Lentil convexity: Difference between revisions

Events which are ''really'' independent ''stay'' independent, however weird things get. The odds of flipping heads on a fair coin stays 0.5 however often you flip it, and whatever the previous results.<ref>Practical point though: the longer your sequence of heads, the greater the probability that the ''coin is not fair''.</ref> This makes the job of modelling truly independent events much, much easier. Your [[standard deviation]] stays put.  

Interconnected events don’t. They go from stable, most of the time, to flat-out nutso in extreme times. Fair coins don’t go nutso. Dice don’t go nutso. Lentil buying ''can'' go nutso. You can’t model it. You can’t predict it. The [[correlation]] between events then changes further ''because it’s gone nutso''. There’s a feedback loop.  

The point? Modelling normal distributions of independent events is easy, and safe. Modelling distributions of interconnected events isn’t. It isn’t just a case of more complex maths. It isn’t ''possible''. Now, mis-modelling overall lentil demand is a relatively low-stakes game: liable to annoy peaceniks — who are dispositionally unlikely to foment insurrection, and it’s kind of amusing anyway — plus, realistically (unless it ''is'' Armageddon, in which case a lentil shortage is no longer the problem) actual consumption of lentils won’t change, so the supply-shortage will quickly sort itself out, as the 95% find themselves long more lentils than they know what to do with and their part of the demand curve hits absolute rock bottom. So a spot of convcexity might not matter for the worlds’ lentil purveyors, but how about the global transport and hospitality industries? I mean, what would ''they'' do if everyone around the world, without warning, as one, stopped leaving their houses indefinitely?

 

Like ''that'' would ever happen.