Complexity Economics

Probability of Default Versus the Principle of Incompatibility

According to the Millennium Project, the biggest global challenges facing humanity are those illustrated in the image above. The image conveys a holistic message which some of us already appreciate: everything is connected with everything else. The economy isn’t indicated explicitly in the above image but, evidently, it’s there, just as the industry, commerce, finance, religions, etc. Indeed a very complex scenario. The point is not to list everything but to merely point out that we live in a highly interconnected and dynamic world. We of course agree with the above picture.
As we have repeatedly pointed out in our previous articles, under similar circumstances:
  • it is impossible to make predictions – in fact, even the current economic crisis (of planetary proportions) has not been forecast
  • only very rough estimates can be attempted
  • there is no such thing as precision
  • it is impossible to isolate “cause-effect” statements as everything is linked
  • optimization is unjustified – one should seek acceptable solutions, not pursue perfection
The well known Principle of Incompatibility states in fact that “high precision is incompatible with high complexity”. However, this fundamental principle, which applies to all facets of human existence, as well as in Nature, goes unnoticed. Neglecting the Principle of Incompatibility constitutes a tacit and embarrassing admission of ignorance. One such example is that of ratings. While the concept of rating lies at the very heart of our economy, and, from a point of view of principle, it is a necessary concept and tool, something is terribly wrong. A rating, as we know, measures the Probability of Default (PoD). Ratings are stratified according to classes. One example of such classes is shown below:
Class     PoD
1              =<0.05%
2              0.05% – 0.1%
3              0.1% – 0.2%
4              0.2% – 0.4%
5              0.4% – 0.7%
6              0.7% – 1.0%
A rating affects the way stocks of a given company are traded – this is precisely its function. What is shocking in the above numbers, however, is the precision (resolution). A PoD of 0.11% puts a company in class 3, while a 0.099 in class 2. How can this be so? Isn’t the world  supposed to be a highly complex system? Clearly, if even a crisis of planetary proportions cannot be forecast, it not only points to high complexity (see the Principle of Incompatibility) but it also says a lot about all the Business Intelligence technology that is used in economics, finance, or management and decision making. So, where does all this precision in ratings come from? From a parallel virtual universe of equations and numbers in which everything is possible but which, unfortunately, does not map well onto reality. But the understanding of the real universe cannot be based on a parallel virtual universe which is incorrect.
The above example of PoD stratification reflects very little understanding of Nature and of its mechanisms. In fact, economic crises of global proportions suddenly happen. As Aristotle wrote in his Nikomachean Ethics: an educated mind is distinguished by the fact that it is content with that degree of accuracy which the nature of things permits, and by the fact that it does not seek exactness where only approximation is possible. 

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

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