Knowledge is an organized and dynamic set of interdependent rules. An example of a rule:
“if UNEMPLOYMENT increases then NEW HOUSE CONSTRUCTION decreases”.
This is an example of a fuzzy rule – no numbers just a global trend. Rules can be more or less fuzzy (or crisp) depending on how many experiments (data samples) they are based on.
What makes information fuzzy and less precise is noise and, in general, uncertainty or disorder. A great way to illustrate the concept is by analyzing, for example, a simple phrase, such as this:
This is an example of a simple phrase which is used to illustrate the concept of critical complexity.
Let’s introduce a few spelling mistakes:
Thos is a n exrmple of a simpcle phrqse whih I s us ed to illuxtrate the concyept of critizal com plexiuy.
Let us introduce more errors – with some imagination the phrase is still readable (especially if you happen to know the original phrase):
Tais xs a n exreple zf a sempcle phrqee waih I s vs ed eo illuxtkate the concyevt of crstrzal ctm plexihuy.
An even more:
Taiq xs a n exrepye zf d semicle pcrqee raih I s vs ed eo ilnuxtkare the cmncyevt tf crstrzaf ctm plsxihuy.
This last phrase is unreadable. All of the original information has been lost. We could say that the phrase before this last one is critically complex – adding a small dose of uncertainty (spelling mistakes) would destroy its structure. Systems which are on the verge of losing their structure simply because one sprinkles a little bit of noise or uncertainty on top, are fragile – they collapse with little or no early warning. This is precisely why in the case of very large or critical systems or infrastructures, such as multi-national corporations, markets, systems of banks or telecommunication and traffic networks, it is paramount to know how complex they are and how close to their own critical complexity they happen to function.
If you do know how complex your business is, and how far from criticality it finds itself functioning, you have a great early-warning system.