In what kind of real-life situations can we use a multi-arm bandit algorithm? Multi-arm bandits work well in situation where you have choices and you are not sure which one will maximize your well being. You can use the algorithm for some real life situations. As an example, learning can be a good field: 

If a kid is learning carpentry and he is bad at it, the algorithm will tell him/her that he/she probably should need to move on. If he/she is good at it, the algorithm will tell him/her to continue to learn that field. 

Dating is a also a good field:

You're a man on your putting a lot of 'effort' in pursuing a lady. However, your efforts are definitely unwelcomed. The algorithm should "slightly" (or strongly) nudge you to move on.

What others real-life situation can we use the multi-arm bandit algorithm for?
PS: If the question is too broad, please leave a comment. If there is a consensus, I'll remove my question.
 A: When you play the original Pokemon games (Red or Blue and Yellow) and you get to Celadon city, the Team rocket slot machines have different odds.  Multi-Arm Bandit right there if you want to optimize getting that Porygon really fast.  
In all seriousness, people talk about the problem with choosing tuning variables in machine learning.  Especially if you have a lot of of variables, exploration vs exploitation gets talked about.  See like Spearmint or even the new paper in this topic that uses a super simple algorithm to choose tuning parameters (and way outperforms other tuning variable techniques)   
A: They can be used in a biomedical treatment / research design setting.  For example, I believe q-learning algorithms are used in Sequential, Multiple Assignment, Randomized Trial (SMART trials).  Loosely, the idea is that the treatment regime adapts optimally to the progress the patient is making.  It is clear how this might be best for an individual patient, but it can also be more efficient in randomized clinical trials.  
A: They are used in A/B testing of online advertising, where different ads are displayed to different users and based on the outcomes decisions are made about what ads to show in the future. This is described in nice paper by Google researcher Steven L. Scott (2010), there was also a page that is currently offline, but available through archive.org.
A: I asked the same question on Quora
Here's the answer


*

*Allocation of funding for different departments of an organization


*Picking best performing athletes out of a group of students given limited time and an arbitrary selection threshold


*Maximizing website earnings while simultaneously testing new features (in lieu of A/B testing)
You can use them anytime you need to optimize results when you don't have enough data to create a rigorous statistical model.

