New Podcast feat: Paul Craven & Myself Part One

A new Podcast, again hosted by Christian Hunt of Human Risk.

This time I join up with the excellent Paul Craven to discuss Statistics, Spreadsheets & Scam Artists.

Here is the link

Both Ends Against the Middle

In late 1880’s the celebrated (is that the right word?) arms dealer and speculator Sir Basil Zarahoff pulled off one of his greatest coups – he managed to sell the same submarine designs on an exclusive basis, to both Greece and Turkey convincing the bitter rivals it would ensure their naval superiority over one other. Just to add into the mix he worked the same trick with the Russian government convincing them the submarines could ensure Russian command of the Black Sea and the Eastern Mediterranean. In the event all the submarines proved to be very inferior, and none saw active service, though Zarahoff of course made huge selling commissions.

This is the classic example of the middleman being a big winner and with limited risk. In this case the middleman actually created the market for the arms. Clearly Zarahoff understood 1970s marketing guru David Abbott’s maxim; “There may be a gap in the market, but is there a market in the gap?”

This story set me thinking about how we play different roles in life (and by extension) markets, and why the distribution curve might be a nice guide on occasions. In this blog I’m not delving into the world of fat tails et al., but rather a general look at how “averages” can help and hinder. We probably become aware of distributions very young in life, perhaps in our class one kid is very tall and another very short with the rest of us somewhere in the middle, just being average. Most of us seem to be average at most things most of the time, well of course we must be!

The media and financial markets are interested in extremes, “Man bites Dog” and “Crash wipes off billions of dollars” are news. This makes sense (despite the sensationalism) as most new information is found in the tails of a distribution not in the data clinging to the average. We may dream of being in the tail, as a rock star, world class athlete, tycoon, or captain of industry but we are resigned to failure in such fields, as we are still stubbornly just average. On occasions an exceptional person will try hard to be average, the field of espionage or fraud usually calls for those with unusual talents but their stock in trade method is to be “Hidden in plain sight”.

Thinking in this simple way can help in our understanding of risk. Too much risk management ends up being just risk measurement, whereas the key to success is in identifying risk, in particular new risks. This is a dynamic approach, whereas risk measurement is often static if not just a formulaic box ticking exercise. By questioning constantly and thinking about new information and whether its unusual or doesn’t ring true, is the real skill in risk management. We need to watch the tails for new information, whilst still keeping an eye on the average group for any sudden unexpected behaviour that reveals hidden deeper information that is being concealed.

A final thought, well an old joke; My head is in the oven and my feet are in the freezer, on average my temperature is just fine!

Sherlock Holmes, The Grain of Wheat, and The Snowball

The great fictional detective Sherlock Holmes was a frequent user of cocaine, and his companion Dr. Watson disclosed that the great man preferred a 7% solution, to ease his mind and let him relax to ponder various complex cases. Perhaps slightly obliquely, the phrase “7% solution” immediately made me think of long term investment strategies. A compounded return of some 7% annually (well 7.2%) will see a sum double every ten years, and somewhat strikingly in a period of 100 years, the capital will have grown by over a thousand fold. Quite a “solution”!

This idea of the enormous power of compounding was nicely illustrated by the great Islamic scholar Ibn Kallikan in 1256 (though its origins are almost certainly far older), when he considered the simple task of putting grains of wheat on a chessboard. The puzzle asks you to put one grain of wheat on the first square and double the amount on each subsequent square thereafter…so two grains on square two, four grains on square three etc…until all sixty four squares of the board are covered. So how many grains of wheat will be on the board overall?

Perhaps somewhat astonishingly, on the entire chessboard there would be no less than 18,446,744,073,709,551,615 grains of wheat, weighing about 1,199,000,000,000 metric tons. This is about 1,538 times the global production of wheat (780.8 million tonnes in 2019).

So, doubling is a hugely powerful force, but away from wheat on chessboards, how realistic is this in long-term investment? Interestingly 7% average returns are not that impossible, over many different time horizons global equity markets have delivered those sort of total returns. Detailed information across various asset classes can be found in the Annual Credit Suisse Global Investment Returns Yearbook (see reference below) and for example US equities in real terms (i.e., after inflation) between 1900 and 2019 returned on average 6.5% p.a., and in the last ten years global equites generated 7.6% p.a. (Though it should be noted returns across a wider portfolio of all stock markets for the last 120 years produces a lesser return of 5.2% p.a.) So our desired 7.2% cannot be guaranteed but investment returns can still be a powerful creator of wealth.

Now on to the snowball – this is a simple way to think of the effect of re-investing any investment income – in time it starts to act like a snowball. This analogy was used in a well-known book on the celebrated investor Warren Buffet, The Snowball: Warren Buffett and the Business of Life by Alice Schroeder.

All of these examples point to two simple investment precepts, invest early (or at least regularly throughout your savings life) and always re-invest any income. In forty years at 6.5% p.a. a £1,000 racks up nicely to around £12,500.
Whilst we may not quite get to Holmes’s 7% solution, we will get wealthier and stay somewhat healthier than the great detective.


In Praise of Effectuation

This is an extract from Two Speed World – a book I co-wrote in 2010. The topic of effectuation seems little discussed but is a good way to think about how entrepreneurs think and act in new fields where there is little or no past data and so plenty of uncertainty.


As we have noted, Ed Roberts produced the Altair 8800 without having any idea who would buy it, or what they would use it for. In the event it pushed IBM into producing the PC which in turn created 10,000 millionaires from Microsoft employees by the year 2000. One was Rob Glaser who took his money in 1994 with the intention of becoming involved with charitable works and civic projects. He wanted to promote his progressive politics and decided that the Web, plus Mosaic, plus 14.4kbps modems made streaming audio a possible route. Because no-one had done such thing before, there was no pattern to follow and in the event Glaser never did promote his ideas through the Web. He had said that he was not interested in the purely economic end of this ‘anymore than Pavarotti is interested in getting paid to sing’, but he became rich just the same and found another way to promulgated his politics, donating over $2.2 million to pro-Democratic organisations the 2004 US election.

There was no way to make a business plan with a pre-determined goal, because at that time there was no real-time audio streaming on the Web. There was no way to gauge market acceptance in a non-existent market. There could be no measurable risk, no statistical uncertainty, just an unknowable future. On the face of it, it was the worst possible situation, but only from the standpoint where a plan is constructed to maximise expected returns only after comprehensive analysis. Glaser did not have to do that, he was going to invest his money into streaming audio anyway and see what goals emerged. This changed the picture completely.

While Knightian Uncertainty with its unknowable future does not allow us to predict a particular outcome as bystanders, if we control events then we do not need to predict the future, we can create it. This neat inversion is called ‘effectual reasoning’, causing things to happen, rather than ‘causal reasoning’, measuring the causes of external events. In the causal world ‘to the extent that we can predict the future, we can control it’. With effectuation ‘to the extent that we can control the future, we do not need to predict it’. In a position of Knightian Uncertainty, the person who bases his decisions on effectuation has a market advantage, because unlike the causal reasoner, he knows where he is going.

Professor Saras Sarasvathy, a leading scholar on the cognitive basis for high-performance entrepreneurship believes that effectuation is a powerful tool in expert entrepreneurial hands. She uses a simple cooking analogy to show the difference between the two styles of decision making.

 ‘You can start from a recipe and follow it (causal), or you can look in the fridge and rustle up something with what you find (effectual).’

Only by using the latter technique will anything new ever be produced. In the absence of similar products in established markets, it is the only way that such goods can be created.

Sarasvathy believes that Glaser was able to employ effectuation because his payoff from Microsoft enabled him to consider the affordable loss, rather than an expected return; his ten years in the software business provided many opportunities to establish strategic partnerships despite the untried nature of the venture; he could react quickly to unexpected events and benefit from them.

Rather than predicting the future and following it, the entrepreneur needs the logic of control to create the market and thereby define the future. This makes prediction unnecessary, Knightian Uncertainty is destroyed, and surprises can be turned into advantages.

How is control of the future achieved?

Firstly by influencing industry standards, which is made easier by being first.

Secondly by alliances and stakeholder commitments so that everyone is singing from your song sheet.

Finally by continual innovation, because a new industry is very unlikely to be right first time.

Sarasvathy and Kotha analysed Rob Glaser’s company RealNetworks, and its products RealAudio, RealVideo and RealPlayer against the effectuation criteria. They found that because Glaser was prepared to incur an affordable loss, products were brought quickly to market, RealPlayer achieved 80% market share and as a result RealNetworks products became the de facto standards. RealNetworks products sat between the content providers and the computer software suppliers and so it was vital to have alliance to maximise the linkages between the two sides. This RealNetworks did to great effect with150 strategic partnerships agreed in 29 months. Innovation was not neglected, with one third of the staff engaged in Research and Development. By 1997, three years after its creation, the company had revenues of $36.3M and went public. This shows the power of effectuation for the entrepreneur, especially in the presence of an external driver as powerful as the Internet. But as a company moves from start-up to established multi-national, the market becomes mature and causal processes come to the fore. In early 2010, Rob Glaser the entrepreneur stepped aside from day-to-day management, McKinsey and Co conducted a strategic review and management talked in the causal, and some might say contradictory,  terminology of an ‘exciting roadmap for the future’.


Two Speed World by Gerald Ashley & Terry Lloyd

Effectuation: Elements of Entrepreneurial Expertise by Saras D Sarasvathy

Human Risk Podcast – An interview with me and Rory Sutherland (Part 1)

To celebrate the Podcast “Human Risk” achieving one hundred interviews; advertising and marketing guru Rory Sutherland and I, did a special celebration podcast for the site.

In fact it is in two parts – here is Part One where we cover a whole host of topics including the tube map, eating eggs, and Rabbit phones.

The How’s and Why’s of Decision Making

A few years back I was lucky enough to chair a series of talks at The Royal Institution in London. The audiences were made up of finance types usually with a science background and the idea was to a get a series of speakers in to range across a number of topics that would spark lively debate. A particularly memorable speaker was the distinguished American physicist Larry Krause. His theme was the importance of questioning and critical thinking in science, and indeed in life in general.

Professor Krause raised an important idea that really resonated with me; namely when confronted by a problem or examining an issue, we tend to rush towards asking Why? This seems particularly true in my own field of finance. Why is the Dow lower? Why are people buying gold? Why are “those idiots” buying at these levels?

Krause suggested that we should not forget to ask How?  Indeed, How, might often be the more appropriate question and may lead to a wider set of possible answers. By thinking about the How, it draws our mind to seek to understand the underlying mechanics and relationships that may help better explain things. We should acknowledge that this wider set of explanations can be a good thing – all too often we grab at the first answer to a problem, and fondly believe that it, must be the solution. Regrettably the human desire for slam dunk solutions knows no bounds and can lead to bad decision making.

By way of example he noted that in his own specialised field many outsiders, particularly non-scientists, spent a lot of time asking Why and Who created the universe, but rarely thought enough about How it came to be. As ever of course, there can be an uncomfortable twist – answering How may be devilishly difficult and potentially impossible. Hence the rush to concentrate on answering Why (or at least present a plausible scenario) and quickly move on, triumphantly announcing the issue was “solved”.

But why may answering How be impossible? Well some of the most difficult issues we face do not readily give up the answer to How. We have to understand that many interactions in life; science, markets, business, and our own behaviour do not always follow predictable linear paths. The past may be an extremely poor guide to the future, and so teasing out the structure or pattern of a current situation and how it may unfold may be impossible. We find this doubly a problem as humans are very attracted to pattern matching, finding patterns in random or noisy data that not really there. (The age-old example of the supposed image of Jesus on burnt toast comes to mind).

So tackling How can mean a very big challenge. In recent years interest in and the application of Complexity Science and understanding non-linear networks has attracted a lot of attention. This is still very much a developing field, but decision makers need to be aware of it might unlock the How puzzle and lead to a wider set of better decisions.

If you are interested in learning more about Complexity, and its applications I would suggest the following links:


Positive Linking – By Paul Ormerod 

The Tangled World by myself and Terry Lloyd


New England Complex Systems Institute

Santa Fe Institute