Climbing the Wrong Hill | Cdixon Blog

People early in their career should learn from computer science: meander some in your walk (especially early on), randomly drop yourself into new parts of the terrain, and when you find the highest hill, don’t waste any more time on the current hill no matter how much better the next step up might appear .

But the lure of the current hill is strong. He ends up falling for a common trap highlighted by behavioral economists: people tend to systematically overvalue near term over long term rewards . This effect seems to be even stronger in more ambitious people.

Going back to the job candidate, he has the benefit of having a less foggy view of his terrain. He knows (or at least believes) he wants to end up at the top of a different hill than he is presently climbing. He can see that higher hill from where he stands.

Another and generally better algorithm has you repeatedly drop yourself in random parts of the terrain, do simple hill climbing, and then after many such attempts step back and decide which of the hills were highest.

A more sophisticated version of this algorithm adds some randomness into your walk. This gives you a better chance of meandering near the bigger hill before you start your focused, non-random climb.

Consider the simplest algorithm. At any given moment, take a step in the direction that takes you higher. The risk with this method is if you happen to start near the lower hill, you’ll end up at the top of that lower hill, not the top of the tallest hill.

A classic problem in computer science is hill climbing. Imagine you are dropped at a random spot on a hilly terrain, where you can only see a few feet in each direction (assume it’s foggy or something). The goal is to get to the highest hill.