## My All 2020 Mathematics A to Z: John von Neumann

Mr Wu, author of the Singapore Maths Tuition blog, suggested another biographical sketch for this year of biographies. Once again it’s of a person too complicated to capture in full in one piece, even at the length I’ve been writing. So I take a slice out of John von Neumann’s life here.

# John von Neumann.

In March 1919 the Hungarian People’s Republic, strained by Austria-Hungary’s loss in the Great War, collapsed. The Hungarian Soviet Republic, the world’s second Communist state, replaced it. It was a bad time to be a wealthy family in Budapest. The Hungarian Soviet lasted only a few months. It was crushed by the internal tension between city and countryside. By poorly-fought wars to restore the country’s pre-1914 borders. By the hostility of the Allied Powers. After the Communist leadership fled came a new Republic, and a pogrom. Europeans are never shy about finding reasons to persecute Jewish people. It was a bad time to be a Jewish family in Budapest.

Von Neumann was born to a wealthy, (non-observant) Jewish family in Budapest, in 1903. He acquired the honorific “von” in 1913. His father Max Neumann was honored for service to the Austro-Hungarian Empire and paid for a hereditary appellation.

It is, once again, difficult to encompass von Neumann’s work, and genius, in one piece. He was recognized as genius early. By 1923 he published a logical construction for the counting numbers that’s still the modern default. His 1926 doctoral thesis was in set theory. He was invited to lecture on quantum theory at Princeton by 1929. He was one of the initial six mathematics professors at the Institute for Advanced Study. We have a thing called von Neumann algebras after his work. He gave the first rigorous proof of an ergodic theorem. He partly solved one of Hilbert’s problems. He studied non-linear partial differential equations. He was one of the inventors of the electronic computer as we know it, both the theoretical and the practical ideas.

And, the sliver I choose to focus on today, he made game theory into a coherent field.

The term “game theory” makes it sound like a trifle. We don’t call “genius” anyone who comes up with a better way to play tic-tac-toe. The utility of the subject appears when we notice what von Neumann thought he was writing about. Von Neumann’s first paper on this came in 1928. In 1944 he with Oskar Morgenstern published the textbook Theory Of Games And Economic Behavior. In Chapter 1, Section 1, they set their goals:

The purpose of this book is to present a discussion of some fundamental questions of economic theory which require a treatment different from that which they have found thus far in the literature. The analysis is concerned with some basic problems arising from a study of economic behavior which have been the center of attention of economists for a long time. They have their origin in the attempts to find an exact description of the endeavor of the individual to obtain a maximum of utility, or in the case of the entrepreneur, a maximum of profit.

Somewhere along the line von Neumann became interested in how economics worked. Perhaps because his family had money. Perhaps because he saw how one could model an “ideal” growing economy — matching price and production and demand — as a linear programming question. Perhaps because economics is a big, complicated field with many unanswered questions. There was, for example, little good idea of how attendees at an auction should behave. What is the rational way to bid, to get the best chances of getting the things one wants at the cheapest price?

In 1928, von Neumann abstracted all sorts of economic questions into a basic model. The model has almost no features, so very many games look like it. In this, you have a goal, and a set of options for what to do, and an opponent, who also has options of what to do. Also some rounds to achieve your goal. You see how this abstract a structure describes many things one could do, from playing Risk to playing the stock market.

And von Neumann discovered that, in the right circumstances, you can find a rational way to bid at an auction. Or, at least, to get your best possible outcome whatever the other person does. The proof has the in-retrospect obviousness of brilliance. von Neumann used a fixed-point theorem. Fixed point theorems came to mathematics from thinking of functions as mappings. Functions match elements in a set called the domain to those in a set called the range. The function maps the domain into the range. If the range is also the domain? Then we can do an iterated mapping. Under the right circumstances, there’s at least one point that maps to itself.

In the light of game theory, a function is the taking of a turn. The domain and the range are the states of whatever’s in play. In this type of game, you know all the options everyone has. You know the state of the game. You know what the past moves have all been. You know what you and your opponent hope to achieve. So you can predict your opponent’s strategy. And therefore pick a strategy that gets you the best option available given your opponent is trying to do the same. So will your opponent. So you both end up with the best attainable outcome for the both of you; this is the minimax theorem.

It may strike you that, given this, the game doesn’t need to be played anymore. Just pick your strategy, let your opponent pick one, and the winner is determined. So it would, if we played our strategies perfectly, and if we didn’t change strategies mid-game. I would chuckle at the mathematical view that we study a game to relieve ourselves of the burden of playing. But I know how many grand strategy video games I have that I never have time to play.

After this 1928 paper von Neumann went on to other topics for about a dozen years. Why create a field of mathematics and then do nothing with it? For one, we see it as a gap only because we are extracting, after the fact, this thread of his life. He had other work, particularly in quantum mechanics, operators, measure theory, and lattice theory. He surely did not see himself abandoning a new field. He saw, having found an interesting result, new interesting questions..

But Philip Mirowski’s 1992 paper What Were von Neumann and Morgenstern Trying to Accomplish? points out some context. In September 1930 Kurt Gödel announced his incompleteness proof. Any logical system complex enough has things which are true and can’t be proven. The system doesn’t have to be that complex. Mathematical rigor must depend on something outside mathematics. This shook von Neumann. He would say that after Gödel published, von Neumann never bothered reading another paper on symbolic logic. Mirowski believes this drove von Neumann into what we now call artificial intelligence. At least, into mathematics that draws from empirical phenomena. von Neumann needed time to recover from the shock. And needed the prodding of Morgenstern to return to economics.

After publishing Theory Of Games And Economic Behavior the book … well, Mirowski calls it more “cited in reverence than actually read”. But game theory, as a concept? That took off. It seemed to offer a way to rationalize the world.

von Neumann would become a powerful public intellectual. He would join the Manhattan Project. He showed that the atomic bomb would be more destructive if it exploded kilometers above the ground, rather than at ground level. He was on the target selection committee which, ultimately, slated Hiroshima and Nagasaki for mass murder. He would become a consultant for the Weapons System Evaluation Group. They advised the United States Joint Chiefs of Staff on developing and using new war technology. He described himself, to a Senate committee, as “violently anti-communist and much more militaristic than the norm”. He is quoted in 1950 as remarking, “if you say why not bomb [ the Soviets ] tomorrow, I say, why not today? If you say today at five o’clock, I say why not one o’clock?”

The quote sounds horrifying. It makes game-theory sense, though. If war is inevitable, it is better fought when your opponent is weaker. And while the Soviet Union had won World War II, it was also ruined in the effort.

There is another game-theory-inspired horror for which we credit von Neumann. This is Mutual Assured Destruction. If any use of an atomic, or nuclear, weapon would destroy the instigator in retaliation, then no one would instigate war. So the nuclear powers need, not just nuclear arsenals. They need such vast arsenals that the remnant which survives the first strike can destroy the other powers in the second strike.

Perhaps the reasoning holds together. We did reach the destruction of the Soviet Union without using another atomic weapon in anger. But it is hard to say that was rationally accomplished. There were at least two points, in 1962 and in 1983, when a world-ruining war could too easily have happened, by people following the “obvious” strategy.

Which brings a flaw of game theory, at least as applied to something as complicated as grand strategy. Game theory demands the rules be known, and agreed on. (At least that there is a way of settling rule disputes.) It demands we have the relevant information known truthfully. It demands we know what our actual goals are. It demands that we act rationally, and that our opponent acts rationally. It demands that we agree on what rational is. (Think of, in Doctor Strangelove, the Soviet choice to delay announcing its doomsday machine’s completion.) Few of these conditions obtain in grand strategy. They barely obtain in grand strategy games. von Neumann was aware of at least some of these limitations, though he did not live long enough to address them. He died of either bone, pancreatic, or prostate cancer, likely caused by radiation exposure working at Los Alamos.

Game theory has been, and is, a great tool in many fields. It gives us insight into human interactions. It does good work in economics, in biology, in computer science, in management. But we can come to very bad conditions when we forget the difference between the game we play and the game we modelled. And if we forget that the game is value-indifferent. The theory makes no judgements about the ethical nature of the goal. It can’t, any more than the quadratic equation can tell us whether ‘x’ is which fielder will catch the fly ball or which person will be killed by a cannonball.

It makes an interesting parallel to the 19th century’s greatest fusion of mathematics and economics. This was utilitarianism, one famous attempt to bring scientific inquiry to the study of how society should be set up. Utilitarianism offers exciting insights into, say, how to allocate public services. But it struggles to explain why we should refrain from murdering someone whose death would be convenient. We need a reason besides the maximizing of utility.

No war is inevitable. One comes about only after many choices. Some are grand choices, such as a head of government issuing an ultimatum. Some are petty choices, such as the many people who enlist as the sergeants that make an army exist. We like to think we choose rationally. Psychological experiments, and experience, and introspection tell us we more often choose and then rationalize.

von Neumann was a young man, not yet in college, during the short life of the Hungarian Soviet Republic, and the White Terror that followed. I do not know his biography well enough to say how that experience motivated his life’s reasoning. I would not want to say that 1919 explained it all. The logic of a life is messier than that. I bring it up in part to fight the tendency of online biographic sketches to write as though he popped into existence, calculated a while, inspired a few jokes, and vanished. And to reiterate that even mathematics never exists without context. Even what seem to be pure questions on an abstract idea of a game is often inspired by a practical question. And that work is always done in a context that affects how we evaluate it.

Thank you all for reading. This grew a bit more serious than I had anticipated. This and all the other 2020 A-to-Z essays should appear at this link. Both the 2020 and all past A-to-Z essays should be at this link.

I am hosting the Playful Math Education Blog Carnival at the end of September, so appreciate any educational or recreational or fun mathematics material you know about. I’m hoping to publish next week and so hope that you can help me this week.

And, finally, I am open for mathematics topics starting with P, Q, and R to write about next month. I should be writing about them this month and getting ahead of deadline, but that seems not to be happening.

## Who’s most likely to win The Price Is Right Showcase Showdown?

A friend pointed out a paper written almost just for me. It’s about the game show The Price Is Right. Rafael Tenorio and Timothy N Cason’s To Spin Or Not To Spin? Natural and Laboratory Experiments from The Price Is Right, linked to from here, explores one of the show’s distinctive pieces, the Showcase Showdown. This is the part, done twice each show, where three contestants spin the Big Wheel. They get one or two spins to get a total of as close to a dollar as they can without going over.

One natural question is: does the order matter? Are you better off going first, second, or third? Contestants don’t get to choose order; they’re ranked by how much they’ve won on the show already. (I believe this includes the value of their One-Bids, the item-up-for-bid that gets them on stage. This lets them rank contestants when all three lost their pricing games.) The first contestant always has a choice of whether to spin once or twice. The second and third contestants don’t necessarily get to choose what to do. Is that an advantage or a disadvantage?

In this paper, published 2002, Tenorio and Cason look at the game-theoretical logic. And compare it to how people actually play the game, on the show and in laboratory experiments. (The advantage of laboratory experiments, besides that you can get more than two each day, is that participants’ behavior won’t be thrown off by the thoughts of winning a thousand or more dollars for a good spin.) They also look some at how the psychology of risk affects people’s play.

(I’m compelled — literally, I can’t help myself — to note they make some terminology errors. They mis-label the Showcase Showdown as the bit at the end of the show, where two contestants put up bids for showcases. It’s a common mistake, and probably reflects that “showdown” has connotations of being one-on-one. But that segment is simply the Showcase Round. The Showcase Showdown is the spinning-the-big-wheel part.)

Their research, anyway, suggests that if every contestant played perfectly — achieving a “Nash equilibrium”, in which nobody can pick a better strategy given the choices other players make — going later does, indeed, give a slight advantage. The first contestant would win about 31% of the time, the second about 33%, and the third about 36% of the time. In watching the show to see what happens they found the first contestant won about 30% of the time, the second about 34%, and the third about 36% of the time. That’s no big difference.

The article includes more fascinating statistical breakdowns, answering questions such as “are spins on the wheel uniformly distributed?” That is, are you as likely to spin \$1.00 on the first spin as you are to spin 0.05? Or 0.50? They have records of what people actually do. Or what prize payouts would be expected, from theoretical perfect play, and how they compare to actual play.

The paper is written for an academic audience, particularly one versed in game theory. If you are somehow not, it can be tough going. It’s all right to let your eye zip past a paragraph of jargon, or of calculations, to get back to the parts that read as English. Real mathematicians do that too, as a way of understanding the point. They can come back around later to learn how the authors got to the point.

## My 2019 Mathematics A To Z: The Game of ‘Y’

Today’s A To Z term is … well, my second choice. Goldenoj suggested Yang-Mills and I was so interested. Yang-Mills describes a class of mathematical structures. They particularly offer insight into how to do quantum mechanics. Especially particle physics. It’s of great importance. But, on thinking out what I would have to explain I realized I couldn’t write a coherent essay about it. Getting to what the theory is made of would take explaining a bunch of complicated mathematical structures. If I’d scheduled the A-to-Z differently, setting up matters like Lie algebras, maybe I could do it, but this time around? No such help. And I don’t feel comfortable enough in my knowledge of Yang-Mills to describe it without describing its technical points.

That said I hope that Jacob Siehler, who suggested the Game of ‘Y’, does not feel slighted. I hadn’t known anything of the game going in to the essay-writing. When I started research I was delighted. I have yet to actually play a for-real game of this. But I like what I see, and what I can think I can write about it.

# Game of ‘Y’.

This is, as the name implies, a game. It has two players. They have the same objective: to create a ‘y’. Here, they do it by laying down tokens representing their side. They take turns, each laying down one token in a turn. They do this on a shape with three edges. The ‘y’ is created when there’s a continuous path of their tokens that reaches all three edges. Yes, it counts to have just a single line running along one edge of the board. This makes a pretty sorry ‘y’ but it suggests your opponent isn’t trying.

There are details of implementation. The board is a mesh of, mostly, hexagons. I take this to be for the same reason that so many conquest-type strategy games use hexagons. They tile space well, they give every space a good number of neighbors, and the distance from the centers of one neighbor to another is constant. In a square grid, the centers of diagonal neighbors are farther than the centers of left-right or up-down neighbors. Hexagons do well for this kind of game, where the goal is to fill space, or at least fill paths in space. There’s even a game named Hex, slightly older than Y, with similar rules. In that the goal is to draw a continuous path from one end of the rectangular grid to another. The grid of commercial boards, that I see, are around nine hexagons on a side. This probably reflects a desire to have a big enough board that games go on a while, but not so big that they go on forever

Mathematicians have things to say about this game. It fits nicely in game theory. It’s well-designed to show some things about game theory. It’s the kind of game which has perfect information game, for example. Each player knows, at all times, the moves all the players have made. Just look at the board and see where they’ve placed their tokens. A player might have forgotten the order the tokens were placed in, but that’s the player’s problem, not the game’s. Anyway in Y, the order of token-placing doesn’t much matter.

It’s also a game of complete information. Every player knows, at every step, what the other player could do. And what objective they’re working towards. One party, thinking enough, could forecast the other’s entire game. This comes close to the joke about the prisoners telling each other jokes by shouting numbers out to one another.

It is also a game in which a draw is impossible. Play long enough and someone must win. This even if both parties are for some reason trying to lose. There are ingenious proofs of this, but we can show it by considering a really simple game. Imagine playing Y on a tiny board, one that’s just one hex on each side. Definitely want to be the first player there.

So now imagine playing a slightly bigger board. Augment this one-by-one-by-one board by one row. That is, here, add two hexes along one of the sides of the original board. So there’s two pieces here; one is the original territory, and one is this one-row augmented territory. Look first at the original territory. Suppose that one of the players has gotten a ‘Y’ for the original territory. Will that player win the full-size board? … Well, sure. The other player can put a token down on either hex in the augmented territory. But there’s two hexes, either of which would make a path that connects the three edges of the board. The first player can put a token down on the other hex in the augmented territory, and now connects all three of the new sides again. First player wins.

All right, but how about a slightly bigger board? So take that two-by-two-by-two board and augment it, adding three hexes along one of the sides. Imagine a player’s won the original territory board. Do they have to win the full-size board? … Sure. The second player can put something in the augmented territory. But there’s again two hexes that would make the path connecting all three sides of the full board. The second player can put a token in one of those hexes. But the first player can put a token in the other of those. First player wins again.

How about a slightly bigger board yet? … Same logic holds. Really the only reason that the first player doesn’t always win is that, at some point, the first player screws up. And this is an existence proof, showing that the first player can always win. It doesn’t give any guidance into how to play, though. If the first player plays perfectly, she’s compelled to win. This is something which happens in many two-player, symmetric games. A symmetric game is one where either player has the same set of available moves, and can make the same moves with the same results. This proof needs to be tightened up to really hold. But it should convince you, at least, that the first player has an advantage.

So given that, the question becomes why play this game after you’ve decided who’ll go first? The reason you might if you were playing a game is, what, you have something else to do? And maybe you think you’ll make fewer mistakes than your opponent. One approach often used in symmetric games like this is the “pie rule”. The name comes from the story about how to slice a pie so you and your sibling don’t fight over the results. One cuts the pie, the other gets first pick of the slice, and then you fight anyway. In this game, though, one player makes a tentative first move. The other decides whether they will be Player One with that first move made or whether they’ll be Player Two, responding.

There are some neat quirks in the commercial Y games. One is that they don’t actually show hexes, and you don’t put tokens in the middle of hexes. Instead you put tokens on the spots that would be the center of the hex. On the board are lines pointing to the neighbors. This makes the board actually a string of triangles. This is the dual to the hex grid. It shows a set of vertices, and their connections, instead of hexes and their neighbors. Whether you think the hex grid or this dual makes it easier to tell when you’ve connected all three edges is a matter of taste. It does make the edges less jagged all around.

Another is that there will be three vertices that don’t connect to six others. They connect to five others, instead. Their spaces would be pentagons. As I understand the literature on this, this is a concession to game balance. It makes it easier for one side to fend off a path coming from the center.

It has geometric significance, though. A pure hexagonal grid is a structure that tiles the plane. A mostly hexagonal grid, with a couple of pentagons, though? That can tile the sphere. To cover the whole sphere you need something like at least twelve irregular spots. But this? With the three pentagons? That gives you a space that’s topographically equivalent to a hemisphere, or at least a slice of the sphere. If we do imagine the board to be a hemisphere covered, then the result of the handful of pentagon spaces is to make the “pole” closer to the equator.

So as I say the game seems fun enough to play. And it shows off some of the ways that game theorists classify games. And the questions they ask about games. Is the game always won by someone? Does one party have an advantage? Can one party always force a win? It also shows the kinds of approach game theorists can use to answer these questions. This before they consider whether they’d enjoy playing it.

I am excited to say that there’s just the one more time this year that I will realize: it’s Wednesday evening and I’m 1200 words short. Please stop in Thursday when I hope to have the letter Z represented. That, and all of this year’s A-to-Z essays, should appear at this link. And if that isn’t enough, I’ll feature some past essays on Friday and Saturday, and have most of my past A-to-Z essays at this link. Thank you.

## Reading the Comics, July 2, 2019: Back On Schedule Edition

I hoped I’d get a Reading the Comics post in for Tuesday, and even managed it. With this I’m all caught up to the syndicated comic strips which, last week, brought up some mathematics topic. I’m open for nominations about what to publish here Thursday. Write in quick.

Hilary Price’s Rhymes With Orange for the 30th is a struggling-student joke. And set in summer school, so the comic can be run the last day of June without standing out to its United States audience. It expresses a common anxiety, about that point when mathematics starts using letters. It superficially seems strange that this change worries students. Students surely had encountered problems where some term in an equation was replaced with a blank space and they were expected to find the missing term. This is the same work as using a letter. Still, there are important differences. First is that a blank line (box, circle, whatever) has connotations of “a thing to be filled in”. A letter seems to carry meaning in to the problem, even if it’s just “x marks the spot”. And a letter, as we use it in English, always stands for the same thing (or at least the same set of things). That ‘x’ may be 7 in one problem and 12 in another seems weird. I mean weird even by the standards of English orthography.

A letter might represent a number whose value we wish to know; it might represent a number whose value we don’t care about. These are different ideas. We usually fall into a convention where numbers we wish to know are more likely x, y, and z, while those we don’t care about are more likely a, b, and c. But even that’s no reliable rule. And there may be several letters in a single equation. It’s one thing to have a single unknown number to deal with. To have two? Three? I don’t blame people fearing they can’t handle that.

Mark Leiknes’s Cow and Boy for the 30th has Billy and Cow pondering the Prisoner’s Dilemma. This is one of the first examples someone encounters in game theory. Game theory sounds like the most fun part of mathematics. It’s the study of situations in which there’s multiple parties following formal rules which allow for gains or losses. This is an abstract description. It means many things fit a mathematician’s idea of a game.

The Prisoner’s Dilemma is described well enough by Billy. It’s built on two parties, each — separately and without the ability to coordinate — having to make a choice. Both would be better off, under interrogation, to keep quiet and trust that the cops can’t get anything significant on them. But both have the temptation that if they rat out the other, they’ll get off free while their former partner gets screwed. And knowing that their partner has the same temptation. So what would be best for the two of them requires them both doing the thing that maximizes their individual risk. The implication is unsettling: everyone acting in their own best interest is supposed to produce the best possible result for society. And here, for the society of these two accused, it breaks down entirely.

Jason Poland’s Robbie and Bobby for the 1st is a rerun. I discussed it last time it appeared, in November 2016, which was before I would routinely include the strips under discussion. The strip’s built on wordplay, using the word ‘power’ in its connotations for might and for exponents.

Exponents have been written as numbers in superscript following a base for a long while now. The notation developed over the 17th century. I don’t know why mathematicians settled on superscripts, as opposed to the many other ways a base and an exponent might fit together. It’s a good mnemonic to remember, say, “z raised to the 10th” is z with a raised 10. But I don’t know the etymology of “raised” in a mathematical context well enough. It’s plausible that we say “raised” because that’s what the notation suggests.

Zach Weinersmith’s Saturday Morning Breakfast Cereal for the 2nd argues for the beauty of mathematics as a use for it. It’s presented in a brutal manner, but saying brutal things to kids is a comic motif with history to it. Well, in an existentialist manner, but that gets pretty brutal quickly.

The proof of the Pythagorean Theorem is one of the very many known to humanity. This one is among the family of proofs that are wordless. At least nearly wordless. You can get from here to $a^2 + b^2 = c^2$ with very little prompting. If you do need prompting, it’s this: there are two expressions for how much area of the square with sides a-plus-b. One of these expressions uses only terms of a and b. The other expression uses terms of a, b, and c. If this doesn’t get a bit of a grin out of you, don’t worry. There’s, like, 2,037 other proofs we already know about. We might ask whether we need quite so many proofs of the Pythagorean theorem. It doesn’t seem to be under serious question most of the time.

And then a couple comic strips last week just mentioned mathematics. Morrie Turner’s Wee Pals for the 1st of July has the kids trying to understand their mathematics homework. Could have been anything. Mike Thompson’s Grand Avenue for the 5th started a sequence with the kids at Math Camp. The comic is trying quite hard to get me riled up. So far it’s been the kids agreeing that mathematics is the worst, and has left things at that. Hrmph.

Whether or not I have something for Thursday, by Sunday I should have anotherReading the Comics post. It, as well as my back catalogue of these essays, should be at this link. Thanks for worrying about me.

## Yes, I Am Late With The Comics Posts Today

I apologize that, even though the past week was light on mathematically-themed comic strips, I didn’t have them written up by my usual Sunday posting time. It was just too busy a week, and I am still decompressing from the A to Z sequence. I’ll have them as soon as I’m able.

In the meanwhile may I share a couple of things I thought worth reading, and that have been waiting in my notes folder for the chance to highlight?

This Fermat’s Library tweet is one of those entertaining consequences of probability, multiplied by the large number of people in the world. If you flip twenty coins in a row there’s a one in 1,048,576 chance that all twenty will come up heads, or all twenty will come up tails. So about one in every million times you flip twenty coins, they all come up the same way. If the seven billion people in the world have flipped at least twenty coins in their lives, then something like seven thousand of them had the coins turn up heads every single one of those twenty times. That all seven billion people have tossed a coin seems like the biggest point to attack this trivia on. A lot of people are too young, or don’t have access to, coins. But there’s still going to be thousands who did start their coin-flipping lives with a remarkable streak.

Also back in October, so you see how long things have been circulating around here, John D Cook published an article about the World Series. Or any series contest. At least ones where the chance of each side winning don’t depend on the previous games in the series. If one side has a probability ‘p’ of winning any particular game, what’s the chance they’ll win a best-four-of-seven? What makes this a more challenging mathematics problem is that a best-of-seven series stops after one side’s won four games. So you can’t simply say it’s the chance of four wins. You need to account for four wins out of five games, out of six games, and out of seven games. Fortunately there’s a lot of old mathematics that explores just this.

The economist Brandford DeLong noticed the first write-up of the Prisoners Dilemma. This is one of the first bits of game theory that anyone learns, and it’s an important bit. It establishes that the logic of cooperatives games — any project where people have to work together — can have a terrible outcome. What makes the most sense for the individuals makes the least sense for the group. That a good outcome for everyone depends on trust, whether established through history or through constraints everyone’s agreed to respect.

And finally here’s part of a series about quick little divisibility tests. This is that trick where you tell what a number’s divisible by through adding or subtracting its (base ten) digits. Everyone who’d be reading this post knows about testing for divisibility by three or nine. Here’s some rules for also testing divisibility by eleven (which you might know), by seven (less likely), and thirteen. With a bit of practice, and awareness of some exceptional numbers, you can tell by sight whether a number smaller than a thousand is prime. Add a bit of flourish to your doing this and you can establish a reputation as a magical mathematician.

## My 2018 Mathematics A To Z: Zugzwang

My final glossary term for this year’s A To Z sequence was suggested by aajohannas, who’d also suggested “randomness” and “tiling”. I don’t know of any blogs or other projects they’re behind, but if I do hear, I’ll pass them on.

# Zugzwang.

Some areas of mathematics struggle against the question, “So what is this useful for?” As though usefulness were a particular merit — or demerit — for a field of human study. Most mathematics fields discover some use, though, even if it takes centuries. Others are born useful. Probability, for example. Statistics. Know what the fields are and you know why they’re valuable.

Game theory is another of these. The subject, as often happens, we can trace back centuries. Usually as the study of some particular game. Occasionally in the study of some political science problem. But game theory developed a particular identity in the early 20th century. Some of this from set theory experts. Some from probability experts. Some from John von Neumann, because it was the 20th century and all that. Calling it “game theory” explains why anyone might like to study it. Who doesn’t like playing games? Who, studying a game, doesn’t want to play it better?

But why it might be interesting is different from why it might be important. Think of what a game is. It is a string of choices made by one or more parties. The point of the choices is to achieve some goal. Put that way you realize: this is everything. All life is making choices, all in the pursuit of some goal, even if that goal is just “not end up any worse off”. I don’t know that the earliest researchers in game theory as a field realized what a powerful subject they had touched on. But by the 1950s they were doing serious work in strategic planning, and by 1964 were even giving us Stanley Kubrick movies.

This is taking me away from my glossary term. The field of games is enormous. If we narrow the field some we can discuss specific kinds of games. And say more involved things about these games. So first we’ll limit things by thinking only of sequential games. These are ones where there are a set number of players, and they take turns making choices. I’m not sure whether the field expects the order of play to be the same every time. My understanding is that much of the focus is on two-player games. What’s important is that at any one step there’s only one party making a choice.

The other thing narrowing the field is to think of information. There are many things that can affect the state of the game. Some of them might be obvious, like where the pieces are on the game board. Or how much money a player has. We’re used to that. But there can be hidden information. A player might conceal some game money so as to make other players underestimate her resources. Many card games have one or more cards concealed from the other players. There can be information unknown to any party. No one can make a useful prediction what the next throw of the game dice will be. Or what the next event card will be.

But there are games where there’s none of this ambiguity. These are called games with “perfect information”. In them all the players know the past moves every player has made. Or at least should know them. Players are allowed to forget what they ought to know.

There’s a separate but similar-sounding idea called “complete information”. In a game with complete information, players know everything that affects the gameplay. At least, probably, apart from what their opponents intend to do. This might sound like an impossibly high standard, at first. All games with shuffled decks of cards and with dice to roll are out. There’s no concealing or lying about the state of affairs.

Set complete-information aside; we don’t need it here. Think only of perfect-information games. What are they? Some ancient games, certainly. Tic-tac-toe, for example. Some more modern versions, like Connect Four and its variations. Some that are actually deep, like checkers and chess and go. Some that are, arguably, more puzzles than games, as in sudoku. Some that hardly seem like games, like several people agreeing how to cut a cake fairly. Some that seem like tests to prove people are fundamentally stupid, like when you auction off a dollar. (The rules are set so players can easily end up paying more then a dollar.) But that’s enough for me, at least. You can see there are games of clear, tangible interest here.

The last restriction: think only of two-player games. Or at least two parties. Any of these two-party sequential games with perfect information are a part of “combinatorial game theory”. It doesn’t usually allow for incomplete-information games. But at least the MathWorld glossary doesn’t demand they be ruled out. So I will defer to this authority. I’m not sure how the name “combinatorial” got attached to this kind of game. My guess is that it seems like you should be able to list all the possible combinations of legal moves. That number may be enormous, as chess and go players are always going on about. But you could imagine a vast book which lists every possible game. If your friend ever challenged you to a game of chess the two of you could simply agree, oh, you’ll play game number 2,038,940,949,172 and then look up to see who won. Quite the time-saver.

Most games don’t have such a book, though. Players have to act on what they understand of the current state, and what they think the other player will do. This is where we get strategies from. Not just what we plan to do, but what we imagine the other party plans to do. When working out a strategy we often expect the other party to play perfectly. That is, to make no mistakes, to not do anything that worsens their position. Or that reduces their chance of winning.

… And yes, arguably, the word “chance” doesn’t belong there. These are games where the rules are known, every past move is known, every future move is in principle computable. And if we suppose everyone is making the best possible move then we can imagine forecasting the whole future of the game. One player has a “chance” of winning in the same way Christmas day of the year 2038 has a “chance” of being on a Tuesday. That is, the probability is just an expression of our ignorance, that we don’t happen to be able to look it up.

But what choice do we have? I’ve never seen a reference that lists all the possible games of tic-tac-toe. And that’s about the simplest combinatorial-game-theory game anyone might actually play. What’s possible is to look at the current state of the game. And evaluate which player seems to be closer to her goal. And then look at all the possible moves.

There are three things a move can do. It can put the party closer to the goal. It can put the party farther from the goal. Or it can do neither. On her turn the other party might do something that moves you farther from your goal, moves you closer to your goal, or doesn’t affect your status at all. It seems like this makes strategy obvious. On every step take the available move that takes one closest to the goal. This is known as a “greedy” strategy. As the name suggests it isn’t automatically bad. If you expect the game to be a short one, greed might be the best approach. The catch is that moves that seem less good — even ones that seem to hurt you initially — might set up other, even better moves. So strategy requires some thinking beyond the current step. Properly, it requires thinking through to the end of the game. Or at least until the end of the game seems obvious.

We should like a strategy that leaves us no choice but to win. Next-best would be one that leaves the game undecided, since something might happen like the other player needing to catch a bus and so resigning. This is how I got my solitary win in the two months I spent in the college chess club. Worst would be the games that leave us no choice but to lose.

It can be that there are no good moves. That is, that every move available makes it a little less likely that we win. Sometimes a game offers the chance to pass, preserving the state of the game but giving the other party the turn. Then maybe the other party will do something that creates a better opportunity for us. But if we are allowed to pass, there’s a good chance the game lets the other party pass, too, and we end up in the same fix. And it may be the rules of the game don’t allow passing anyway. One must move.

The phenomenon of having to make a move when it’s impossible to make a good move has prominence in chess. I don’t have the chess knowledge to say how common the situation is. But it seems to be a situation people who study chess problems love. I suppose it appeals to a love of lost causes and the hope that you can be brilliant enough to see what everyone else has overlooked. German chess literates gave it a name 160 years ago, “zugzwang”, “compulsion to move”. Somehow I never encountered the term when I was briefly a college chess player. Perhaps because I was never in zugzwang and was just too incompetent a player to find my good moves. I first encountered the term in Michael Chabon’s The Yiddish Policeman’s Union. The protagonist picked up on the term as he investigated the murder of a chess player and then felt himself in one.

Combinatorial game theorists have picked up the word, and sharpened its meaning. If I understand correctly chess players allow the term to be used for any case where a player hurts her position by moving at all. Game theorists make it more dire. This may reflect their knowledge that an optimal strategy might require taking some dismal steps along the way. The game theorist formally grants the term only to the situation where the compulsion to move changes what should be a win into a loss. This seems terrible, but then, we’ve all done this in play. We all feel terrible about it.

I’d like here to give examples. But in searching the web I can find only either courses in game theory. These are a bit too much for even me to sumarize. Or chess problems, which I’m not up to understanding. It seems hard to set out an example: I need to not just set out the game, but show that what had been a win is now, by any available move, turned into a loss. Chess is looser. It even allows, I discover, a double zugzwang, where both players are at a disadvantage if they have to move.

It’s a quite relatable problem. You see why game theory has this reputation as mathematics that touches all life.

And with that … I am done! All of the Fall 2018 Mathematics A To Z posts should be at this link. Next week I’ll post my big list of all the letters, though. And, as has become tradition, a post about what I learned by doing this project. And sometime before then I should have at least one more Reading the Comics post. Thanks kindly for reading and we’ll see when in 2019 I feel up to doing another of these.

## Reading the Comics, April 2, 2016: Keeping Me Busy Edition

After I made a little busy work for myself posting a Reading the Comics entry the other day, Comic Strip Master Command sent a rush of mathematics themes into the comics. So it goes.

Chris Browne’s Hagar the Horrible for the 31st of March happens to be funny-because-it’s-true. It’s supposed to be transgressive to see a gambler as the best mathematician available. But quite a few of the great pioneering minds of mathematics were also gamblers looking for an edge. It may shock you to learn that mathematicians in past centuries didn’t have enough money, and would look for ways to get more. And, as ever, knowing something secret about the way cards or dice or any unpredictable event might happen gives one an edge. The question of whether a 9 or a 10 is more likely to be thrown on three dice was debated for centuries, by people as familiar to us as Galileo. And by people as familiar to mathematicians as Gerolamo Cardano.

Gambling blends imperceptibly into everything people want to do. The question of how to fairly divide the pot of an interrupted game may seem sordid. But recast it as the problem of how to divide the assets of a partnership which had to halt — say, because one of the partners had to stop participating — and we have something that looks respectable. And gambling blends imperceptibly into security. The result of any one project may be unpredictable. The result of many similar ones, on average, often is. Card games or joint-stock insurance companies; the mathematics is the same. A good card-counter might be the best mathematician available.

Tony Cochran’s Agnes for the 31st name-drops Diophantine equations. It’s in the service of a student resisting class joke. Diophantine equations are equations for which we only allow integer, whole-number, answers. The name refers to Diophantus of Alexandria, who lived in the third century AD. His Arithmetica describes many methods for solving equations, a prototype to algebra as we know it in high school today. Generally, a Diophantine equation is a hard problem. It’s impossible, for example, to say whether an arbitrary Diophantine equation even has a solution. Finding what it might be is another bit of work. Fermat’s Last Theorem is a Diophantine equation, and that took centuries to work out that there isn’t generally an answer.

Mind, we can say for specific cases whether a Diophantine equation has a solution. And those specific cases can be pretty general. If we know integers a and b, then we can find integers x and y that make “ax + by = 1” true, for example.

Graham Harrop’s Ten Cats for the 31st hurts mathematicians’ feelings on the way to trying to help a shy cat. I’m amused anyway.

And Jonathan Lemon’s Rabbits Against Magic for the 1st of April mentions Fermat’s Last Theorem. The structure of the joke is fine. If we must ask an irrelevant question of the Information Desk mathematics has got plenty of good questions. The choice makes me suspect Lemon’s showing his age, though. The imagination-capturing power of Fermat’s Last Theorem as a great unknown has to have been diminished since the first proof was found over two decades ago. It’d be someone who grew up knowing there was this mystery about xn plus yn equalling zn who’d jump to this reference.

Tom Toles’s Randolph Itch, 2 am for the 2nd of April mentions “zero-sum games”. The term comes from the mathematical theory of games. The field might sound frivolous, but that’s because you don’t know how much stuff the field considers to be “games”. Mathematicians who study them consider “games” to be sets of decisions. One or more people make choices, and gain or lose as a result of those choices. That is a pretty vague description. It covers playing solitaire and multiplayer Civilization V. It also covers career planning and imperial brinksmanship. And, for that matter, business dealings.

“Zero-sum” games refer to how we score the game’s objectives. If it’s zero-sum, then anything gained by one player must be balanced by equal losses by the other player or players. For example, in a sports league’s season standings, one team’s win must balance another team’s loss. The total number of won games, across all the teams, has to equal the total number of lost games. But a game doesn’t have to be zero-sum. It’s possible to create games in which all participants gain something, or all lose something. Or where the total gained doesn’t equal the total lost. These are, imaginatively, called non-zero-sum games. They turn up often in real-world applications. Political or military strategy often is about problems in which both parties can lose. Business opportunities are often intended to see the directly involved parties benefit. This is surely why Randolph is shown reading the business pages.

## Reblog: Parrondo’s Paradox

Ad Nihil here presents an interesting-looking game demonstrating something I hadn’t heard of before, Parrondo’s Paradox, which apparently is a phenomenon in which a combination of losing strategies becomes a winning strategy. I do want to think about this more, so I offer the link to that blog’s report so that I hopefully will go back and consider it more when I’m able.

My inspiration with my daughter’s 8th grade probability problems continues. In a previous post I worked on a hypothetical story of monitoring all communications for security with a Bayesian analysis approach. This time when I saw the spinning wheel problems in her text book, I was yet again inspired to create a game system to demonstrate Parrondo’s Paradox.

“Parrondo’s paradox, a paradox in game theory, has been described as: A combination of losing strategies becomes a winning strategy. It is named after its creator, Juan Parrondo, who discovered the paradox in 1996.” – Wikipedia.org

Simply put, with certain (not all) combinations, you may create an overall winning strategy by playing different losing scenarios alternatively in the long run. Here’s the game system I came up with this (simpler than the original I believe):

Let’s imagine a spinning wheel like below, divided into eight equal parts with 6 parts red…

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