everything breaks. Democracies avoid the revolutions by voting out the bums. Systems with controlled instabilities avoid the big avalanches.
While democracies use controlled instability to avoid revolutions, the same is not true in economics. Typically democratic governments suppress recessions in order to get reelected. This suppression process seeks to enhance economic stability. The elimination of controlled instability in economics pushes societies to the point of economic avalanche – a depression.
When is an avalanche likely?
First rule, moving from a stable state to avalanche state takes time. Time of stability is the most important factor in determining when the next avalanche will occur. Looking back in history will give us an idea of how long it takes before things break. For the US, that time is 80 to 100 years since the beginning of the last crisis. The last crisis period ran from 1925 to 1945. The next crisis period runs from 2005 to 2025. These periods are based on the research by two historians as told in The Fourth Turning.
Second rule, problems or cracks start to appear in society after a long period of time. Experts start to warn about instabilities or dangers on the horizon. Societies become more sensitive, and there are protests and/or riots.
Third rule, there must be a triggering event. However, this event does not need to be big as we saw with the Tunisian fruit vendor. Causality is not linear. Linear causality is where small things can only have a small impact.
Avalanches, forest fires, economic crashes and wars work the same way. They all follow the same mathematical distribution in terms of collapses – the power law distribution. Who cares? Keep reading as I apply these concepts.
Mathematics of Collapse
Take a look at this little video about the mathematics of war.
Take a look at the graphs in the video. These are the same graphs as forest fires. Notice how the frequency (y-axis) versus size (x-axis) graph follows a straight line for both attacks in war and forest fires. Wars in total also follow the same graph.
The next graph shows attack frequency versus the size of the attack in the Iraq war.
The following graphs show forest fire frequency versus size of the fire.
The graphs between the Iraq war and forest fires look kind of similar, don’t they? They tend to form a straight line. Why is that?
Societies and forests move into the future in the same way. Each new day is heavily influenced by the past. And that is a positive feedback loop process. That feedback loop process causes collapses to be similar in both sets of graphs with both having a power-law distribution of collapses. You can treat societies and forests the same way in terms of collapses. If you suppress small collapses then you will get bigger collapses. If you suppress bigger collapses then you will get the mother of all collapses. If you suppress that collapse then you will sit on the edge of a cliff forever, or until you allow the collapse to happen. The probability of an extreme collapse (the black swan) is 10 to 20 times greater than you think. A society or forest becomes susceptible to a black swan (catastrophic fire, depression, major war, …) after a long period of stability. Use history to determine what “long period” means. For a snow avalanche, long period may mean months. For a forest or society, long period may mean 50 years or 100 years.
If societies follow a positive feedback loop process, then so do economies. Economic stability (suppressing collapses) leads to catastrophe. That’s why Japan has been stuck in the mud for the last 20 years. The West is now stuck with Japan at the edge of a cliff waiting for something to push them over.
Why is stability a bad thing?
During the good times, the bad stuff (bad ideas, bad decisions and corruption) grows along with the good. Small collapses help to eliminate some of the bad stuff before it gets too big. Suppressing all collapses means the bad stuff grows so big that only a huge crash will fix the problems. No crash equals no solution.
How can we tell when the bad stuff has become a real problem? In the next paragraph see how scientists figured out how to discover the rot developing in growing sandpiles until there was a complete collapse.
“To find out why [such unpredictability] should show up in their sandpile game, Bak and colleagues next played a trick with their computer. Imagine peering down on the pile from above, and coloring it in according to its steepness. Where it is relatively flat and stable, color it green; where steep and, in avalanche terms, ‘ready to go,’ color it red. What do you see? They found that at the outset the pile looked mostly green, but that, as the pile grew, the green became infiltrated with ever more red. With more grains, the scattering of red danger spots grew until a dense skeleton of instability ran through the pile. Here then was a clue to its peculiar behavior: a grain falling on a red spot can, by domino-like action, cause sliding at other nearby red spots. If the red network was sparse, and all trouble spots were well isolated one from the other, then a single grain could have only limited repercussions. But when the red spots come to riddle the pile, the consequences of the next grain become fiendishly unpredictable. It might trigger only a few tumblings, or it might instead set off a cataclysmic chain reaction involving millions. The sandpile seemed to have configured itself into a hypersensitive and peculiarly unstable condition in which the next falling grain could trigger a response of any size whatsoever.”
Without color-coding it’s a lot harder to see the rot. We have to rely on clues. Extreme problems in one or more areas of society after a long period of stability probably indicate that that society is in trouble. 9/11 was one clue. The financial collapse in 2008 was another clue. There is rot in our military. The US nuclear arsenal has been gutted. So America appears to be in trouble at this time.
About the Power Law Distribution
Find out a little more about the power law distribution. Did you ever wonder where the 80-20 rule comes from? Please meet the power law distribution.
The power law – sometimes referred to as the Pareto distribution, Zipf’s law, or the 80-20 rule – has drawn a great deal of attention lately as an alternative to the ‘normal’ (Gaussian) distribution (i.e, the bell curve). The power law has gained in popularity among more numerate intellectuals, policy makers, and business people because it seems to fit better with common sense than what we were told in Statistics 101: Extreme and rare events have a greater than expected impact; a few products, people, and websites seem to have the bulk of market share, wealth, and mindshare; etc.
The power law distribution doesn’t fit everything which means outliers exist. However, it does a much better job than the normal distribution. For our purposes of trying to understand the real world better, the power law distribution provides a good foundation.
Collapse Framework for Societies
What follows is a framework for viewing collapses in society – economic collapse or war/revolution. I have essentially summarized the concepts I covered above.
1. Societies follow a positive feedback loop process. Each new day is heavily dependent on the past. This is similar to forests and sandpiles. The process never stops and collapses are impossible to prevent. One may only transform the size and timing of collapses.
2. Positive feedback loop processes are subject to self-organizing criticality. They will automatically move toward a pre-collapse state, then just collapse.
3. Collapses follow a power-law distribution. Outliers exist.
4. All collapses are the same. There is no difference (other than size) between a small collapse and a big collapse. Big collapses require longer to form and happen less often.
5. Collapse transformation: Collapse suppression will delay a collapse and make the resulting collapse bigger. Suppress small collapses and you will get bigger collapses. Suppress bigger collapses and you will get the mother of all collapses. Suppress that and you will sit on the edge of a cliff forever waiting to fall or be pushed over the edge. Think Japan.
6. Collapses are caused by the build-up of bad ideas, bad decisions and corruption. These things can spread to all corners of society.
7. War or revolution is just a collapse like an economic collapse. Only the form is different.
8. Collapse suppression leaves the original problems (bad ideas, bad decisions and corruption) in place giving them the ability to continue growing.
9. A collapse in one area could mean problems in other areas as well. For example, 9/11 could mean more than a terrorism problem. It could represent a sign of spreading problems into all corners of society.
10. The longer the time of stability means the longer (and bigger) the problems can grow. Time of relative stability is the most important criteria in determining when a large collapse is possible. History helps us determine which time frames are important.
11. When a system has reached a point where a small event can have a large impact then it is at a pre-collapse state or tipping point. Causality is not linear.
12. Big collapses (the outliers) may represent phase transitions where everything you know changes.
13. Black swans are outliers in a normal distribution which cause a phase transition.
14. Dragon-kings are outliers in a power-law distribution which cause a phase transition.
15. Examples of systems with a power-law distribution (outliers allowed): Forest fires, sandpile collapses, snow pile avalanches, earth quakes, financial market collapses, wealth, city size, serial killers, riots, attacks within war and wars.
16. In financial mathematics, the use of the normal distribution is forbidden. It assumes behavior is independent. In a crisis or collapse, market behavior is not independent as people start herding. Naturally this means in reality all financial mathematics uses the normal distribution. Were you wondering why these models blow up?
17. How to build a better economic model. The key here is to harness collapses.