75 years is the approximate period of time you’re going to be alive. A big part of this time is already reserved for a total engagement in a typical conventional mission that includes education, career, jobs, marriage, a happy family and so on. The last ten years or so, you are most likely to be more aware of the fun you’ve missed out.What made you stick to this game till the very end? What made you commit to it this much? You might be happy and proud with what you’ve accomplished. You may have created the billion-dollar startup you always dreamed of and made yourself a place among the elite.But is that all? Maybe you could’ve done more, or maybe you could’ve done better.How can you make sure that you’ve seized all of your chances and that you really made the most out of your lifetime?
Well, we have some good news! Nearly everything you do, every choice you make, generates a problem that can be projected in recreational mathematics and can therefore follow an algorithm that gives you the optimal solution for your problem.To make sure you are living the good life, which is to say you are making the right choices, mathematics and computer science combined can help conquer this amazing decision making challenge.
When having to choose between sticking to what you already know and good at or trying to learn something new, you are faced with the fear of the unknown.You cannot guarantee that a new experience will bring you the same amount of joy and satisfaction your favorite experiences do. And if you’re going to keep on doing the same thing over and over again your life will be unbearably tedious. You will never have favorite things and you will never be able to appreciate what you have now. In computer science, this is called the Explore/Exploit trade off (used in reinforcement learning).
A dilemma we are all familiar with: should you gather more information(Explore) or should you start using the data you already have(Exploit)? Obviously the answer is to find the right and most efficient combination. John Gittin’s mathematical model assigns a number for each of your options taking into consideration the effort and time you sacrifice.This number is the Gittin’s index and simply the best option is the one with the highest Gittin’s index. Given the outcomes of each and every option we have, we can certainly make the right decision. But it becomes much more challenging when we have no clue of how things are going to turn out. Adding to this the time factor, the problem grows much more complex and elevates to a discounting problem due to the uncertainty of the future.
In simpler words,when you have to choose between trying something new and committing to your old habits time becomes a crucial factor.The more you go into the future the less certain it becomes that you’ll be able to enjoy the things you already like and are good at because the less certain it becomes that you are going to live at all (you and all outer conditions that make the situation possible).
Algorithms including Gittin’s index take all this into account, evaluate the total reward you receive from your options and return a score referring to its potential gain. The whole thing often boils down to one principle “ The gain of potentially having a new favorite exceeds the loss.“. Such conclusion is certainly inciteful for adventures yet it cannot be generalized into a law. For Gittin’s formula is highly dependent on the amount of time you have left, your previous experiences,the current situation and a bunch of other variables.
Many more lines can be written on the course, but I shall leave to you the pleasure of discovering all the awesomeness of Gittin’s index. Its ingenious way in providing answers for complex daily problems, which resolution is usually out of the human league, is absolutely fascinating. Stopping problems like the secretary problem and questions like “what is the best way to find love?” Are in fact among the algorithmically resolved problems using the look and leap strategies. Below you’ll find attached a couple of links that can help you dive deeper into this.