College Math Teaching

March 18, 2013

Odds and the transitive property

Filed under: media, movies, popular mathematics, probability — Tags: — collegemathteaching @ 9:51 pm

I got this from Mano Singham’s blog: he is a physics professor who mostly writes about social issues. But on occasion he writes about physics and mathematics, as he does here. In this post, he talks about the transitive property.

Most students are familiar with this property; roughly speaking it says that if one has a partially ordered set and a \le b and b \le c then a \le c . Those who have studied the real numbers might be tempted to greet this concept with a shrug. However in more complicated cases, the transitive property simply doesn’t hold, even when it makes sense to order things. Here is an example: consider the following sets of dice:

dice

What we have going here: Red beats green 4 out of 6 times. Green beats blue 4 out of 6 times. Blue beats red 4 out of 6 times. All the colored dice tie the “normal” die. Yet, the means of the numbers are all the same.

Note: that this can happen is probably not a surprise to sports fans; for example, in boxing: Ken Norton beat Muhammed Ali (the first time), George Foreman destroyed Ken Norton and, Ali beat Foreman in a classic. Of course things like this happen in sports like basketball but when team doesn’t always play its best or its worst.

But this dice example works so beautifully because this “impossibility of the dice obeying a transitive ordering relation is theoretically impossible, by design.

Movies
Since the wife has been gone on a trip, I’ve watched some old movies at night. One of them was the Cincinnati Kid, which features this classic scene:

Basically, the Kid has a full house, but ends up losing to a straight flush. Yes, the odds of the ten cards (in stud poker) ending up in “one hand a full house, the other a straight flush” are extremely remote. I haven’t done the calculations but this assertion seems plausible:

Holden states that the chances of both such hands appearing in one deal are “a laughable” 332,220,508,619 to 1 (more than 332 billion to 1 against) and goes on: “If these two played 50 hands of stud an hour, eight hours a day, five days a week, the situation would arise about once every 443 years.”

But there is one remark from this Wikipedia article that seems interesting:

The unlikely nature of the final hand is discussed by Anthony Holden in his book Big Deal: A Year as a Professional Poker Player, “the odds against any full house losing to any straight flush, in a two-handed game, are 45,102,781 to 1,”

I haven’t done the calculation but that seems plausible. But, here is the real point to the final scene: the Kid knows that he has a full house but The Man is showing 8, 9, 10, Q of diamonds. He knows that the only “down” card that can beat him is the J of diamonds but he knows that he has 3 10’s, 2 A’s. So there are, to his knowledge, 52 - 9 = 43 cards out, and only 1 that can beat him. So the Kid’s probability of winning is \frac{42}{43} which are pretty strong odds, but they are not of the “million to one” variety.

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January 17, 2013

Math and Probability in Pinker’s book: The Better Angels of our Nature

Filed under: elementary mathematics, media, news, probability, statistics — Tags: , — collegemathteaching @ 1:01 am

I am reading The Better Angels of our Nature by Steven Pinker. Right now I am a little over 200 pages into this 700 page book; it is very interesting. The idea: Pinker is arguing that humans, over time, are becoming less violent. One interesting fact: right now, a random human is less likely to die violently than ever before. Yes, the last century saw astonishing genocides and two world wars. But: when one takes into account how many people there are in the world (2.5 billion in 1950, 6 billion right now) World War II, as horrific as it was, only ranks 9’th on the list of deaths due to deliberate human acts (genocides, wars, etc.) in terms of “percentage of the existing population killed in the event”. (here is Matthew White’s site)

But I have a ways to go in the book…but it is one I am eager to keep reading.

The purpose of this post is to talk about a bit of probability theory that occurs in the early part of the book. I’ll introduce it this way:

Suppose I select a 28 day period. On each day, say starting with Monday of the first week, I roll a fair die one time. I note when a “1” is rolled. Suppose my first “1” occurs Wednesday of the first week. Then answer this: “what is the most likely day that I obtain my NEXT “1”, or all days equally likely?”

Yes, it is true that on any given day, the probability of rolling a “1” is 1/6. But remember my question: “what day is most likely for the NEXT one?” If you have had some probability, the distribution you want to use is the geometric distribution, starting on Thursday of the next week.

So you can see, the mostly likely day for the next “1” is Thursday! Well, why not, say, Friday? Well, if Friday is the next 1, then this means that you got “any number but 1” on Thursday followed by a “1” on Friday, and the probability of that is \frac{5}{6} \frac{1}{6} = \frac{5}{36} . The probability of the next one being Saturday is \frac{25}{196} and so on.

The point: if one is studying the distribution of events that have a Poisson distribution (probability p ) on a given time period, the overall distribution of such events is likely to show up “clumped” rather than evenly spaced. For an example of this happening in sports, check this out.

Anyway, Pinker applies this principle to the outbreak of wars, mass killings and the like.

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