Before you start to solve a problem (a numerical problem) you should check its conditioning. An ill-conditioned problem will produce very fragile and unreliable results – no matter how elegant and sophisticated solution you come up with it will be vulnerable. If decisions or strategies are based on fragile outcomes, those decisions and strategies will…
Prologue from “Governing a Liquid Society – How to Save Democracy From Itself” In the past the Earth was populated by numerous civilizations. The Greeks, the Incas, or the Romans are just a few egregious examples. Because the temporal and spatial correlation between those civilizations was often non-existent, or very limited, if one happened…
Since its founding in 2005 Ontonix has focused its QCM (Quantitative Complexity Management) technology on protection, problem prevention, ‘robustification’, early warnings and identification of crisis precursors in diverse fields and industries (manufacturing, engineering design, economics, finance, traffic control, medicine, etc.). The present blog introduces a new exotic application of the QCM technology – the use…
Remember the recent British Airways IT meltdown? Problems with power supply are believed to have caused the crisis. Note the word “believed”. According to the mentioned source, when the power came back online, it did so in an uncontrolled manner, damaging the IT system and initiating a sequence of events that plunged the system…
We reproduce verbatim a very interesting article by Angel Gurría, OECD Secretary-General. All our blogs are original, with very minor exceptions. This is one of those exceptions. An exceptional article: “The recent financial crisis exposed some serious flaws in our economic thinking. It has highlighted the need to look at economic policy with more critical,…
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…
