This one is a little meta, so as usual I’m going to open up by breaking down what the what I mean with this title. If you’ve been reading this stuff up to now then you know what I mean by learning and that memorization is for lazy teachers who can’t teach (that and Doctors). But I digress, when I say learning this time, what I really mean is the process of experimentation and looking for an ideal state from which to execute whatever you happened to be doing. The ~10,000 hours you need to truly master a skill, or at least the part of those hours that are actually productive.
This definition is going to be important, so keep it in mind as you read ahead. Annnnd…Go:
I am very good at games.
This is a statement is an extremely broad one, more-so then most give it credit for being. Mastery of a single game can in itself take an obsessively large amount of time, and each game has a different set of skills, often wildly different ones, which it requires to do well within. A (good) Collectible Card Game, for example, requires strong strategic thinking to come up with a strong concept for your deck; enough game knowledge to actually implement it; tactical and metagame knowledge to execute your plan competently when your actually playing; and the ability to bluff and counter-bluff so you can predict what your opponent is doing without giving him or her the same knowledge. In short, there’s a huge number of skills that go into it, and were you to compare this to, say, Mario, you would find a similarly massive list of skills with no overlap whatsoever.
Now like I said, I’m very good at game, and a very broad range of them at that. Part of that is a lifetime of experience, but as established there are an infinite number of skills that games draw on and I’ll never do all of them, much less master them all. No, the reason I can do well, quickly, at such a wide variety is because I’ve done what I put up there in the title.
Teaching people how to learn is something that a lot of older games are very good at. A lot of new games are too, though the hand-holding you get in a lot of more recent works really hurts them on this front. Figuring out and discussing how they do this is an assay in itself, so I’m not going to go there. What I will do, however, is explain the key points to the whole thing:
- Find out what, exactly, it is you’re trying to do
- Do that thing (and ignore stuff that isn’t it)
- Experiment with the hard stuff (but not all at once)
- Be greedy
And that’s about it. Most of these seem pretty obvious when you say them out loud (or type them as the case may be) but I guarantee you 1 in 2 people reading this don’t follow through on it. Hell, I don’t follow it with everything I do, despite it being a lesson I’ve been learning for the last 20 years. It is one thing to know something and another entirely to actually be able to execute it (where have I heard that before =p) and Metalearning (which is what this is, really) is a very difficult thing to do innately.
Oh, there is one other thing that I should probably address before signing off. “Be Greedy?”, you might be saying, “Whatever does that mean??”.
It means to be greedy with your results and be greedy with your methods. Do the things that take less resources (your time, incidentally, being a resource) and give you better payoffs. You won’t always be successful. Being greedy also means taking on more risks in fulfilling that greed. But it also means pushing yourself out of your comfort zone, to do new things that might be better, find out the things that don’t work and generally be more productive in the long run.
And on that note, learning how to judge Risk Vs Reward is probably a good place to start.
