The Principle of Fragility

The following equation, which we call the Principle of Fragility, has been coined by Ontonix in early 2005 and indicates why complexity management is a form of risk management:

Complexity X Uncertainty = Fragility        

In order to understand the Principle of Fragility let us borrow Fourier’s idea of variable separation and create a useful parallel. Let us assume, without loss of generality, that the term “Complexity” is specific to a certain system, e.g. a corporation, while  the term “Uncertainty” concentrates the degree of turbulence (entropy) in the environment in which the system operates, e.g. a market. The equation assumes the following form:

Csystem  X Uenvironment = Fragility         

or, in the case of a business,

Cbusiness model X Umarket = Fragility         

What the equation states is that in a market of given turbulence a more complex business model will be more fragile (exposed). In practical terms, the equation may be seen as a mathematical version of Ockham’s razor: with all things being equal a less complex compromise is preferable.

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Complexity Maps get a facelift.

Business Structure Maps (known also as Complexity Maps) have now a new look and feel. The size of the nodes (variables) is now function of its importance, or footprint on the system as a whole. This makes reading maps much easier as it is immediately clear where the important things are and where to start solving problems. The larger nodes are where there is more leverage and this is where one needs to concentrate.

Numerous interactive examples of maps may be seen here.



Get your own maps here.


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Complexity Impacts Negatively Portfolio Returns.

In a recent blog we have pointed out research conducted at the EPFL in Lausanne, Switzerland, which confirms that complexity impacts negatively portfolio returns. The research has now been concluded and the full report is available here.

The research has bee conducted using Ontonix's on-line system for measuring the complexity and resilience of businesses and portfolios.



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How Healthy Are the US Markets? A Look at a System of Systems.

The US stock markets indices have been enjoying upward trends for a few months now. When analysed one by one, the situation appears to be very positive. Based on the last 60 days of trading and on the values of "Open", "High", "Low", "Volume", "Close" and "Adjusted Close", we have analyzed the DJIA, S&P 500 and NASDAQ Composite markets separately and then as s single interacting system. Here the results (analysis performed on August 15-th, 2013).

The DJIA. Resilience:83%

The S&P 500. Resilience: 97%

  








The NASDAQ Composite. Resilience: 95%

Because of the inter-dependencies that globalization has created, no system acts in isolation and no system should be analyzed in isolation. All systems interact, forming a huge system of systems. To show how this can impact the big picture we have analysed the three markets simultaneously. This is the picture:

DJIA + S&P + NASDAQ. Resilience: 72%

In the above map the first six red nodes correspond to the Dow, the following 6 blue are the S&P, the remaining 6 nodes correspond to the NASDAQ.

The combined markets have a resilience o 72% even though the three markets boast values of 83%, 97% and 95%, with an average of 92%. Surprised? We put together three components, each of which has a resilience greater than 83% and the resulting system has a resilience of 72%! This is a great  example of  "the whole being actually less than the sum of the parts". So much for linear thinking. 




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Visual Analytics and Cognitive Computing - Ontonix Beats IBM

IBM has recently announced a new "way to program computers" - the so-called "cognitive computing" - see here - which uses "visual analytics" techniques to process (and display) data.

The method of "Visual Analytics" has been pioneered by Ontonix over a decade ago when we introduced and patented (in 2005) our model-free technique of data processing which actually mimics the human brain. The method doesn't use conventional mathematics or math models - it just "sees data" and extracts workable conclusions and knowledge from it. As data gets richer the system "learns" and accumulates the new information in the form of experience and rules.  See our recent blog on "Computing Knowledge" here, where we speak of extracting "Cognitive Maps" from raw data. More on the subject can be found in this other recent article. On model-free methods read here.

But there is more. We also measure the complexity of data as well as that of the resulting Cognitive Maps and, consequently, of the knowledge they contain. Knowledge and complexity are inextricably linked because complexity - which is measured in bits - actually quantifies the amount of structured information contained within a piece of data. But structured information is knowledge. More soon.





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How Complex is the World? We've Measured It.

Using annual data from the World Bank, spanning the last 5 decades, we have computed the complexity of the World seen as a system of interacting systems (countries). There are currently 196 countries (the US recognises only 195), each of which is monitored via a series of over 1250 indicators, covering the economy, energy, transportation, education, health care, infrastructures, agriculture, environment, telecommunications, finance, crime, military expenses, etc., etc. Doing the arithmetic leads to approximately 250000 parameters which describe the entire system, i.e. the World (List of indicators).

The results are quite astonishing - see plot below illustrating the evolution of complexity (middle curve) as well as of it upper (critical) and minimum bounds.

As a system evolves and develops new functionality it becomes more complex. This is natural. The direction of evolution in our biosphere is a good example: from single-cell organisms to mammals. However, each system in nature possesses also the so-called critical complexity (green curve in above plot). This too increases as the system evolves. The same may be said of the lower complexity bound (blue curve). When a system functions close to the blue curve, its behaviour is deterministic and predictable. The problem is to to stay away from the critical complexity curve because there things get chaotic and uncontrollable. The amount of chaos (uncertainty) in the system is reflected by its entropy, see plot below.

 

 In the 60s through the 80s complexity as well as entropy have been growing steadily. In the 90s things have slowed down and in the last decade both complexity and entropy have plateaued. This means that our rate of development and growth is slowing down. Keep in mind we're not talking of the economy, we're looking at the entire system. The economy is only one way to "measure" a system but there are many other facets too.

While it is not the goal of this article to provide an in-depth analysis of the reasons and implications of the above, we would like to draw attention to a few points. First of all, the ratio between  Complexity and Critical complexity has almost doubled in the period 1960-2010 - see plot below.

This means that compared to the 1960s, it is almost twice as difficult to understand the world and to govern it. This means that it has become almost twice as difficult to make forecasts, to do business, to get things done. The world is becoming a more intricate place. However, the system seems to have settled in a sort of equilibrium. The fact that we're still far from critical complexity is good news, the bad news is that the system seems to be stuck. The mild evolution of all of the above curves tells us that the current economic crisis seems to be symptom and not the cause of this state of affairs.

The analysis has been run at the CINECA Supercomputer Center in Bologna. The collaboration of QBT is also acknowleged.

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Italy Beats Moody's.

Moody's is the largest of the three major rating agencies. It employs 7000 people worldwide and posted sales of 2.7 billion in 2012. Since rating agencies are under heavy fire from the beginning of the financial crisis - in January 2011, the Commission of Inquiry Financial Crisis U.S. Senate stated that "The three rating agencies have been instrumental in triggering the financial collapse" - we have decided to calculate their ratings. In particular, we chose Moody's because it is the largest credit rating agency and also because it is perhaps the one that has downgraded Italian debt more aggressively than others. Obviously, Moody's is publicly traded and therefore subject to the dynamics of the markets as all listed companies.

In Moody's rating we did not assess the financial performance of the company or its ability to meet its financial obligations and even its probability of default. In other words, we have not performed a calculation of the conventional rating, but instead have we focused on another key feature for those who live in turbulent times: resilience, i.e. the ability to company to resist and survive sudden and extreme events (natural disasters, failures of large companies or banks, financial contagion, etc..). Since the global economy is constantly exposed to extreme events and turbulence, which will become not only more intense but also more frequent, resilience becomes a feature of an economy or a business that could make the difference between survival or its collapse.

For the analysis we used the quarterly information that Moody's publishes on its website. In particular, we used the following items:

1. Net income
2. Depreciation and amortization
3. Weighted average shares outstanding Basic
4. Provision for income taxes
5. Total expenses
6. Operating
7. Income before provision for income taxes
8. Revenue
9. Selling general and administrative
10. Operating income
11. Earnings per share Basic
12. Diluted
13. Diluted
14. Non-operating income (expense) net
15. Interest income (expense) net
16. Restructuring
17. Other non-operating income (expense) net
18. Net income attributable to Moody's
19. Net income attributable to non-controlling interests
20. Gain on sale of building

The Resilience Rating is as follows:

(see interactive Moody's Business Structure Map here).


The Resilience Rating of 72% is, on the scale of conventional ratings, equivalent to BBB-, one step from class BB +, the first of speculative ratings.

Given that Italy has often been targeted by Moody's we wanted to compare the resilience of both. Using macroeconomic data published by Eurostat, we obtain the following Resilience Rating:

(see interactive Business Structure Map of Italy here).




A Resilience Rating of over 75% places Italy two steps above Moody's, i.e. at the level of BBB +.

The result immediately raises the question: shouldn't he who has the power to judge others be the first to set a good example? Would you trust a coughing cardiologist as he smokes while recording your electrocardiogram?