In today’s turbulent world, punctuated by destabilizing events of increasing intensity and frequency, policy makers, governments and the military often speak of preparing for the worst-case scenario. While it is impossible to make predictions of the future, one still needs to make decisions, devise strategies and move forward and attempt at least some basic risk management, but with very little knowledge of what those risks may actually be. However, in the context of high complexity, when turbulence and thousands of interdependent factors combine, decision making and risk management become extremely difficult. The goal of this blog is to explain why this difficulty.

What is of greatest concern are the so-called Black Swans – extremely rare events but with huge consequences – which are obviously very difficult to anticipate. Today, Black Swans preoccupy not only investors or corporations, but also regulators and governments. Given their peculiar characteristics, it is practically impossible to predict them and to counter their devastating events. The inherent criticality in a Black Swan event is not just the fact that it is a very rare event. The problem is that for each given system or scenario, there are many possible Black Swan events. In effect, in any given given context

there is no single worst case scenario – there are many

The number of possible Black Swans or worst case Scenarios increases with the complexity of the said context. This concept is difficult to grasp if one thinks the world functions as a three-dimensional reality where eveything follows a gaussian distribution. One extrapolates (using, with all likelihood, some linear regression model) the past and thinks that the future will follow the trend. Unfortunately this is not how Nature works. When high complexity kicks in, strange things tend to happen and predictability is something one must forget. The decision-making paradigm must shift from thinking one can predict what can happen and then prepare for it, to something different. We will show what that is.

High complexity wipes away of chances of predictability, but let’s take a quick look at the QCM (Quantitative Complexity Management) and what is boils down to.

A QCM analysis of a data set produces what is known as a Complexity Map – an illustration of how information flows within a given system. A very simple example is shown below.

The discs are known as hubs and they indicate variables that have a larger footprint on the entire system. This looks pretty stratightforward. Let’s also forget for a moment that the synthesis of the map involves a sophisticated mechanism for computing the so-called generalized correlations, which replace the popular and often risky linear (Pearson’s) correlations.

Once the Complexity Map is avaliable, the complexity of a given system may be computed, as well as its bounds, especially the upper bound known as critical complexity. So far so good.

However, what is not so obvious is that the Complexity Map is only the tip of the iceberg. A Complexity Map is a topological sum of other complexity maps known as modes. An example is illustrated below:

What most users of our QCM tools get to see is what is on the left hand side of the equation. Real, deep understanding of complexity and of the underlying system means knowing what is going on on the right hand side too. Each complexity mode has its own complexity as well as ‘intensity’. An example of how modes are arranged and classified is illustrated below:

Ontonix has recently launched the Black Swan Protection System. The tool, based on a given piece of data, generates automatically a multitude of feasible future scenarios and identifies the most unfavourable ones from a resilience, complexity and sustainability points of view. Scenarios are generated based on user-defined probability of an unlikely event occurring in any of the variables that describe the system. Indispensable for governments and decision-makers in a Crisis Management context, the tool exposes sets of worst case scenarios and identifies the factors that cause them. The number of worst case case scenarios increses quickly with complexity.

The severity of a Black Swan is defined by the probability of occurrence in terms of multiples of standard deviations. A three-sigma event, for example, occurs with a probability of 0.27% while a six-sigma event with a probability of 0.00000002%. With this information provided by the user, the system generates multiple scenarios based on the current Complexity Map and without the need to resort to lengthy Monte Carlo Simulation. In addition to the worst-case scenarios, the most likely one and the most favourable ones are also determined.

A simplified example of the type of scenarios that the tool generates are shown below.

Applications are numerous:

• Crisis Management
• Conflicts
• Finance, economics
• International relations, policies planning
• Strategy management
• Social engineering
• Critical infrastructure protection
• Insurance

The bottom line is that based on your data, we are able to identify scenarios that define your worst nightmares. Extrapolation of potential future scenarios using conventional techniques is not applicable. But even without looking at very rare occurrences, complex systems can still deliver surprising and unfavorable behavior.

It is because of the existence of potentially very many future scenarios, complex systems or products cannot be debugged fully before they are launched. In fact, modern products have become so complex that manufacturers launch them anyway and have the customers do the debugging for them. One can think of modern cars, which are full of software and gadgets, or other types of products which would require very long testing periods in order to identify potential adverse side effects.