Complexity in FS: why trying to predict…is [still] futile

I thought it about time I revisited an article that s-p-e-l-l-s things out about as plainly as they possibly can be….

CxU=F…the ability of the system to adapt, often in completely unpredictable ways, means that you can’t model it and you can’t foresee the outcomes of any strategy of intervention. It’s all completely unknowable in advance. Once you accept this it becomes suddenly apparent that a huge swathe of modern finance is complete rubbish. For example, in a complex system you expect to see “tipping points” or phase transitions when the system suddenly and unpredictably switches from one stable state to another. As Caballero and Krishnamurthy have documented this appears to be exactly what happens during the episodes of liquidity hoarding and flights to quality associated with financial crises. People suddenly switch from a belief that they’re in a state where risk is measurable based on probability to one characterised by fear in the face of absolute uncertainty, so called Knightian uncertainty.

So, in the depths of the panic of 2008 we saw investors selling Collateralised Debt Obligations at almost any price largely because they didn’t know how to analyse them. What it looks like is that they bought these sub-prime backed securities because they’d been given the highest rating possible by the credit rating agencies. When some of these went bad the investors – many of them supposedly high powered institutions – belatedly recognised that they hadn’t got a clue about what they’d bought and sold, virtually at any price. One day they had nice risk models giving default probabilities, the next day they had junk.

via Complexity In Financial Systems: Why Trying To Predict The Next Crisis Is Futile | Timarr.

Presentation: what stage in the cycle do YOU think we are at?

I know what I think and I reckon and, had it not been for the unimaginable amounts of money created out of thin air by Governments, we would already be travelling the road to recovery in the new landscape!

I am no financial or banking expert but I really don’t believe one need be if embracing some ‘Systems Thinking‘. Of course, I would love to hear some reasoned arguments for and against my viewpoint. I hope you enjoy the presentation.

Here is some further “food for thought”

“In finance it’s often been survival of the fattest rather than the fittest”

Andy Haldane, Bank of England

Read more of this post

Scientists Seek Warning Signs for Catastrophic Tipping Points

In case anyone is reading my blog for the first time – shame on you – or you have read bits and pieces without ever really thinking too much about complexity or systems, THIS may be the article that changes all that! Because, what the scientists have been trying to figure out, is what Ontonix are able to achieve by measuring system complexity:

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.

“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,” said Steve Carpenter, a University of Washington ecologist and co-author of a review Wednesday in Nature.

In 1982, physicist Kenneth Wilson won a Nobel Prize for developing equations to describe transitions that don’t happen in a linear, easily predictable way, but are sudden and massive, such as fluids becoming turbulent and metals becoming magnetized.

Since then, scientists have noticed similar shifts elsewhere. The theory provides the only models that make sense of the Sahara’s sudden flip from fertile grassland to sandy wastes some 5,500 years ago. Exploited fish populations fluctuate wildly. Futures prices on the S&P 500 displayed telltale skewing in the year preceding the 1987 stock market crash.

The proposition is by no means certain, but the possibility of being able to predict these sorts of events is tantalizing.

via Scientists Seek Warning Signs for Catastrophic Tipping Points | Wired Science |

Still don’t “get” Complexity? Try this…

This brief extract is from an excellent paper “Tipping Point”  by Dr David Korowicz of FEASTA who tells me that, it was, “an attempt to describe globalised complexity in more popular form”. In my opinion it is considerably more than that!

I have tried this myself and it isn’t easy but I think he has done a great job. Judge for yourself.

Last year I used one of his video presentations with the (not at all) understated title of:

All you could ever want to know about why complexity is THE big deal if you want to gain an understanding you won’t be disappointed.

Here is a vivid description of one aspect of complexity by Eric Beinhocker who compares the number of distinct culturally produced artefacts produced by the Yanomamo tribe on the Orinoco River, and modern New Yorkers. The Yanomamo have a few hundred, the New Yorkers have in the order of tens of billions, and this wealth is a measure of complexity:

”To summarize 2.5 million years of economic history in brief: for a very, very long time not much happened; then all of a sudden all hell broke loose. It took 99.4% of economic history to reach the wealth levels of the Yanomamo, 0.59% to double that level by 1750, and then just 0.01% for global wealth to reach the level of the modern world”

Or we can look at it from the point of view of the supply-chains that are required to transform raw materials into products and services that criss-cross the globe. It is said that a modern car manufacturer has about 15,000 inputs to the manufacturing process. If each of those components was made by a supplier who put  together on average 1500 components (10%), and each of those was put together by a supplier who put together 150 components, that makes over 3 billion interactions- and we have not included staff, plant, production lines, IT and financial systems. Nor are we at the end of the story here.

For the car manufacturer would not exist were there not customers who could afford to buy a new car, which depends upon their economic outputs which are themselves dependent upon vast complex supply chains, and so on. Nor could these vast networks of exchange exist without transport, finance, and communications networks. And those networks would not be economically viable unless they were benefiting from the economies of scale shared with many other products and services. In this way we can start to see how intimately connected we are with one another across the planet, and why we see the global economy as a singular system