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The understanding, assessment and management of risk and uncertainty is important not only in engineering, but in all spheres of social life. Given that the complexity of man-made products, and the related manufacturing processes, is quickly increasing, these products are becoming more and more exposed to risk, given that complexity, in combination with uncertainty, inevitably leads to fragility. Complex systems are characterized by a huge number of possible failure modes and it is a practical impossibility to analyze them all. Therefore, the alternative is to design systems that are robust, i.e. that possess built-in capacity to absorb both expected and unexpected random variations of operational conditions, without failing or compromising their function. This capacity of resilience, main characteristic of robust systems, is reflected in the fact that the system is no longer optimal, a property that is linked to a single and precisely defined operational condition, but results acceptable (fit for the function) in a wide range of conditions. In fact, contrary to popular belief, robustness and optimality are mutually exclusive.
Complex systems are driven by so many interacting variables, and are designed to operate over such wide ranges of conditions, that their design must favor robustness and not optimality. Robustness is equivalent to an acceptable compromise, while optimality is synonymous to specialization. An optimal system is no longer such as soon as a single variable changes - something quite possible in a world of ubiquitous uncertainty. As the ancient Romans already knew, corruptio optimi pessima - when something is perfect, it can only get worse. When you're sitting on a peak, the only way is down - when you're optimal, your performance can only degrade. It is for this reason, that optimal systems are fragile. It is for this reason that a state of optimality is not the most probable state of a system. No, optimal is not best!
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