From there, the conversation took off quiickly with a lot of intuition and analysis. There was immediate support for the intuitive threhold of 0.5, which a simple case analysis shows to give the maximum expected value for a player, 0.625. Some head-to-head analysis of various combinations, however, showed other values winning more often than 0.5, with values around 0.6 doing the best. What was up? One of these analyses was wrong, but we weren't sure which. One list member, who had built a quick model in Excel, said,http://fivethirtyeight.com/features/can-you-win-this-hot-new-game-show/

Two players go on a hot new game show called "Higher Number Wins". The two go into separate booths, and each presses a button, and a random number between zero and one appears on a screen. (At this point, neither knows the other's number, but they do know the numbers are chosen from a standard uniform distribution.) They can choose to keep that first number, or to press the button again to discard the first number and get a second random number, which they must keep. Then, they come out of their booths and see the final number for each player on the wall. The lavish grand prize -- a case full of gold bouillon -- is awarded to the player who kept the higher number. Which number is the optimal cutoff for players to discard their first number and choose another? Put another way, within which range should they choose to keep the first number, and within which range should they reject it and try their luck with a second number?

Another said,I think the optimum may be somewhere around .61, but I'm damned if I know why.

To confirm his suspicions, this person went off and wrote a C program -- a "terrible, awful, ugly, horrible C program" -- to compute all the expected values for all possible head-to-head cases. He announced,I can't help thinking we're ignoring something game theoretical. I'm expecting we've all arrived at the most common wrong answer.

He posted a snippet of the output from his program, which showed a steady rise in the win percentages for cutoffs up to a threshold of 0.61, which beat the 99 other cutoffs, with a steady decline for cutoffs thereafter. Before reading the conversation on the mailing list, I discussed the puzzle with my wife. We were partial to 0.5, too, but that seemed too simple... So I sat down and wrote a program of my own. My statistics skills are not as strong as many of my friends, and for this reason I like to write programs that simulate the situation at hand. My Racket program creates players who use all possible thresholds, plays matches of 1,000,000 games between each pair, and tallies up the results. Like the C program written by my buddy, my program is quick and dirty; it replays all hundred combinations on each pass, without taking advantage of the fact that the tournament matrix is symmetric. It's slower than it needs to be, but it gets the job done.We have a winner, according to my program. ... 61 wins.

Player 57 defeats 95 opponents. Player 58 defeats 96 opponents. Player 59 defeats 99 opponents. Player 60 defeats 100 opponents. Player 61 defeats 98 opponents. Player 62 defeats 96 opponents. Player 63 defeats 93 opponents. |