These are terms used by psychologist Robin Hogarth, and what a “kind” learning environment is, is one where patterns recur, ideally a situation is constrained – so a chessboard with very rigid rules and a literal board is very constrained – and importantly, every time you do something you get feedback that is totally obvious, all the information is available, the feedback is quick, and it is 100% accurate. And this is chess, and this is golf: you do something, all the information is available, you see the consequences, the consequences are completely immediate and accurate, and you adjust accordingly. And in these kinds of “kind” learning environments, if you’re cognitively engaged you get better just by doing the activity.
On the opposite end of the spectrum are “wicked” learning environments. And this is a spectrum from “kind” to “wicked”. In “wicked”learning environments often some information is hidden. Even when it isn’t, feedback may be delayed, it may be infrequent, it may be nonexistent, or it maybe partly accurate or inaccurate in many of the cases. So the most wicked learning environments will reinforce the wrong types of behaviour.
One of the examples that Hogarth talks about is a famous physician, a famous diagnostician, who became very prominent because he could accurately tell that someone was going to get typhoid weeks before they had any symptoms whatsoever. And the way that he would do that was by palpating their tongues with their hands… and it turned out… that he was in fact giving people typhoid by feeling around their tongues from one typhoid patient to another. And so in that case, the feedback of his successes taught him the wrong lesson.
Now that’s a very extreme case. Most learning environments are not that wicked. But most learning environments are not nearly as kind as chess and golf either, and most of the areas that most of us work in do not have built in rules and recurring patterns that we can rely on, or built in feedback that is always immediate, automatically comes, is complete and fully accurate.
So in that sense things like golf and chess are poor models for extrapolating most things people want to learn, and in fact one of the reasons chess is – relatively speaking – easy to automate is because it’s such a kind learning environment. So there’s a huge store of data, very constrained situations, repeating patterns – so the kinder a learning environment is, the more amenable it is to both specialisation and to being automated.David Epstein – on Econtalk (May 27, 2019) with Russ Roberts