Complexity Economics Engineering

Can Simulation Technology Really Help Companies Do Business?


For someone who has been doing simulation for the last 30 years it is not easy to say no. Large consulting firms are correct when they state that Complexity Management is going to become an indispensable tool of the future. Ontonix claims is it also the foundation of a new way of seeing corporate strategy. However, we wish to deliver a few words of caution. In fact, the message that is being spread in a wave of White Papers is confusing and damaging when it falls into the hands of the uneducated public (and managers!). And most of the public (and managers) is, by definition, uneducated.It is claimed that collective knowledge doubles every 15 years, that computer power doubles every two years and that product development and launch cycles are getting shorter. State-of-the-art simulation technology is supposed to help companies make use of this huge increase of data and to do better business. It is also claimed that over the past 50 years simulation technologies have enabled major advances in diverse fields. While nobody here is claiming that computers are useless, these advances are not so evident. The global economy is in a mess, the financial crisis is of planetary proportions and recovery is not around the corner. Where were these super simulation models and methods five years ago when the crisis was around the corner? Why are major aircraft manufacturers years late when it comes to launching new products? Why are global warming models so unreliable? Why is the Space Age over? Are we really progressing?Simulation is useful but only in very specific cases and, evidently, only in the right hands. Simulation needs models and models are full of assumptions. The first assumption is the type of model that fits a given problem. Take 3 points on a plane. You can fit a parabola that goes exactly through each one. Or a circle. Which do you chose? Once you’ve picked a model you have decided irrevocably what it will be able to deliver (technically this is called ‘unwrapping’). It’s a bit like “playing God”. So, selecting modelling techniques is the first source of risk. Now, models need assumptions. Example: the value of real-estate will always increase. True in many cases but not in all the cases. Think of the sub-prime bubble. That assumptions, on which many models were based was dead wrong. Now we are paying the consequences.

Another rampant practise of the modern age is optimisation. Optimisation is a sophisticated means of creating fragile solutions. Optimising a chip layout is one thing but building optimal portfolios is another. Here you need models, fancy models, and, many many assumptions. Again, take a look at the World’s economy to appreciate the consequences.

So why are simulation techniques successful sometimes (mainly in specific, scientific/engineering applications) but not when it comes to the economy, business or traffic systems? Life and the economy are turbulent and irrational. Our modelling and simulation techniques reflect old ways of thinking and fall flat on their faces when it comes to dealing with reality. We cannot just automate old ways of thinking hoping that in virtue of abundant teraflops things will simply work out fine. Some things just cannot be modelled. Take for example risk.

There is a fundamental principle – the Principle of Incompatibility – which states that as complexity increases, precision and relevance become mutually exclusive. In other words, as things get complex (and they seem to be) your statements about it become less and less precise. This means that as something becomes highly complex you can forget building models. You need to change strategy. A new approach is needed. You must change direction. Large consulting firms claim otherwise.

The Earth is a huge computer. It does things for real, it doesn’t simulate them. In effect, we are actually living on the surface of one huge supercomputer. It constantly floods us with unimaginable volumes of data. For free. However, we are being told to take some of that data, to fit models on top of it and to simulate  – in other words, to produce synthetic versions of the reality which we already have or soon will have. What is the scope? In most cases it is to predict the future. Prediction, however, is, in most cases, futile. Because of the physics we have the future is always under construction. This is why others, who realise that prediction is often irrelevant, also use models for a nobler cause, to try to understand the underlying phenomena.

So, it is a crime to be sitting in the middle of a gold mine of information (= Nature) and to build emasculated caricatures thereof. What we propose is:

1. Not spend time and resources on building increasingly complex (and irrelevant) models but,

2. Focus our efforts on the analysis of REAL data which the system called Earth produces free of charge.

But a new problem emerges. We must resort to new means of analysing this mass of data and turning it into something useful. As the state of our global economy shows, conventional means of data analysis, coupled with simulation (the so called Business Analytics) are a bit outdated, to say the least. What is the alternative?

Model-free methods. As things get messy, chaotic and turbulent a different approach is needed. In fact, with model-free methods you go to the next level. What you get is:

  • understanding of the structure of data – relationships, topology, hubs, information flow patterns, etc. Structure, not hundreds of pie charts, plots or surfaces
  • new means of parameter ranking
  • transformation of terabytes of data into megabytes of knowledge
  • measures of complexity and critical complexity
  • measures of resilience and fragility
  • global patterns

The most important of these is understanding. In order to understand Nature better we must analyse the data is provides us with in its pure form, not using methods which warp and distort the information it carries. With statistical (and other) techniques it is incredibly easy to destroy information. Model-free methods preserve all data and in its original form and shape. Building models is NOT the only way to proceed.

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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