I recently learned about a pretty well-known concept in math/compsci called the “Greedy Algorithm.”
To try to summarize it quickly: it’s a decision-making technique that maximizes your local decisions, with the hope that by grabbing at the best possible outcomes from what’s immediately nearby, you’ll be on your way to finding your long-term best outcome (the global maximum).
With computers (and in real life), this doesn’t really work all too well. Yet many of us might unconsciously playing this game. I see it all the time in the tech world, among media companies, among highly ambitious college students deciding on career moves, etc.
This is the Greedy Algorithm visualized:
If you start at point A, the Greedy Algorithm will find the nearest local maximum. In this case, that’s B. So being at B is much better than being at A, for sure– if you have a problem with making decisions at ALL, the Greedy Algorithm gets you to at least make a decision.
But by moving to B, you completely ignore/ lose track of M.
I see a lot of people who end up on the track to M (a more nonlinear track, something that requires more time, less immediate/instant reward), but then backtrack and shoot for B. The B is more predictable, and it’s easier to see that you’re headed there.
We’re really bad at developing a good feel for long-term value. I think a lot of times our gut knows that we should do things a certain way, even if the pay-off will be distributed at a longer scale. But because we are a “grass is greener” type of species, we aren’t often disciplined enough to stay the course on this crazy M path.
ammar mian 