Winning by Sharing

What is progress?

A recent article in the London Evening standard reports on French President Sarkozy's new initiative.

You can always rely on the French to ask difficult questions such as: what is progress? And set up an international commission to find out.

It's not what people say.  It's what's *behind* what people say.

France is the first country to at least indirectly open the debate on the case for 'steady state economics'.  That is, an economy with no growth.  Because what politicians aren't really telling us is that the future is about less.  Less of everything. Except for (and with apology) the most uneducated amongst us, it doesn't take a rocket scientist to work out that stuff is going to run out eventually, actually quite soon.

The relentless pursuit of profit, systemic corruption, and rapacious greed is ruining us.  Humanity has to evolve very quickly to a state of cooperation between individuals, companies and nation states where there is a continuous, fair exchange of value, rather than the notion of "I win. You lose", as the default position of business and government.

A steady state economy can only be built and sustained by peer-to-peer, open source styles of organisation and production.

Sorry to burst your bubble but humanity's survival is going to have to be based on 'being' rather than 'having'.

A secret GDP exists that is not accounted for anywhere

Once again the French seem to be the most insightful in their understanding of globalisation, the economy and the direction in which capitalism is going driven by open source, peer-to-peer models of production. This excellent post by Bernard Maris (and translated by the priceless Leslie Thatcher) on the economic value of open source captures it in plain English;

"How does it happen that extremely talented software engineers spend their time improving software for the collectivity, without receiving any remuneration for that work? Strange, no? ..... But how to measure this wealth? How to calculate all this knowledge due to voluntary cooperation? How to calculate all the knowledge and labor garnered in Wikipedia? It's pretty convenient to go on Wikipedia! And yet Wikipedia results from largely non-commercial work.

All this secret, hidden (open source software) GDP is based on cooperation. It's the immersed surface of the iceberg! While real GDP (proprietary software) is based on rivalry, exclusion, competition."

This is why I've always argued that organisations like Visa & Mastercard should quit spending their money on sponsoring the Olympics and actually go out there and do something useful like unlock trillions of dollars of economic value by building an open, micro-payment system that can be used by anyone.  Perhaps even, dare I say it, create a new kind of community currency?  So if any of you are reading this, let me be clear, for the avoidance of all doubt about the implications for your brand of doing something like this;

Open source models of production are about social interactions. When you become part of the social fabric of hundreds of millions of people, even the $2trillion per annum that are processed by your credit card networks will look like pocket change.

'Winning by sharing' is a state of cooperation between individuals, companies and nation states where there is a continuous fair exchange of value as opposed to the prevailing nature of business and government that is predicated on the notion of "I win, you lose".

Economic Crimes Against Humanity

Beautifully crafted article by Shoshana Zuboff describing the unspeakable suffering unleashed by the 'banality of evil' of those we entrusted with our economic welfare.  It ends with;

That in the crisis of 2009 the mounting evidence of fraud, conflicts of interest, indifference to suffering, repudiation of responsibility, and systemic absence of individual moral judgment produced an administrative economic massacre of such proportion that it constitutes an economic crime against humanity.

Note:  The author is famous in the ‘social, p2p world’ after writing a book called The Support Economy (2003/4) with co-author Jim Maxmin.  Amongst other things, the book foretells the failure of managerial capitalism (or the end of command-and-control).  Rather prescient given where we are right now.

'Winning by sharing' is a state of cooperation between individuals, companies and nation states where there is a continuous fair exchange of value as opposed to the prevailing nature of business and government that is predicated on the notion of "I win, you lose".

Too much, too soon?

A model for self-funding health care

THE BIG PICTURE, HISTORY, CONTEXT

We propose that it is economically viable for the UK's National Health Service (NHS) to become a self-funding entity by selling patient healthcare data to a wide variety of industry actors.  We also propose that an individual’s healthcare data record itself can eventually becoming an earning asset that can be passed on to future generations. 

In 2004, Jeff Kelley, an American Organisational Development expert working with Freddie McMahon, a UK Knowledge Software expert ‘discovered’ a means of capturing patient behaviour and preference data that is of sufficient value  for pharmaceutical companies, insurers and healthcare providers to actually pay for.  

They collaborated with IBM, Google and the UK Work Foundation who contributed to, and verified an economic model that could make any large scale healthcare system self-funding.  The author was involved in early discussions in the context of using social networks as a platform for testing and piloting the model.  Jeff Kelley died and his work was never published.

THE ECONOMICS – ‘HEALTHWEALTH’

What is the data? Think of a patient record as more than a static file or electronic representation of doctors’ notes. Think of it extending to DNA fingerprints, Tesco Clubcard data, medication consumption data (using RFID tagging), exercise data, and most valuably, self-service diagnosis data because it can be gathered in real time.  This data, aggregated at scale, provides answers to questions like;

• How many more type 2 diabetics are there?
• If I know the rate at which people are taking up no-smoking treatment should I really launch this new cigarette brand?
• Exactly how many heart drugs should I buy this year?
• Are we over-prescribing antibiotics in children, and by how much?

In fact the combinations of data are almost infinite, as are the questions.  What this data really does is allow companies to sense demand, predict and change user behaviour, and they will pay more for this than they do for conventional  market data.  Enough for the NHS to eventually become completely self-funding.

Both Microsoft and Google have introduced online services aimed at consumers who want to maintain their healthcare data in exchange for a range of benefits.  Neither of these initiatives has been successful in terms of scale and economics.  Their failure has been largely through lack of trust.

To illustrate the value of consumer data consider this; all the current academic research on large data sets provided by the major social media providers concludes that advertising is not a sustainable revenue model.  The reason why Facebook, Bebo (prior to acquisition) and Twitter are free is because they are VC backed, and even YouTube (owned by Google) has applied for broadcast TV rights in its major markets because online ads aren’t generating enough revenue. 

 Recently both Twitter and Facebook announced the development of what  they call ‘sentiment engines’ signalling a radical shift in their business model.  Sentiment engines are web services for anyone who wants to extract intelligence from the conversational content of huge numbers of consumers – for a fee. Big companies already have huge budgets for customer insight systems (CRM) and ‘social data’ is just another form of this data.  In the next 2 years, between them Facebook and Twitter will hold more valuable (that is, saleable) data about UK citizens than the government itself. 

The UK government would have to pay US software companies run by teenagers to test the impact on people of consuming these items: how many burgers is too many? How many beers? How many late night films? How much violence on a video game? How much cough medicine?

NEW MODELS OF ORGANISATION

Some upside-down thinking is needed to create a model of trust such that 60m citizens’ healthcare (anonymised) data can be monetized in a way where there is a continuous exchange of value for all parties.  The answer lies in organisational form.  

Perversely the clues can be found in financial services.  Visa’s organisational model created a network of 22,000 banks, 1 billion card holders, and 16 million merchants, generating $2.4 trillion of transactions at its peak.  Only 0.2% of that value was required to operate the Visa platform making it one of the most profitable businesses of all time.  The fact is, traditional command and control organisation is not suited to 21st century value creation models.  Dee Hock, the founder of Visa created a non-stock, for-profit membership based network with ownership in the form of non-transferable rights of participation. He designed the organisation according to his philosophy: highly decentralized and highly collaborative.

If the participants own the service, have full control and responsibility for the veracity and quality of their data and are able to democratically agree upon how this ‘Health Wealth’ is distributed, then people will trust it.  Jeff Kelley suggested the creation of a Public Interest Enterprise based on Open Source models of value exchange and ownership – “The power of us”.  Given that healthcare models in the US and Britain are only 10-20 years away from collapse we’re going to have to bet more heavily on the future, than we do on the past.  Indeed, such is the value of the nation’s collective health data, that it’s a future national asset, and of major strategic importance to the United Kingdom.

Think about this.  If a patient can derive an income from their medical data, even if it means re-keying it into another platform, they will.  All the indications are that Microsoft, Google and a number of ‘up starts’ are creating these platforms. 

In December 2007, 23AndMe launched an online service that enables consumers to understand their own genetic information by sending a mouth swab sample for genome mapping. Users know whether their children are predisposed to certain traits or talents — athletics or music or languages — and encourage them to pursue certain paths. “We will, counter intuitively, face even more pressure to conduct our lives carefully, strictly, and cautiously; we'll practice the art of predictive diagnosis and receive a demanding roster of things to avoid, things to do, and treatments to receive — long before there's any physical evidence of disease”.   In other words, this service is creating the very behaviour change in patients that governments seek. Google has invested in 23andMe, whose co-founder Anne Wojcicki is married to Google co-founder Sergey Brin.

Last year a major step towards the widespread use of intelligent ‘web agents’ was made when researchers in New York, created a character in the virtual world of Second Life with the reasoning abilities of a child.  These web agents are commonly referred to as Chatterbots. The Wikipedia definition;

“A chatterbot (or chatbot) is a type of conversational agent, a computer program designed to simulate an intelligent conversation with one or more human users via auditory or textual methods.”

It is now possible for non-technical subject matter experts to program chatterbots (instead of a computer programmer).  So a nurse on the frontline who carries out diabetic screening diagnostics on patients can ‘embed’ her knowledge of clinical procedures in a chatterbot that can be used on the web, interactive TV or mobile phone by any consumer or professional wishing to test themselves or a patient for type 2 diabetes.  What would the cost base of NHS Direct look like if most of its conversations with callers could be handled by chatterbots?  £75 per hour for a GP, £28/hr for a practice nurse.

HOW DOES IT WORK?

Last year, a group of experts in London carried out an analysis of the potential market for chatterbots based on Gartner research.  This research predicts that by 2011, 80% of internet users will have virtual representations of themselves - working or playing online.  User-generated scripts will power their chatterbots, enabling conversations with people within their social network or beyond.  Dialogue can be text or voice-based, covering any subject from informal socialisation to advice; people will  have a Chatterbot as a personal assistant within FaceBook, another as virtual tax advisor and a third specialising in the investor implications of doing business in Shanghai.  The group projected that based on these conservative assumptions;

• World population 7.2bn
• PC & mobile devices converged
• $100 PC pervasive
• Smart consumer devices i.e. health
• 4bn connected consumers  
• 2.2 chatterbots per consumer
• 1bn users with multiple devices
• 0.5bn organisations (incl. micro)
• 8.8 chatterbots per organisation

..by 2018 there will be.

13.2bn chatterbots
Chatterbot = Social Network Person

…and because chatterbots can carry advertising;

• US$50 revenue for each Chatterbot
• US$660bn
• Variance (-20% to +100%)

Clearly there are many privacy and data protection issues but by using a basic principle of allowing users to control the separation of identity and data, coupled with end user ownership of the organisation that manages it, is the only way.  A Creative Commons for health data with government oversight but citizen ownership.

HOW DOES IT START?

It starts with having a healthy disrespect for the impossible.

It starts with the maxim "We are continually faced with great opportunities which are brilliantly disguised as unsolvable problems." - Margaret Mead

It starts with understanding that “It is not because things are difficult that we do not dare; it is because we do not dare that things are difficult.”  - Seneca

It starts with taking some time to talk to me.

POST SCRIPT

Serendipitously, on 19th February 2009, a German university announced the development of  Assistance for patients: Intelligent sensor networks that remotely monitor important vital signs of patients or measure their activity.  Heart patients simply wear a small, lightweight device on their body.  The device records movement. A physician can evaluate, for instance, the heart rate in connection with physical effort, thus simplifying the diagnosis.

The data is transferred via a home gateway to an Internet portal where the physician can access the information remotely. A range of optional components can be added to the system to measure other vital data such as blood pressure, weight or blood sugar level.

A socially responsible hedge fund emerges

"The name Hedge Fund is actually a misnomer since it implies a trading strategy where speculative risks are NOT taken; one where the risks in one investment are counterbalanced by other, separate investments". 

Over a period of many years I've been tutored on a broad array of subjects by an ex-colleague Frank Dunn.  He and I worked together at Kleinwort Benson in the mid-nineties and have been friends ever since.  His 'thing' is physics & mathematics and he describes himself thus;

"Frank Dunn is a professional commentator on Hedge Fund strategies & advisor to numerous financial professionals. Along with many noted academics, he has argued for some time that risk models need updating; to reflect the dynamic nature of sentiment risk; that is the changing behaviour of market participants.  He uses a variety of highly mathematical techniques to model this; built upon the theory of Social Networks." So naturally this interests me ;)

Frank has been applying social network theory to predicting the movement of the financial markets for the past 10 years.  His models detect changes in human behaviour early on and so once you know what mood investors are in, you can predict their decision making behaviour;  which is, apparently the easy bit.

I appreciate this subject matter won't be for everyone, but it's one of those pearls of wisdom that has to be placed in the public record as a point of reference, for posterity and for our central bankers.  A paper that describes a credible and proven means of generating wealth in a socially responsible way.  Where a hedge fund actually 'does what it says on the tin'.  “There is actually the opportunity for value creation in being socially responsible.” - Brian Walker

I have to stress that none of what follows constitutes legal, investment or financial advice.  So with kind permission;

The route to investment safety:
A brief description of Smoother Composite Investing

The End of Capitalism?

Statistics can fool you. In fact it is fooling your government right now. It can even bankrupt the system (let's face it: use of probabilistic methods for the estimation of risks did just blow up the banking system).  Nassim Nicholas Taleb, from a recent Essay entitled The Fourth Quadrant

Many academics other than Taleb have been warning of the frailty of our risk models for many years.  The favourite aphorism amongst EU researchers who I met working on the Complexity in Finance initiative was: ”your risk models will fail just when you need then most”.  The truth of that should now be painfully self-evident.

The reasons why they were all ignored can be summed up in one word: greed.  Everyone was in too much of a hurry making money to reflect on the risks they were taking.  However, it is important to understand that there were different characters in the villainy. 

Most of the financial services industry is numerically dyslexic; so relies on others to ‘do all the numbers’.  Those ‘others’ are the ones Taleb singles out for his most vitriolic criticism.  In the same essay he says:

My outrage," he says, "is aimed at the scientist-charlatan putting society at risk using statistical methods.

However, even Taleb struggles to understand the issues clearly.  He says there are ‘no known tools’ to model certain features; when in fact the tools exist.  They are little-known & poorly understood as is most good wisdom; but they do exist.

The title of his book, Fooled by Randomness, is a mistake.  Neither financial markets nor other features of our social and natural world are random; to assume they are random is itself misleading & highly dangerous. 

Events & outcomes are uncertain, not just because our models are not good enough; but also because real-world causality is complex: effects have many causes; & causes have many effects.  This network of interdependencies between events is sometimes called non-localisation.

Understanding financial markets in these terms enables newer, more robust measures of risk to be employed.

Is there any true Hedge Fund Out There?

As recent events have revealed, most Hedge Funds are run on a highly speculative basis; with little or no understanding of the finer aspects of market risk.

The name Hedge Fund is actually a misnomer since it implies a trading strategy where speculative risks are NOT taken; one where the risks in one investment are counterbalanced by other, separate investments. 

The textbook example is a comparison between the transport & energy sectors: when energy costs go up, transport companies usually suffer; & vice versa.  A hedge between an airline company and oil company should then smooth out the worst fluctuations in energy prices. 

Unfortunately this traditional hedging approach was always a naïve and simplistic strategy.  It no longer works; if it ever did.  The key flawed simplifying assumption is that the interdependence between the two investments is one stable, single relationship; called the correlation.  This is NOT the case.

Markets are fractal in nature: within each phase or trend, there are sub-phases & sub-trends; with sub-sub-phases and even smaller, shorter phases still.  Typically, at any time there are 12 separate timescales exhibiting phase-like or trend-like behaviour. 

The patterns at each timescale are subtly different.  Benoit Mandelbrot coined the term Self-affinity to highlight the patterns are different; compared to simple fractals which have identical patterns and are thus known as ‘self-similar’.

There are thus 12 components to the return in an investment (the trend gradient); 12 components to any sensible risk measure; & 12 components to the co-dependency between any two connected investments. In global financial markets, all investments are now connected in some degree or other; in mathematical terms, the 12-component co-dependency vector is always non-zero.

To hedge the fluctuations in an investment, you need to devise a strategy that smoothes out the adverse price moves caused by fluctuations in timescales shorter than the investment horizon; that is how long you intend to hold the investment.

No-one has been able to do this properly; until now.

Fast-Moving markets are excessive fluctuations of the shortest timescale

The credit crunch is a new & different phase of market behaviour; owing in large part to fast-moving markets; & the excess volatility they cause.  Fast-moving markets have been an ever-present feature of the credit crunch. 

They have always existed to some degree in headline commodity markets, such as Oil & Gold.  An important piece of news would send all traders scurrying to their brokers; creating a sharp move in a matter of minutes; sometimes even seconds.   Exchanges have devised mechanisms to limit such moves; by simply preventing further trades from occurring.

However, the contagion has now spread.  Stocks, currencies, indeed any investment at all can move several full percentage points on the back of a news item; frequently unconnected to the sector in question; but caused by a domino effect of co-dependencies with the relevant markets.

Fast-moving markets highlight an undeniable facet of price patterns: short-term fluctuations are event-driven and therefore inherently unpredictable.  It is not possible to know all the news items of any day in advance; from storms, earthquakes & hurricanes through to what the US Treasury or Federal Reserve might say, or how a committee or government might vote.

Many traders do try to profit from such short term moves by ‘living on their nerves’ and chasing the trends within such fluctuations.  Sadly, these intraday traders merely contribute to the instability; amplifying the fluctuations to larger & larger spikes, making current markets the maelstrom of nerves we all witness.  One thing they are definitely not doing is trying to hedge these risks.

To provide lower-risk trading strategies, you need to measure the co-dependencies of markets under these very stressed fast-moving market conditions; then choose combinations of investment that minimise the effect of these high-frequency fluctuations. 

It turns out that, if you can do this, then measuring the rest of the 12-component co-dependency vector becomes a comparatively simple exercise.

High-Frequency Investment Smoothing: the Process Itself

Our new software is designed specifically to overcome this problem; so that we can offer smoothed (i.e. properly hedged) investment combinations.  We have also designed chart production software; so that these investment combinations can be viewed graphically.

In practice what this means is that, for each potential investment we might consider (e.g. stock, currency, commodity or financial future, but avoiding options at the present time), each night we search & scan all other potential investments for the ‘best combinations’.   We make our ‘potential investment’ list (what we call our investment universe) as wide as possible; or rather as wide as broking costs permit us to do. 

Our guiding principle of maximum diversity;  trading as many different investments in as small amounts as possible; has to be offset against the expense of so many transactions.  We run a specific optimisation trial to decide on the correct basic trade size for a given set of brokers’ charges.

For all items in our investment universe we collect & clean high-frequency (tick-based) market data (price, volume, etc).  For futures contracts we have our own proprietary techniques for merging data series from electronic & open outcry pit-based markets; and for contracts with differing expiry dates.

‘Free Lunch’ criteria

Once we have identified smoothed pairs of the ‘best combinations’ of investments, we then repeat the process seeking to add further investments to the combination; thereby creating smoothed complex composites.

The criteria that we apply to decide what are the ‘best combinations’ do vary.  The general aim is to find investments that are counter-dependent (have negative co-dependency) in shorter timescales; but have positive co-dependency in the intermediate & longer term.

Roughly speaking we apply a kind of Dedekind cut (ref. any standard maths text on set theory & the real numbers) to the pattern features we detect in the investment combination.  Patterns that reflect features that are longer term than the intended investment timescale are regarded as part of the projected reward; whereas patterns that represent fluctuations of a shorter term than the intended investment timescale are regarded as part of the predicted risk.

The startling thing about this technique is it eliminates risk far more than reward: in financial markets, there is such a thing as a free lunch!

Investment Timescale or Horizon

This begs the question of what is the intended investment timescale.  That in itself varies and is critically dependent upon prevailing market sentiment, globally, regionally and within the sector and its most closely-related areas.  

Currently most investment combinations are stable for only between 2 to 8 weeks; whereas in calmer times, this would extend out to many months; sometimes years.

The success of the criteria we apply are judged by a technique called competing experts: those criteria with the most successful portfolio performance in recent weeks, as well as the most stable predictions moving forward are used to build complex composites.

Order Preparation, New entries

Finally, when complex composites are judged to meet all our smoothness criteria, they are examined to see whether the timing is right for market entry.  This can be compared to traditional charting methods; except of course the price series is that of the investment composite.

Perhaps the truly astounding nature of our investment composites is revealed by the fact the charts they produce give such clear & unambiguous technical signals. In other words, by filtering out short-term fluctuations we have rendered the job of the Technical Analyst considerably simpler.

Our own entry signals are nevertheless not those of the typical chartist trader.  Instead we exploit features of the patterns we have detected to identify the lowest risk entry points.

Order Preparation, Adjustment of existing Holdings

Then the same overall process is repeated for all existing holdings; making sure complex composites are still stable propositions moving forward; with or without rebalancing.

The output from all this process is something we call the conditional order list; which contains all the suggested adjustments (full or partial entries & exits) of the whole portfolio.  This is intended to be provided on an automated basis via the intelligent interface of the broking system(s).

Handling Actual Trade Execution: Block Trades

For very profound reasons, the instabilities of the current markets have led us to require all trades (apart from a few ‘headline markets’ discussed shortly) are executed at or around the same time of day.  This can be seen as a kind of block trade or even a delayed program trade.

In simple terms, this enables us to ignore many of the effects of the unbalanced portfolios that would result from only partial execution of the suggested trades.  Such effects might lead to unpredictable risk scenarios and are thus to be avoided.

Even with this constraint there is a need for scenario planning for each day’s trading: dependent upon the patterns revealed by the morning’s (i.e. European) price moves; followed by the opening of the (US-based) trading pits (primarily in New York & Chicago);  different subsets of the conditional order list may or may not be executed.

Once the market has evolved sufficiently for a list subset to be enabled for trading; the orders can be confirmed to the broker.  Orders are typically of the ‘Entry @ Limit’ kind but they can be accompanied by triggers. 

Each successful entry order must be followed by two ‘exit strings’: a list of prices where some or all of the holding must be cut; with the upside string being known as Target Exits; and the downside string usually called Stops.

The distance of prices in the target exit string from the entry price depend on the pattern of projected reward; whereas the distance of prices in the Stop string from the entry price depend on the pattern of predicted risk.

Simply put, if an investment combination stays within the bounds of what we were expecting, we stay with it; if not, we get out.  We trade NOTHING that does not do what it is supposed to.

Controls & Checks

Once the full scenario is clear, several portfolio balancing checks are run. 

The most important of these is whether the portfolio is still self-hedging: one aim of trading in a widely diverse set of holdings is that they are as risk-independent as possible; that is the exposure to any one particular market or risk is minimised.

However, fast-moving markets can shift this balance very quickly; so it can be necessary to trade some key ‘headline markets’ several times a day.  These markets include: the 6 major currencies, the major US & EU stock indices; some commodity futures related to energy, metals & agriculturals. 

The important constraint on this intraday futures trading is that it is only allowed in one direction: it must reduce exposure to that market of the portfolio as a whole.

This high-frequency trading will take some while to automate fully.

Another related check is on weak or rogue combinations. If a particular investment composite is requiring constant adjustment; or if it shows an unacceptable sequence of adverse days, even within pattern; or if it exhibits anyone of a number of undesirable instabilities; it is removed from the portfolio.

The basic outlook is there is no need to remain in any investment; that falls short of expectations; there are always more desirable opportunities arising each and every day; the scan & search software reveals where they lie.

The ultimate aim of all these controls and the criteria by which they are judged (by comparing the outcome if they had not been applied) is the reduction in adverse movements of the whole portfolio.  We think it possible to find yet further improvements so that the number of ‘successful days’ consistently exceed 90%.

What kind of portfolio do we get?

The essential philosophy is thus to deliver the best possible portfolio for the given market environment.  It does this by recognising that different market environments exist. 

These can be categorised in terms of the 12-component vectors for Risk, Reward & Co-dependency for each major sector: an empirically measurable view of the state of human sentiment; that is far more detailed & precise than anything previously conceived.

However, it must be stressed that the investment strategy requires no fundamental view of the market.  Its aim is to grow a portfolio that is immune to the short-term fluctuations created by unpredictable news events; regardless of which phase of market sentiment currently predominates.

We describe such a portfolio as being Risk-Neutral: in summary, a portfolio that attempts to eliminate all known risks, as can be measured by human interaction with them; leaving only the unquantifiable unknown left to ponder.  We shall have to deal with that later.

This document should nevertheless have set out why our performance is so different to anything else; and will remain so. In absolute truth it is the only Hedge Fund.

Frank Dunn
CEO Market Maths Ltd

Toxic debt

My friend, Frank Dunn, says in an email today about the huge bail out of the US banking system being agreed this weekend;

Basically if the US do not implement this, the global financial system will stop working: the interbank channels are clogged up with toxic debt and only the heaviest laxative will clear them out.

Not a bad way of putting it really.

We could do so much better

A guest post from Frank Dunn, an English physicist, mathematician and polymath, best summed up as an answer to why "we could do so much better, be so much better; if only we wanted to".

Taleb: what fools these mortals be?

Reading Brian Appleyard on Taleb in the ST Magazine (Cover Article, Expect the Unexpected, Prophet of Loss) left me with a sadness I have felt many times before.  Appleyard writes sympathetically & warmly of Taleb.  I too have much sympathy with Taleb. He writes and talks warmly and wisely on the foolish hubris of financial maths.

However, even he has misunderstood several key issues.  He also gives the impression we can do no better.  He, like Donald Rumsfeld seems to have given up: “There are things that we do not know we do not know”.  You can hear mediocre scientists saying much the same thing about consciousness or quantum theory: “there are things that we are perhaps not meant to know”; or similar specious superstitious nonsense.   I could write several books on how we could do better.  Actually I already have, but I will keep it brief here.

Ostriches, not Black Swans

The ‘Black Swans’ that Taleb describes so poetically have been known for a few decades to students of market behaviour by a far less glamorous name: ‘Fat Tails’.  These are the extreme events that happen far more often than they should, if you believe in conventional financial maths.  These ‘extreme events’ or ‘outliers’ have been ignored for all this time as if they did not matter.  However, ‘Fat Tails’ are more than merely important: they reveal the real, true risks of investment(s).  The problem is thus not so much the existence of Black Swans, but the ostriches who have chosen to ignore them.

Fooled by Uncertainty, not Randomness

Financial markets are conclusively NOT random.  If they were, the ‘Fat Tails’ full of ‘Black Swans’ would not be there.  Instead there would be a nice simple ‘Bell Curve’ of the ‘Normal’ statistics of random behaviour.  Taleb thus confuses randomness with uncertainty.

Financial markets, like human beings behave neither deterministically nor randomly.  They exhibit the open-ended uncertainties of choice; human choice.

There ARE Better Models

Using Network Models, it is possible to understand the effects of individual free will, as well as the collective free will of groups or crowds.  The essential differentiating feature of networks is they allow for influence (or cause) to be non-local; for remote events to influence local outcomes. 

Inside the human brain, this means we can collect and organise information before we are ready to act.  Externally, it reveals our collective, group or crowd behaviour; as well as how crowds can be often foolish but sometimes wise.  This tribal, organised and communal behaviour gives the lie to the Darwinist illusion of man as ruthless individual.  We are team players and Dawkins is yet another deluded fool.

However, these network models are still very crude and there is much work yet to do.  In particular, as Penrose pointed out 20 years ago we will need better computers to model the human brain accurately: non-algorithmic quantum computers.

We could do so much better, be so much better; if only we wanted to.

Who is the Bigger Fool, the Mathematician, or the Central Banker?

So financial mathematicians turned out to be cheap salesmen like so many others; like so many politicians, lawyers, pensions sellers, IT & management consultants; pouring all their energies into flogging defective products they already had; rather than developing better offerings.

The question then must be: why do we all buy so much specious mediocrity?  Why are Central Bankers, just like the typical consumer so eager to buy all the rubbish proffered to them?  Perhaps it is because no-one thinks too deeply any more; anything too difficult is left to some arbitrary ‘expert’?

Perhaps, like Taleb we enjoy parties too much?  Ironically, we are all too busy ‘networking’ to see how networks really work. I hope we sober up soon.

Attention as a product

Image via Wikipedia

Ronald Woperis invented the idea of attention as a product.  A Twitter account is on sale at eBay from a guy who has amassed over 1500 followers. Is this the first indication of the monetary value of one's network of relationships?  Can attention be bought by individuals in the same way advertisers buy eyeballs?

Not winning by not sharing

When one of the world's richest men says; "The trouble with America is that it keeps helping people like me, and I don't need any help." -  Warren Buffett - it's not a good sign. The increasing concentration of wealth in too few hands has historically led to disaster, collapse and dissolution.  The American Empire will be no different. On the bright side, events like this trigger re-birth and hopefully the widespread adoption of decentralised organisation.