Bertrand’s Ballot Problem

Early leads don’t always lead to much. Remember Republican presidential candidate Rudy Giuliani? Back in 2008, the outspoken New Yorker polled so well that some pundits predicted he’d win the nomination, but his lead fizzled out before the convention. The same thing has happened with many other would-be presidents from both sides of the aisle over the past couple of decades. So you might be inclined to discount a candidate’s early lead in polls. In fact, this reasonable inclination was part of why FiveThirtyEight analyst Harry Enten didn’t see Donald Trump’s nomination coming; Enten figured that Trump would wind up being just another Giuliani. Oops.

So, how closely do we expect early polling in an election process to correspond with the final outcome? A classic problem in probability theory strips this real-world question down to manageable size.

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“The Man Who Knew Infinity”: what the film will teach you (and what it won’t)

During my years as a mathematician, not one film-maker has tried to teach me how to write better articles. So I’m not going to tell Matt Brown, the writer/director of “The Man Who Knew Infinity”, what he should have done differently in a movie that, as the fine print on the poster reminds us, is merely based on the life of Ramanujan. If I knew as much about movie-making as Matt Brown does, I probably would have made the same choices he did.

But I am going to tell you, fellow-members of the movie-going public, what characteristics of the math life are conveyed by the film, and what characteristics aren’t. I’m not saying that the film in and of itself is inaccurate, but it does recycle some tropes about mathematics that you’ve probably seen in other movies about mathematicians and that give an inaccurate picture of mathematics. Along the way, you’ll meet the surprising base-ten expansion of the infinite product .9 × .99 × .999 × .9999 × … and learn what it has to do with Ramanujan’s story. (I’m going to assume that you’ve read my blog essay Sri Ramanujan and the Secrets of Lakshmi from last month, or that you already know something about the life and work of Ramanujan.)
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Sri Ramanujan and the Secrets of Lakshmi

Srinivasa Ramanujan

Srinivasa Ramanujan

What can you say about a thirty-two-year-old mathematician who died?  That he loved numbers and equations.  That he had a mysteriously intimate understanding of infinite numerical processes (infinite sums, infinite products, infinite continued fractions, and the like). That to the mathematicians of England, his ideas seemed to spring from nowhere — while he himself said that his ideas came from a goddess.

The collaboration that took place in the years 1914–1919 between the Indian mathematician Srinivasa Ramanujan and the English mathematician G. H. Hardy — perhaps the most famous collaboration in the history of mathematics — is the subject of the 1991 book The Man Who Knew Infinity by Robert Kanigel.  It’s also the subject of a new film by the same name, which I’ll say more about next month.  Today I want to show you three “flowers from Ramanujan’s garden” (to steal a metaphor from Freeman Dyson), with some puzzles strewn along the way, and to explain why Kanigel’s choice of title makes sense. I’ll also speculate a bit about where mathematical ideas come from, and here the case of Ramanujan can be instructive, not because he was typical but because he was such an outlier; it’s hard to think of a parallel example of someone who came up with so many beautiful ideas but had so much trouble leading others to the wellsprings of his inspiration.  (His early death was certainly the greatest and most final source of this trouble, but it wasn’t the only one.) Continue reading

Fermat’s Last Theorem: The Curious Incident of the Boasting Frenchman

The story as told by math popularizer Simon Singh in a Numberphile video goes as follows: There’s this 17th century French mathematician, Pierre de Fermat, sitting in his private library reading a book.  He excitedly records in the margin a new discovery he’s made — the assertion we now call Fermat’s Last Theorem, or FLT for short — but he writes that the margin is too small to contain his proof, and then, before he can communicate the details to anyone, “he drops dead.”

Pierre de Fermat.

Pierre de Fermat.

I have two problems with this version of the FLT story, and the way it shows a character’s death preventing the revelation of a vital secret.  First, we’ve all “seen that movie” many, many times (see the TV Tropes entry on the His Name Is … trope, as well as the related tropes Conveniently Interrupted Document  and Lost in Transmission); it’s kind of hokey, isn’t it?  Second, there’s no evidence that Fermat wrote the passage right before he died.  We can’t go back and date the ink he used (or breathalyze the page for alcohol content, for that matter), as the book was lost after his annotations were transcribed, but we know from Fermat’s correspondence that he read the book fairly early in his career, in the 1630s. Most scholars agree that this particular remark was written two decades before he died. So Singh is putting an over-dramatic spin on things at the 02:15 mark. Still, he is right about Fermat dying without revealing a proof of his claim to anyone.

Andrew Wiles.

Andrew Wiles.

Thanks to modern-day mathematician Andrew Wiles, who this month receives the prestigious Abel Prize for his work on Fermat’s Last Theorem, we know that Fermat’s claim was correct.  But did Fermat have a proof?  That’s the question I want to explore today. And while we’re discussing the most famous proof Fermat never revealed, I’ll tell you about the proof method that Fermat did reveal — one that beautifully solves other problems of the same kind.

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The Paintball Party Problem and the Habit of Symmetry

As the show-runner of my nine-year-old son’s birthday party, I expected to face lots of problems. I just didn’t expect any of them to be math problems.

There were eight boys at that party, including my son and his close buddy, and while those two would’ve been happy to be on the same team in every four-on-four game, my wife wisely suggested that I set things up so that each boy would be on my son’s team the same number of times. In fact, it would be ideal if, over the course of the party, each boy could be each other boy’s team-mate the same number of times. Then no one would have cause to call “No fair!” the way nine-year-olds often do. Continue reading

Believe It, Then Don’t: Toward a Pedagogy of Discomfort

I don’t know which is stranger: the way mathematicians often embrace ideas that at first glance (and later glances!) seem nonsensical, or the way mathematicians often hold obvious truths at arm’s length, scrutinizing them with a skeptical eye and asking “How do we really know it’s true?”


Figure 1. Four disks of diameter 1 packed in a 2-by-2 square.

Here’s one such obvious truth. While it’s possible to pack four disks of diameter 1 into a 2-by-2 square as shown in Figure 1, it’s evident that there’s no way to fit five disks of diameter 1 into a 2-by-2 square. It seems crazy to ask for a rigorous proof of this; what could be intuitively clearer? And while we’re at it, it seems obvious that you can’t fit more than six disks of diameter 1 into a 2-by-3 rectangle, or more than eight disks of diameter 1 into a 2-by-4 rectangle, and so on (all our disks today will be of diameter 1, so soon I’ll stop saying “of diameter 1”). Aiming for more generality, let’s define Pn to be the proposition that you can’t pack more than 2n disks of diameter 1 into a 2-by-n rectangle. It’s reasonable to believe that Pn is true for all positive integers n, isn’t it? Continue reading

When Not to Expect What You’re Expecting

My wife and I played the Massachusetts PowerBall lottery last month. In one respect it was a good thing that we didn’t win: if we had, it would’ve made my job as a math popularizer that much harder. When a lottery-winner says that playing the lottery is a bad investment strategy, it comes across as hypocritical at best.

One way experts in statistics and probability try to explain why buying a lottery ticket every week is a bad retirement plan is by invoking the concept of expected value. To illustrate the idea, imagine for simplicity a scaled-down version of a lottery — a roulette game with a wheel that has 38 pockets. Suppose that you paid $1 to bet that the ball will wind up in a particular pocket, and that the payoff if you guessed right will be $36. Then in 37 possible worlds you win $0 while in 1 possible world you win $36. Your average payoff over those 38 equally likely parallel worlds is (37x$0+1x$36)/38, or about 95 cents, which is less than what you paid to play. If you like the ambiance of casinos (music, drinks, company) but hate the element of uncertainty, or if the idea of other yous in other worlds having different destinies freaks you out, you can fold all thirty-eight worlds into one by placing a bet on each pocket. Then you’ll pay $38 and are sure to win $36, for a net loss of $2. (Seems like a bad idea, but maybe the croupier is cute and you’re hoping your unorthodox betting strategy will make a good conversation-starter.) State-run lotteries are a lot like this roulette game: if you were to bet on every “pocket” (ignoring the fact that there might not be enough money in the economy to enable you to do that), you’d lose big time. And it stands to reason that if buying all possible tickets is a bad idea, so is buying just one per week, or just one this time.

But getting back to the casino example: wouldn’t it be nice if the casino paid $40 rather than $36 for a winning bet? Then the strategy of betting on all 38 pockets would give you a profit of $2 instead of a loss of $2. A $2 profit doesn’t sound like much, but the strategy scales up: bet $1000 on every pocket and you’re guaranteed to make a $2000 profit. (Don’t have $38,000 lying around? Find some rich friends to front you the money and promise to split the $2000 profit with them.) But how likely is it that a casino would offer such a good deal?

Amazingly, there is a casino that operates this way. Continue reading