Friday, December 28, 2012

Federal prisoners use snitching for personal gain
Snitching has become so commonplace that in the past five years at least 48,895 federal convicts -- one of every eight -- had their prison sentences reduced in exchange for helping government investigators, a USA TODAY examination of hundreds of thousands of court cases found. The deals can chop a decade or more off of their sentences.

How often informants pay to acquire information from brokers such as Watkins is impossible to know, in part because judges routinely seal court records that could identify them. It almost certainly represents an extreme result of a system that puts strong pressure on defendants to cooperate. Still, Watkins' case is at least the fourth such scheme to be uncovered in Atlanta alone over the past 20 years.

Those schemes are generally illegal because the people who buy information usually lie to federal agents about where they got it. They also show how staggeringly valuable good information has become --­ prices ran into tens of thousands of dollars, or up to $250,000 in one case, court records show.

Friday, December 21, 2012

Great Minds

I recently read Nate Silver's newest book, The Signal and the Noise: Why Most Predictions Fail – But Some Don't, which was good if you're wondering.  Chapter 11 is about the stock market, and he points out that there is some correlation between one day's rise or fall and the next's.  He then calculates that one could make quite a bit of money investing based on this fact, but that fees would kill it.  Finally, he notes that the correlation has disappeared recently, making the whole plan worthless.

Assuming you've memorized all my posts, you should now be realizing I made a post about the same thing.  His book came out September 27, and my post was on October 13, so it would seem pretty obvious that I read the book and got the idea from there (even though I didn't).  I'm surprised no one pointed this out in the comments.  I can only assume that this is due to a lack of readership of his book.

In another case, I recently discovered this post on the expected value of a Mega Millions ticket.  He calculates the expected value of the lesser prizes, calculates the amount of the jackpot one would actually get after taxes, and then uses the Poisson Distribution to calculate the expected value of the jackpot.  He then fits a polynomial model to the past data in order to predict what various jackpot's values will be.  In other words, exactly what I did.

The key difference is he didn't spread his through 3 long posts, filled with math, and no visual breaks in the wall of text.  Also, he didn't include an unnecessary free lesson on the guts of Linear Algebra. I'll leave it to the masses to decide which approach is better.

I made my first post on the subject in April 2012, and he made his in January 2011.  So it's debatable who copied who.

Time-machine-assisted plagiarism aside, it is interesting to see someone else tackle the same problem, and do it largely the same way, and produce very similar results.  One example of a difference is he calculated an expected value from the non jackpot prizes of $0.10, whereas I calculated $0.15.  The difference is he applied taxes to all the prizes, whereas I exempted the $150 and below prizes from taxes.  It is interesting how much of a difference that makes.