Archive for the ‘Game Theory’ Category

Connect6

October 23, 2007

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I’ve been enjoying playing the game Connect6 with my son Adam. The game was invented and introduced by Professor I-Chen Wu, from National Chiao Tung University in Taiwan. Connect6 is played with a Go board and stones. The object is to place six stones in a row, diagonally, horizontally, or vertically. On the first turn, Black places a single stone; after that each player places two stones per turn. Because each player will always have placed one more stone than his or her opponent after each turn, the game appears to be balanced.

One potential concern about this notion of balance is that perhaps the second player should place his or her stones far from the first stone, to get a two-stone advantage somewhere else on the board, and possibly forcing the first player to follow in that part of the board. Fortunately, Wu and a colleague demonstrated that this initial break-away strategy is unlikely to be good for White in this paper.

Anyways, it’s not clear whether with perfect play the game should be a win for the first player, a win for the second player, or a draw (with neither player ever able to achieve six-in-a-row.) If I had to guess, I would venture a draw, even on an infinite board, but on the other hand my actual games have all ended in victory for somebody.

The game is very similar to Gomoku (also known as Connect 5), where one tries to get five stones in a row, but each player only places one stone at a time. Of course, that game favors the first player, and in fact it has apparently been demonstrated that the first player wins with perfect play.

Renju is an older and much less elegant approach to balancing Gomoku. In Renju, the first player is restricted from making moves which make certain types of threats. Looking at all the complications in the Renju rules, I find it surprising that it took so long for Connect6 to be introduced.

In fact, aside from the issues of fairness and elegance of rules, I also find that Connect6 has a more dynamic feel than Gomoku or Renju; I definitely prefer it.

Because of the large number of possible moves each side can make each turn, and the difficulty of evaluating a position, it’s not easy to program a computer to play Connect6 well; I don’t think any programs exist yet that play as well as humans. You can play Connect6 against some relatively weak bots and other humans at Vying Games, which also features other interesting turn-based strategy games (currently Checkers, Pente, Keryo-Pente, Phutball, Breakthrough, Othello, Kalah, Oware, and Footsteps).

Enhanced Chess

October 8, 2007

For a long time, Chess was considered the “ultimate test of cerebral fitness,” as the 1980’s song “One Night in Bangkok” proclaimed. Its popularity probably peaked in 1972, during the Fischer-Spassky world championship match which seemed to capture the interest of the entire world. Lately, however, its popularity has seriously declined, because of a number of factors:

1. Humans are no longer the best Chess players in the world. It’s somewhat depressing to study and play a game when you know that a program you can buy for $50 can out-play you, or any other human.

2. Chess opening theory has become extraordinarily detailed, and onerous to study. Very often, grandmaster games begin with 20 or more known theoretical moves. Moreover, nowadays most new theoretical novelties are suggested and analyzed by computer programs used by top players before their games. If a player does not keep up with opening theory, he will be at a disadvantage, but many people find the prospect of studying openings in this way increasingly dehumanizing.

3. Most games between top-flight Chess grandmasters end in a draw, simply because Chess is almost certainly a draw with best play. To take one example, Kramnik won his match against Kasparov in 2000 with 2 wins, no losses, and 14 draws.

I want to propose here a Chess variant, that I developed with my son Adam, that addresses all these problems. We call this variant “Enhanced Chess.” In Enhanced Chess, there are no draws, there is no opening theory (or endgame theory) to study, there is no advantage for the first player, and humans should out-class computers for a long time to come. The game is extremely skill-intensive; the more imaginative and “cerebrally fit” competitor will definitely win the game.

The game combines a couple existing Chess variants. The first of these variants is “Bughouse Chess.” Ordinary Bughouse Chess is played by two teams of two players each. On each team, one player plays White and the other plays Black. When you capture one of your opponent’s pieces, you hand it to your partner, who can drop it on his own board rather than making an ordinary move. Pawns may not be dropped on the first or eighth rank, but otherwise there are no restrictions on drops; you can even checkmate with a dropped piece. When a pawn reaches the eighth rank, it is promoted normally, but if the promoted piece is captured, it turns into a pawn again. The game ends when one of the kings is checkmated.

In ordinary Bughouse you may communicate freely with your partner. It is normally played with fast time controls (e.g. 5 minutes for each player for the game) as a very amusing social game with lots of trash-talk. Here’s a video of a typical Bughouse game, where the only unusual thing is that one of the players (at the back left) is Levon Aronian, one of the strongest Chess grandmasters in the world, and a participant in the 2007 World Chess Championship:

In Enhanced Chess on the other hand, a single player will control both the boards on his side, so that the game is again a two-player game. Moreover, the moves are made in a strict turn-based order. First Player 1 makes a move with White, then Player 2 makes a Black move followed by a White move, and then the players continue to alternate, always making a Black move followed by a White move. Notice that because Player 1 only makes one move on his first turn, and then Player 2 makes two moves, there is no obvious advantage to going first or second.

Time controls can be as slow as you like; Enhanced Chess is meant as a serious game.

Turn-based Bughouse is a good game, but if it became standard, it would quickly suffer from the problem of excessive opening theory. But that problem can be remedied by using the idea, proposed and popularized by Bobby Fischer for ordinary Chess, of randomizing the initial position. In Enhanced Chess, the initial random position must be identical on the two boards, as in the following diagram of an initial position:

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Chess with randomized initial positions (sometimes called “Fischer Random Chess” or “Chess960” because of the 960 legal starting positions) is already a somewhat popular variant. Levon Aronian is the current “world champion” of this variant, having beaten Vishy Anand (the current world chess champion in ordinary Chess) in a short match in 2007.

One difficult point in Chess960 is how to handle castling. In Enhanced Chess, we propose to handle this problem by simply forbidding castling. Castling is usually of dubious utility in any Bughouse Chess variant in any case, and there’s no reason to carry all the complicated rules baggage. There would also be more than 960 legal starting positions, because while Chess960 tries to ensure that the kings start between the rooks, in Enhanced Chess that is not necessary. (We still required that the two bishops on each side start on opposite color squares).

It would in fact probably be sufficient if at the beginning of the game, the players alternated on deciding the positions of one piece at a time. For example, my first move would decide that the Kings go on b1 and b8. You then decide that one of the Knights should be placed on f1 and f8, and so on (one needs to forbid placements that place the bishops on the same color square or leave no option but for that to happen in the future). This version would remove any element of luck from the game, and I doubt that studying openings would pay with even this amount of “pre-processing.”

Making a pair of moves which repeats a position (on the combined boards) is forbidden in Enhanced Chess, and stalemate is a loss. These rules remove any theoretical possibility of a draw.

It is interesting to speculate on what the “correct” result would be in Enhanced Chess if God played against God. There is no obvious end to the game until positions start repeating. It’s clearly just idle speculation, but how many moves do you think would need to occur in the perfect game tree before one side won?

In practice, I don’t think that Enhanced Chess games would typically last any longer than ordinary Chess games, although a big difference is that players would definitely need to rely much more on intuition and much less on knowledge and calculation. One other big difference, which would definitely be a drawback for some players, is that reduced-material end-games would cease to exist. Enhanced Chess would be much like Shogi (Japanese Chess) on steroids, but maintaining all the familiar pieces (and removing the advantage of moving first).

Finally, I should address why I think computer programs will play Enhanced Chess poorly, at least in comparison to ordinary Chess. The main point is that programs basically use a search algorithm, that would be hobbled by the much larger number of possible moves in each position (remember that for each turn you get one move on each board, and all the drops mean many more possibilities as well, so for each turn there might be 10,000 possibilities for the computer to consider, instead of 30 or so). Evaluating a position will also much more difficult for a computer in Enhanced Chess compared to ordinary Chess, because material and mobility are less important. I would anticipate based on these points that Enhanced Chess programs would perform significantly worse than Shogi (Japanese chess) programs (where there are drops, but only one move per turn), and even in Shogi, computer programs still fall somewhat short of the top level.

In the 15th century, a series of major rules modifications were accepted into Chess (the modern Queen, Bishop, and Pawn moves, Castling, and en passant) that succeeded in reviving what had become a rather dull game. It seems to me that something similar will need to occur again before Chess can reverse its decline and regain its status as the King of Games.

 

Rock-Paper-Scissors Without the Luck

October 6, 2007

As I’ve mentioned before (click here for his “Duke and Pawn” game, or click here for a more game-theoretical type of game), my son Adam enjoys designing games. Here’s a game he designed, with very simple rules but complex play, on the theme of Rock-Paper-Scissors.

1. The two players are White and Black, and they each have three pieces, a Rock, a Paper, and a Scissors, that they set up on a 6 by 6 board as shown below.

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2. White moves first, and then the players alternate.

3. Normally, a player moves a single one of his pieces one square orthogonally (up or down or right or left) on each move. You may not place a second piece on the same square except to capture. (So for example, on the first move, White’s legal moves are to move his Paper up or to the left, or his Scissors up or to the left.)

4. Rocks can capture Scissors, Scissors can capture Papers, and Papers can capture Rocks (of the opposite color in each case) but no other captures are possible. A piece can be moved a second time on the same move if and only if it enables it to make a capture that move (e.g. if a Paper is diagonally below and to the right from an opposing Rock, it could move up and then move left in order to capture the Rock.)

5. The object of the game is to move one of your pieces to the square where the opposing Rock is placed at the beginning of the game. If you do so, you instantly win the game.

6. You may never repeat a position.

7. You must move each turn. If you have no moves, you lose.

The game has no draws. One important and non-obvious point of strategy: if you exchange your Rock for your opponent’s Scissors, you will have an important material advantage, because your Paper can safely rampage without worrying about any of his pieces, while his Rock still has to worry about your Paper, and his Paper still has to worry about your Scissors.

It might appear that the first player has a significant advantage, but our games haven’t worked out that way at all. By the way, we use Chess pieces for the pieces; rooks for rocks, pawns for papers, and bishops for scissors.

I like the irony that ordinary Rock-Paper-Scissors is a game of complete chance and Adam’s version has no chance. Have fun!

(EDIT: This game now has a name; it’s called “Cyclotron.”)

 

Duke and Pawn Endgames

September 3, 2007

Note: to understand this post, you don’t need to know anything about chess; I’ll explain everything necessary right here.

One of the best ways for a chess beginner to improve is to study king and pawn endgames, and one of the best ways to do that is to play a game with only kings and pawns. Just remove all the rest of the pieces, and have at it.

Unfortunately, while Chess with just kings and pawns is not trivial, as you improve you will eventually outgrow it, because between two good chess players, the game is very likely to end in a draw.

As I mentioned in an earlier post, my son Adam likes to design games, and he designed this very clever variant of the King and Pawn endgame that eliminates the possibility of a draw.

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Rules:

Set up the pieces as above, with four pawns plus a King for both White and Black.

The players alternate moves, starting with White.

The pawns move as in chess: one square forward, or optionally two squares forward if they haven’t moved yet, and they capture a piece one square ahead of them diagonally.

Pawns may still capture en passant as in Chess, which means the following. If an enemy pawn moves two squares forward, and one of your pawns could have captured it if it had only moved one square forward, you may capture that pawn as if it had only moved one square forward, but only if you do so with your pawn on the turn immediately after the enemy pawn moves.

The Kings can only move one square up or down, or left or right; they cannot move diagonally. (Such limited versions of Kings are sometimes called “Dukes;” hence the title of this post.)

You win the game immediately if you capture the opponent’s King, or if one of your pawns reaches the other side of the board.

You must always move; if you cannot make a legal move, you lose the game. (This is different from Chess, where stalemate is a draw.)

You may not make a move which recreates a position that has previously occurred during the game. (This is also different from Chess, but such a rule exists in Shogi, the Japanese version of Chess.)

That’s all the rules. In this game, draws are impossible, even if only two Kings remain on the board. For example, imagine that the White King is on a1, and the Black King is on b2. If White is to move, he loses, because he must move to a2 or b1, when Black can capture his King. But if Black is to move, he must retreat to the north-east, when White will follow him, until Black eventually reaches h8 and has no more retreat.

So arranging that your opponent is to move if the two Kings are placed on squares of the same color (if there are no pawn moves) is the key to victory. This concept, known as the “opposition,” is also very important in ordinary Chess King and pawn endgames, which is why skill at chess will translate into skill in this variant, and why improving your play at this variant will improve your Chess. Of course, the pawns are there, and they complicate things enormously!

Naturally, one can consider other starting positions, with different numbers of pawns.

This game can be analyzed using the methods of Combinatorial Game Theory (CGT). Noam Elkies, the Harvard mathematician, has written a superb article on the application of CGT to ordinary chess endgames, but it required great cleverness for him to find positions for which CGT could be applied; with this variant, the application of CGT should be much easier.

Combinatorial Game Theory

August 29, 2007

Combinatorial Game Theory (CGT) is a mathematical discipline that studies two-player, zero-sum games, where the players play sequentially, and there is perfect information and no chance. The aim of CGT is to build mathematical theories that will help players win such games! In practice, the ideas from CGT are of minimal help to the serious player in more complicated games like Chess or Go, but the theories are extremely helpful for simpler but still non-trivial games like Dots-and-Boxes.

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The CGT bible is Winning Ways for Your Mathematical Plays, a four-volume set of books (for the 2nd edition from the early 2000’s; the first edition from 1982 was in two volumes), by the eminent mathematicians Elwyn Berlekamp, John Horton Conway, and Richard Guy.

The basic idea of CGT is to develop a calculus, which lets you assign a value to a position in the game. One normally considers games with the condition that a player who cannot move loses. If both players are without a move in a position (whoever moves loses), the position is assigned a value of 0. If the player on the right can make one move that transforms the position to one of value 0, and the player on the left cannot move at all, the position is assigned a value of 1 (if the roles are reversed, the position has value -1).

Now what happens if the player on the left has a move to a position of value 0, and the player on the right has a move to a position of value 1? What is the value of that position? It turns out to be worth 1/2, as can be seen by considering the position obtained by adding such two such positions together with a position of value -1, when one gets back to a position of value 0 where whoever moves loses (I’ll let you work out the details).

There are more different kinds of values than just fractions. Berlekamp, Conway, and Guy consider a multitude of different interesting games, which give rise to ever deeper mathematics, and game positions with strange values along with complex rules for combining those values.

The writing is informal, with constant punning, but the mathematics is serious. Still, one charm of the field is that there is no pre-requisite mathematical material needed to understand these books–bright high school students can launch right in.

After finishing “Winning Ways,” the ambitious student can move on to the two volumes “Games of No Chance” and “More Games of No Chance” (note that nearly all the papers in these books are available online), and then move onto the rest of the papers collected at David Eppstein‘s CGT page.

Also, take a look at Aaron Siegel‘s Combinatorial Game Suite software to help you do CGT computations.

There’s still much more to say, but I’ll leave that for other posts.

On Rationality

August 28, 2007

I want to expand on what I wrote previously in “A Simple But Challenging Game: Part II, this time focusing on Rosenthal’s Centipede Game. To remind you of the rules, in that game there are two players. The players, named Mutt and Jeff, start out with $2 each, and they alternate rounds. On the first round, Mutt can defect by stealing $2 from Jeff, and the game is over. Otherwise, Mutt cooperates by not stealing, and Nature gives Mutt $1. Then Jeff can defect and steal $2 from Mutt, and the game is over, or he can cooperate and Nature gives Jeff $1. This continues until one or the other defects, or each player has $100.

As I previously wrote, in this game, the Nash equilibrium is that Mutt should immediately defect on his first turn. This result is obtained by induction. When both players have $99, it is clearly in Mutt’s interest to steal from Jeff, so that the he will end with $101, and Jeff will end with $97. But that means that when Jeff has $98 and Mutt has $99, Jeff knows what Mutt will do if he cooperates, and can see that he should steal from Mutt, so that he will end with $100 and Mutt will end with $97. But of course that means that when both players have $98, Mutt can see that he should steal from Jeff, and so on, until one reaches the conclusion that Mutt should start the game by stealing from Jeff.

Of course, this Nash equilibrium behavior doesn’t really seem very wise (not to mention ethical), and experiments show that humans will not follow it. Instead they usually will cooperate until the end or near the end of the game, and thus obtain much more money than would “Nashists” who rigorously follow the conclusions of theoretical game theory.

Game theorists often like to characterize the behavior of Nashists as “rational,” which means that they need to explain the “irrational” behavior of humans in the Rosenthal Centipede Game. See for example, this economics web-page, which gives the following “possible explanations of ‘irrational’ behavior”:

There are two types of explanation to account for the divergence. The first assumes that the subject pool contains a certain proportion of altruists who place a positive weight in their utililty function on the payoff of their opponent. Also to the extent that selfish players believe that there is some probability that other players are altruists, they have an incentive to mimic altruistic behaviour by passing.

The second explanation considers the possibility of action errors. Errors in action, or ‘noisy’ play, may result from subjects experimenting with different strategies. Or simply from subjects pressing the wrong key.

Let’s step back for a second and consider what “rational” behavior should mean. A standard definition from economics is that a rational agent will act so as to maximize his expected utility. Let’s accept this definition of “rational.”

The first thing we should note is that “utility” is not usually the same as “pay-off” in a game. As noted in the first explanation above, many people get utility from helping other people get a pay-off. But there are many other differences between pay-offs and utility. You might lose utility from performing actions that seem unethical or unjust, and gain utility from performing actions that seem virtuous or just. You might want to minimize the risk in your pay-off as well as maximize the expected pay-off. You might value pay-offs in a non-linear way, so that the difference between $101 and $100 is very small in terms of utility.

Of course, this difference between pay-off and utility is very annoying theoretically. We’d really like the pay-offs to strictly represent utilities, but unfortunately for experiments, it is only possible to hand out dollars, not some abstract “utils.”

But suppose that the pay-offs in the Rosenthal Centipede Game really did represent utils. Would the game theory result really be “rational” even in that case? Would the only remaining explanation of cooperating behavior be that the players just don’t understand the situation and are making an error?

No. Remember that to be “rational,” an agent should maximize his expected utility. But he can only do that conditioned on some belief about the nature of the person he is playing with. That belief should take the form of a probability distribution for the possible strategies of his opponent. A Nashist rigidly reasons by backward induction that his opponent must always defect at the first opportunity. He even believes this if he plays second, and his opponent cooperates on the first turn! But is this the most accurate belief possible, or the one that will serve to maximize utility? Probably not.

A much more accurate belief could be based on the understanding that even people who understand the backward induction argument can reason beyond it and see that many of their opponents are nevertheless likely to cooperate for a long time, and therefore it pays to cooperate. If you believe that your opponent is likely to cooperate, it is completely “rational” to cooperate. And if this belief that other players are likely to cooperate is backed by solid evidence such as the fact that they started the game by cooperating, then the behavior of the Nashist, based on inaccurate beliefs that cannot be updated, is in fact quite “irrational,” because it does not maximize his utility.

Sophisticated game theorists do in fact understand these points very well, but they muddy the waters by unnecessarily overloading the term “rational” with a second meaning beyond the definition above; they in essence say that “rational” beliefs are those of the Nashist. For example, take a look at this 1995 paper about the centipede game by Nobel Laureate Robert Aumann. Aumann proves that “Common Knowledge of Rationality” (by which he which he essentially means the certain knowledge that all players must always behave as Nashists) will imply backward induction. He specifically adds the following disclaimer at the end of his paper:

We have shown that common knowledge of rationality (CKR) implies backward induction. Does that mean that in perfect information games, only the inductive choices are appropriate or wise? Would we always recommend the inductive choice?

Certainly not. CKR is an ideal (this is not a value judgement; “ideal” is meant as in “ideal gas”) condition that is rarely met in practice; when it is not met, the inductive choice may be not only unreasonable and unwise, but quite simply irrational. In Rosenthal’s (1982) centipede games, for example, even minute departures from CKR may make it incumbent on rational players to “stay in” until quite late in the game (Aumann, 1992); the resulting outcome is very far from that of backward induction. What we have shown is that if there is CKR, then one gets the backward induction outcome; we do not claim that CKR obtains or “should” obtain, and we make no recommendations.

This is all well and good, but why use the horribly misleading name “Common Knowledge of Rationality” for something that would be more properly called “Universal Insistence on Idiocy?”

I hope it is obvious by now why I am skeptical of explanations of various types of human behavior that are based on assuming that all humans are always Nashists, and even more skeptical of recommendations about how we should behave that are based on those same assumptions.

[Acknowledgement: I thank my son Adam for discussions about these issues.]

A Simple but Challenging Game: Part II

August 15, 2007

Here’s Part I again:

My 15-year-old son Adam likes game theory. He invented the following simple game, and asked me about it when I got on the phone with him while I was away at a conference last month (I’ve simplified and formalized the set-up slightly):

There are two players, each of whom is given a real number which is chosen randomly from a uniform distribution between 0.0 and 1.0. The players know their own number but not their opponent’s. One player moves first and has the choice of passing or challenging. If he challenges, both players reveal their number, and the player with the higher number receives a payoff of 1, while the other player receives a payoff of 0. If the first player passes, the second player has a choice of challenging or passing. If he challenges, again both players reveal their numbers and the player with the higher number receives a payoff of 1, while the other player receives a payoff of 0. If the second player also passes, both players receive a payoff of 1/2. They play the game one time, and are interested in maximizing their expected payoff.

What is the right strategy? For example, if you received the number 0.17, would you pass or challenge if you were the first player? What about if you were the second player? What would you do if the number you received was 0.0017?

I’ll tell you more in a later post, but for now why don’t you think about it….

Here’s the promised followup:

It is clear that if any player has the advantage, it’s the second player, because he gets some information from the first player, and can use it to make his decision. Nevertheless, the first player can adopt the strategy of always challenging, and thereby guarantee that he wins half the time. So apparently he should always challenge. This is the answer that was given by “Optionalstopping” in the comments. The same answer was given to me by the evolutionary game theorist Arne Traulsen (who has recently worked on a ground-breaking theory for the emergence of punishment), after I asked him about the game at a lunch conversation.

There is another way to arrive at the same answer. Assume that each player chooses a strategy parameterized by a single value; if he receives a number above that value, he challenges, while if he receives a number below that value he passes. If you work out the Nash equilibrium (basically, that means that both you and your opponent pick the strategy that gives you the best payoff assuming that the opponent is maximizing their payoff), you’ll find (I won’t bore you with the math) that the value for both players is zero–they should always challenge. My son Adam gave an intuitive version of this argument, without the math. Of course it’s true that probabilistic strategies are also possible. I haven’t proven it, but I strongly doubt that introducing probabilistic strategies will change the result that the Nash equilibrium is to always challenge for both players.

So at first I thought the matter was settled. But still, there is something very weird about this result. Would you really challenge if you were the first player and you received a .001? Would you really?

And what if you were the second player, and the first player passed, and you had a .000001? You know that the first player is not “following the rules” of Nash equilibrium. Are you really going to challenge because Nash tells you to? It’s obviously a crazy result! There must be a hole in the arguments.

So what’s wrong with the above arguments?

First let’s start with the Nash equilbrium arguments. By the way, many authors use the term “rational” for players that use strategies dictated by Nash equilibrium arguments, but I think “rational” and “irrational” are excessively loaded terms, so I prefer to instead say that a player that follows a strategy dictated by Nash equilibrium arguments is a “Nashist.”

If you are the second player, and the first player has passed, you can deduce that the first player is not a Nashist. So in order to make a correct play (maximize your expected winnings) you need to choose some probability distribution for what the first player’s strategy is, and then compute whether you will win more or less depending on whether you challenge. There doesn’t seem to be any obvious way to choose the probability distribution for the first player’s strategy, but you can definitely say that he is not certainly a Nashist! A Nashist is stuck (by definition of being a Nashist) believing that all other players are always Nashists, even in the face of clear evidence that they are not (you see why I don’t like to call Nashists “rational”) and would choose the strategy that followed from that obviously wrong belief; he would always challenge as the second player.

A non-Nashist, on the other hand, can come up with a reasonable probability distribution for the first player’s strategy, and come to the conclusion that he should pass if he is the second player and he has a .000001.

OK, so we can see why the second player might want to pass if he receives a .000001. What about the first player: is it wrong to be a Nashist? Should you pass or challenge if you get a .001, or a .000001, or a .000000001? A Nashist would be compelled, by the force of his “idealogy,” to challenge in each case. But you can make a good argument that that’s wrong. Instead, let’s say I am the first player and I have a .000001. I know that if I pass, the second player will be able to deduce that I’m not a Nashist, and will go through the argument given above for the second player. Now “all” I have to do is form my probability distribution for what his probability distribution of my strategy will be, and compute whether I will have more chance of winning depending whether I challenge or not. Again there’s no obvious way to choose these probability distributions, but it seems pretty clear to me to that reasonable probability distributions will give a result that says don’t challenge if you are the first player and you have a sufficiently low number.

Well what about the other argument, that says that the first player should always challenge, since he is at a disadvantage and if he always challenges, he’ll win half the time? It seems paradoxical to think that there is a strategy that can do better than winning half the time for the first player.

Of course, all Nashists will always win exactly half the time, whether they play first or second. If you are playing against someone who you know is a Nashist, it actually doesn’t matter what you do! But suppose instead that you are playing against an ordinary human. You should play forming the best possible probability distribution of what they will do. Many humans will challenge if they have a number above .5 and pass otherwise, whether or not they play first (a very bad strategy by the way). It is perfectly possible, indeed likely, that playing against a population of ordinary humans, there exists a strategy that wins more than half the time for the first player. I can’t prove that strategy exists by arguing in this way (one would need to run experiments to determine the probability distributions of strategies, and then it would be easy to compute), but I’m actually pretty confident that it does exist, and I’m also pretty confident that the strategy involves passing when you are given a .000001 as the first player.

So I don’t believe either argument for always challenging holds up, which is comforting, because always challenging does seem intuitively wrong. Unfortunately, I can’t tell you exactly what the optimal strategy is either, at least until you tell me what the true probability distribution is for player strategies.

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By the way, Arne recommended that I pick up Game Theory Evolving, by Herbert Gintis, for my son. It’s a wonderful book, full of interesting games and solved problems in game theory. Adam and I both love it. Gintis gives other examples showing that Nashists (he calls them “self-regarding agents”) can choose bizarre strategies, including “Rosenthal’s Centipede Game:”

The players, Mutt and Jeff, start out with $2 each, and they alternate rounds. On the first round, Mutt can defect by stealing $2 from Jeff, and the game is over. Otherwise, Mutt cooperates by not stealing, and Nature gives Mutt $1. Then Jeff can defect and steal $2 from Mutt, and the game is over, or he can cooperate and Nature gives Jeff $1. This continues until one or the other defects, or each player has $100.

In this game, the Nash equilibrium, obtained by induction by working backwards from the end of the game, when it is clearly “correct” to defect, is that Mutt should immediately defect on his first turn. So that’s what a Nashist would do, but fortunately humans are much more “rational” than Nashists!

A Simple but Challenging Game, Part I

August 9, 2007

My 15-year-old son Adam likes game theory. He invented the following simple game, and asked me about it when I got on the phone with him while I was away at a conference last month (I’ve simplified and formalized the set-up slightly):

There are two players, each of whom is given a real number which is chosen randomly from a uniform distribution between 0.0 and 1.0. The players know their own number but not their opponent’s. One player moves first and has the choice of passing or challenging. If he challenges, both players reveal their number, and the player with the higher number receives a payoff of 1, while the other player receives a payoff of 0. If the first player passes, the second player has a choice of challenging or passing. If he challenges, again both players reveal their numbers and the player with the higher number receives a payoff of 1, while the other player receives a payoff of 0. If the second player also passes, both players receive a payoff of 1/2. They play the game one time, and are interested in maximizing their expected payoff.

What is the right strategy? For example, if you received the number 0.17, would you pass or challenge if you were the first player? What about if you were the second player? What would you do if the number you received was 0.0017?

I’ll tell you more in a later post, but for now why don’t you think about it….

Multicellular Logic Circuits, Part I: Evolution

August 7, 2007

If we want to construct artificial machines that rival the capabilities of biological organisms, we should try to understand the principles by which complex natural “machines” such as plants and animals are created.

It is generally agreed, at least by scientists, that all natural organisms have been “designed” by the completely blind and random process of evolution. Through evolution, a population of organisms tends to become progressively better adapted to its environment via the mutation of genomes of individuals in the population, and the selection and more rapid reproduction of the fittest organisms in that population.

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Harvard professor Martin Nowak a has written a lovely and elegant book describing the mathematics of evolutionary dynamics, using the ideas of evolutionary game theory; here is a video of Nowak describing evolutionary game theory at Harvard in 2004.

My own interest is not so much in analyzing evolution, but in exploiting it. If we understand evolution so well, shouldn’t we be able to use it to design useful machines?

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Of course, humans have already for many centuries exploited evolution, using artificial selection to breed domesticated animals or cultivate useful plants.

But I am looking for something else: the design of artificial machines through artificial selection. Although it has never been a mainstream idea, computer scientists have pursued such dreams since the 1950’s. When I was in graduate school in the 1980’s, I loved reading John Holland’s seminal 1975 book “Adaptation in Natural and Artificial Systems.”

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Holland and his students were deeply influential in popularizing the whole field of genetic algorithms.

Another important figure in the field is John Koza, who has advocated for many years one of the most important variants of genetic algorithms, which he calls “genetic programming.” In genetic programming, computer programs, typically written in Lisp, are evolved through a process that involves mutating the programs by altering or swapping branches of the computation tree representing the program.

Genetic programming and genetic algorithms more generally, have had considerable success creating interesting and useful systems and programs. Nevertheless, I think it is fair to say that these ideas are still considered “fringe” ideas in the scientific and engineering community, and they have not widely replaced more conventional software and hardware design strategies.

So what might be missing? I will begin discussing that in Part II.