Complexity Economics Engineering Medicine Uncategorized

Complexity of Coupled and Merged Systems

It is popular in the conventional ‘qualitative’ approach to complexity – whereby a metric of complexity isn’t at all considered – to say that “systems are complex” when “the whole is greater than the sum of the parts”. Physics teaches us that most, if not all systems in the universe follow this rule. Spontaneous emergence of structure or new hierarchies has been taking place since the Big Bang all over the place. There is nothing extraordinary about that. That’s what is supposed to happen.

In one of our previous blogs we have shown how for a certain class of systems – coupled systems – the complexity of the whole was always greater than the sum of the complexities of the components. Now we take a look at merged systems, i.e. systems which are simply the result of superposition of two ore more systems.

These simple considerations show how it is possible to construct a system that is less complex than the sum of the complexities of the components. This may be useful, for example, when considering a merger of two companies and the respective balance sheets are simply “added” (with all the necessary modifications of course). In fact, 70%-80% of mergers fails because, with all likelihood, a monster is created which is far more complex than the sum of the complexities of the two companies and which nobody can handle. This happens typically when a merger between two not so synergetic businesses is forced.

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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