Making decisions

Greedy algorithms are simple approaches to solving problems in computer science. The basic principle is that you have an agent who makes decisions to maximize utility/reward based on the current state. The principle seems logical but when you apply the approach to hill climbing, for example, the basic flaws are exposed. The basic problem is that the algorithm can get stuck in local maxima or minima depending on what you want to optimize for.

This is not meant to be a lecture on computer science and in fact my point in writing this blog is to see how this approach applies to life. It seems that the basic approach of always maximizing for the short-term can lead to situations where you get stuck because you aren't willing to make short term sacrifices for long term rewards.

Basic lesson: This myopic way of thinking has been proven sub-optimal in computer science, so there is no reason to believe that it will work in life.