“ In the wicked world, with ill-defined challenges and few rigid rules, range can be a life hack.”.But those are poor models of most things humans want to learn.”

“When we know the rules and answers, and they don’t change over time-chess, golf, playing classical music ( kind world)-an argument can be made for savant-like hyper-specialized practice from day one.The problem is that we often expect the hyper-specialist, because of their expertise in a narrow area, to magically be able to extend their skill to wicked problems. Facing kind problems, narrow specialization can be remarkably efficient. “ Facing uncertain environments and wicked problems, breadth of experience is invaluable.Wicked problems (better for generalists): Uncertain environments, ill-defined challenges, few rules, rapidly changing, etc. Kind problems (better for specialists): Certain environments, specific challenges, rigid rules, unchanging, etc.

The world has kind problems & wicked problems. So is he actually just arguing against superficial pattern matching? Or maybe in wicked environments, each problem requires new interdisciplinary thinking and just because you've solved a problem once, doesn't mean it'll help you in the next, as it would in chess with its recurring patterns/issues.1. Is this what the author is distinguishing? I'm confused because it seems he argues against pattern matching for many complex situations because predictability is low, but if this was the case, pattern matching on any level shouldn't be useful because even when a situation seems conceptually similar to another one, it very well may not be (hence, low predictability). The first is more concrete, superficial pattern matching (which is sufficient for kind environments), whereas the second is deeper pattern matching (necessary for wicked environments). My issue here is: both of these seem to be pattern matching. Instead of pattern matching, he argues for learning broadly, in many subjects, so you can use a technique whereby you think of situations that are conceptually similar and see how solutions to those can apply to the one you're looking at. It fails, he says, in 'wicked' environments, where you constantly face patterns you've never seen or indeed, like in predicting the stock market, solving a long-unsolved problem, etc. He claims hyperspecialization works in fields with 'kind' environments, where pattern-matching is key like in chess, music, or even firefighting because studying the patterns truly makes you an expert.

David epstein range review