In the second half of the twentieth century it has become very popular to seek optimal solutions to a broad spectrum of problems: portfolios, engineering systems, strategies, traffic systems, distribution channels, networks, policies, etc. But have you ever wondered if optimal really means best? Well, it does not. Optimality is not the most convenient state in which to function. The reason?
Optimal solutions are inherently fragile
Anything that is optimal is, "by definition",
fragile, hence vulnerable. This is the price one pays for excessive
specialization or extreme organization. Let us see why.
There are very few things that are stationary. In
fact, we live in a quickly evolving environment in which there is little
time for equilibrium and in which irreversible and dissipative
mechanisms, together with chaos, randomness, not to mention extreme
events (the socalled Black Swans) produce a sequence of unique events
in which only fundamental patterns can be distinguished but in which the
search for repeatable details is futile. This simple fact clashes
frontally with the concept of optimality which hinges on precision and
details. Sure, one can identify sweet spots in a multidimensional
design space. From a mathematical perspective many things are possible.
However, the dynamic nonequilibrium character of Nature guarantees
that the conditions for which a given system has been optimized soon
cease to exist. The pursuit of perfection is, therefore, an attampt to
ignore the ways of Nature and Nature taxes similar efforts in
proportion to the magnitude of the intended crime.
The above is true not only in the global economy. In the biosphere it is also risky to be optimal, precisely because ecosystems are dynamic, and there is little time to enjoy optimality. As Edward O. Wilson stated in one of his wonderful books: "excessive specialization is a tender trap of evolutionary opportunism." Nature very rarely tolerates optimal designs. In fact, natural systems are, in the majority of the cases, fit for the function, not optimal.
But there is more. High complexity compounds the dangers of optimality. As a system becomes more complex, approaching its own critical complexity, it possesses an increasingly large number of the socalled modes of behaviour
(or attractors). Because these modes of behaviour are often very close
to eachother, tiny perturbations are sufficient for a given system to suddenly transition
from one mode of behaviour to another. These sudden mode transitions
are more frequent as complexity approaches its upper limit. This is why
humans intuitively try to avoid highly complex situations  they are
unmanageamble precisely because of the mentioned unexpected mode
transitions. In layman's terms, high complexity reflects a system's capacity to deliver surprises.
This is why when speaking of a highly complex system a good design is
not an optimal one but one that is fit and resilient. In other words:
Attmpting to construct optimal solutions in the face of high complexity increases the cost of failure
The following question arises at this
point. Knowing that an optimal system is fragile, why then not design
systems to be suboptimal in the first place? Why not settle for a
little less performance, gaining in robustness and resilience? Why this
obsession to be perfect? Why push a system into a very tight corner of
its design space, out of which it pops out at the snap of the fingers?
Why do people pursue optimal solutions knowing that an optimal system,
precisely because it is optimal, can only get worse, never better? As
the ancient Romans claimed even the Gods are powerless against
stupidity.
But how do you get a solution that is fit and not
optimal (=fragile)?. More than a decade age we have come up with a very
simple algorithm called SDI (Stochastic Design Improvement) which is
described here and which establishes the following new paradigm in system design:
The above philosophy is superior to conventional approaches to design, strategy and decisionmaking because it is tailored to highly uncertain, interconnected and turbulent environments, in which fitness counts much more than ephemeral perfection.
Our economy (but not only) is fragile because
everything we do is focused on maximizing something (profits,
performance, success) while minimizing something else (risk, time,
investment, R&D) at the same time. This leads to strains within the
system. Everything is stretched to the limit (or as much as physics
will allow). This is exactly what one should not do when facing turbulence. The focus should, instead, be on:

Wednesday, 3 July 2013
Does Optimal Mean "Best"? Not Really.
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