Ladder game based on coin flips

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The discussion focuses on calculating the probability of winning a ladder game with 10 steps, where players can either win, lose but stay on the same step, or bust and start over. The game mechanics are compared to coin flips, with a 50% chance to win (Event A), a 25% chance to lose but not bust (Event B), and a 25% chance to lose and bust (Event C). The probability of winning a single step is determined to be 2/3, leading to the conclusion that the overall probability of winning all 10 steps is (2/3) raised to the power of 10. The mathematical approach involves summing probabilities of different outcomes, emphasizing the importance of avoiding two consecutive losses. The analysis provides a clear framework for understanding the game's odds.
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Hi there, found a situation where I'm trying to find the probability of winning a ladder type game.

There are 10 steps and the goal is to win all 10 steps without busting (i.e. a streak, given one exception below in event B). On any given step three different events can occur:
Event A: 50% chance to win, move up to next step.
Event B: 25% chance to lose but not bust, stay at same step. A "life saver" if you will.
Event C: 25% chance to lose and bust. Start over.

Think of it like flipping a coin, but with a double elimination. So on a heads you move up a step. On a tails you flip again, a heads after the tails and you reset to this level, but if you get tails again, you lose. The best way I could think of it mathematically is what is the probability that you will have 10 more heads than tails and never have two tails in a row.

For instance
HHHHHHHHHH
HHHthHHthHHHthHthH
both produce a win. But
HHHthHHtt
loses after winning 5 steps.

Any ideas? Been a few years since I've done any higher math. The 10 more heads than tails portion sounds like standard deviation, but I never used that in any class I took.
 
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Well, you can ignore the B's, and suppose it is A with 2/3 chance and C with 1/3 chance. On any given "run" (that is, starting from 0) the probability that you get n A's followed by a C is (1/3) * (2/3)^n. The probability that you win on a given run is therefore
\sum_{i=10}^\infty \frac{(2/3)^i}{3} \\<br /> = \frac{(2/3)^{10}}{3}\sum_{i=0}^\infty (2/3)^i \\<br /> = (2/3)^{10}.
 
mXSCNT said:
Well, you can ignore the B's, and suppose it is A with 2/3 chance and C with 1/3 chance. On any given "run" (that is, starting from 0) the probability that you get n A's followed by a C is (1/3) * (2/3)^n. The probability that you win on a given run is therefore
\sum_{i=10}^\infty \frac{(2/3)^i}{3} \\<br /> = \frac{(2/3)^{10}}{3}\sum_{i=0}^\infty (2/3)^i \\<br /> = (2/3)^{10}.

It wasn't obvious to me that A has 2/3 chance, but I'm guessing you worked it out this way: To win a single step needs one of H, thH, ththH,... which has total probability
\sum_{k=0}^\infty (1/4)^k*(1/2)=(1/2)/(1-1/4)=2/3
and thus to win 10 steps the probability is (2/3)^{10}.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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