A system—organizational, industrial, biological, environmental, or IT—is composed of components, objects, or members, each of which have specific properties that characterize its behavior in space and time. All members interact, impact, serve, and receive from other members in time and space. We can think of this as the connectivity or more specifically the time and space connectivity from which many possible combinatorial and dependencies result. Depending on the intensities of such intra- and inter-relations among components and their configuration, the overall system will expose behavior patterns and characteristics.

From this we can produce a set of quantitative and qualitative metrics that will provide a synthesis of what happens. This set of metrics will show the global characteristics of the system, but the ultimate target is contribution of each individual component and their interactions. This knowledge will allow us to properly identify the causal configuration. In this case, deconstruction theory becomes important to our goal of identifying the component, or components, that expose the system to a risk—in terms of limits beyond which the system will no longer work, service quality, or cost. Basically, if you want to understand the behavior of a system, you must deconstruct it and look at its components.

It is important to perform deconstruction in such a way that allows the shortest path to the identification of the risk component(s), the dynamic signature of what happens or may happen, the conditions under which a component will reveal the risk, and above all the actions required to proactively fix the problem while there is still an opportunity for a possible solution.

Over the last 10 years, we have been able to confirm that this approach yields significant contributions to the determination of risk and risk management in comparison to traditional methods. The suggested process of causal deconstruction has been applied many times on different business, industrial, economic, and services activities, and the results have been significant and exhaustive.

A Complex System under Optimal Control

By combining causal deconstruction theory and perturbation theory, a dynamic complexity problem can be accurately solved with the right level of representation and a good level of certainty on the reproducibility. This method shows great promise as a powerful process for risk identification, evaluation, management, and avoidance.

To determine the performance and accurately identify risky components within an open structure involving multiple orders perturbations, we use a layered hierarchical process based on the causal deconstruction to feed a mathematical hierarchy of specialized algorithms, which are computed and aggregated following the capabilities of perturbation theory. Through this approach, the behavior of a component determines its status that, with respect to others, will determine the characteristics of the component, its ability to deliver its service to the system, and to what extent. The environment is composed of the ensemble of components, the demand structures from each to all components, and the possible combinations that deliver a service based on multiple interactions.

From this point, the solution can be extended to meet the goals of optimal business control (OBC). In this case, a knowledge base and other automation technologies are used to observe the system in operation to identify dynamic characteristics that may lead to a risk. The ambition of these methods are to place the system under permanent control, so that it becomes possible to slow down the adverse effects of dynamic complexity or prepare for the avoidance of an eventual risk.