In this post, I want to explain a bit about my research experience in Online Stochastic Matching. But as my research for this project is not finished yet, I will just explain the preliminaries of my research and the way I (as a junior researcher) look at these type of problems.
We used to believe that to have the best final product, we should make a competitive environment for all companies. This way they will do their best to provide us a high-quality product with the minimum cost. Although this intuition seems to be true all the time, there are some cases in which the best outcome happens when we restrict this type of competition between different companies. For instance, consider the competition between different navigation assistant applications such as Google Maps, Waze, etc. They are trying to always give you the best possible route. Otherwise, we probably will not use them again so they will become extinct! Although this competition seems very nice, in this post, I will explain how this competition can lead us to a bad outcome for the society of drivers!
For me, a question arises when people use MSE as an objective function for their learning tasks. The question is: WHY?? Why?? But when you ask this question you probably get answers like:
- Since it works well on this dataset!
- Because we want to give more penalty for bad predictions (in comparison with l1-norm)
- Computing the derivation of MSE is simple (in comparison with l1-norm)