*Prisoner's Dilemma*has become a real-world-phrase here and speculated about the terms

*Venn Diagram*,

*Zeno's Paradox*, and

*n+1*here.

I recently came across a pair of words that are related--- one of them seems to be (like Prisoner's Dilemma) going from MATH to THE REAL WORLD. The other one is very odd in that I've seen it in the REAL WORLD but it SHOULD be in MATH.

*Long Tail:*A Probability distribution has a

*long tail*if there are MANY items that have a SMALL but NON-ZERO prob of happening. This is a term in probability. However, I have seen it used in the REAL WORLD as in

*Amazon has a long-tail strategy*meaning that they will sell LOTS of DIFFERENT things even if the number of people buying some of them is small (like this which is ranked 9,578,520- though I doubt they can be that precise). This article from the Atlantic Monthly points out that ESPN used to have a long tail strategy (e.g., showing Billiards and others sports that are not that popular, but ALOT of them) but then abandoned it for... see next definition. Note that the term

*Long Tail*is used for both a type of Prob Dist and a marketing strategy related to it. How common a word is

*Long Tail*? It gets 66,500,000 hits on Google. The first page has the definition above only. The 10th page had about half of the hits with the def above.

*Fat Head:*A strategy where you concentrate on just a few items. ESPN is doing that by covering just a few sports, but the most-watched ones (too bad, I was hoping they would cover my favorite sport, chess boxing). This SHOULD be a math term for a Prob Dist with just a few points of high prob. I asked my friends in the ML community and he assures me that NO its not a math term--- but it SHOULD be! How common a word is this? It gets 2,300,000 hits on Google. The first page seems to have NOT have ANY reference to the definition above.

SO- this COULD be a case where a term used in the REAL WORLD migrates to MATH with essentiallythe same meaning. This isn't that uncommon (the term Continuity comes to mind) but this timeI will have predicted it! Maybe I should do Machine Learning.