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Games in the Classroom #3: Can’t Stop

James Fung
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Here’s an example of using conditional probability to solve for a result in Can't Stop.

Q. What is the probability of rolling at least one 7?

A. Let A, B, C, and D represent the four dice rolls. Because of symmetry, it doesn’t matter what A is.

There are 3 cases to consider for B:
* B = 7 – A, a 1-in-6 probability, in which we have failed to not roll a 7.
* B = A, a 1-in-6 probability.
* Otherwise, a 4-in-6 probability.

Therefore, after 2 dice, we have 3 cases:
* 7 has been rolled: probability 1/6
* Only 1 unique number has been rolled: probability 1/6
* 2 unique numbers have been rolled (that don’t add up to 7, which I’ll leave implied): probability 2/6

Let’s add a third die:
* P(7 has been rolled after 3 dice) = P(7 has been rolled after 3 dice | 7 has been rolled after 2 dice) P(7 has been rolled after 2 dice) + P(7 has been rolled after 3 dice | 1 unique number after 2 dice) P(1 unique number after 2 dice) + P(7 has been rolled after 3 dice | 2 unique numbers after 2 dice) P(2 unique numbers after 2 dice) = (1)(1/6) + (1/6)(1/6) + (2/6)(4/6) = 15/36
* P(1 unique number after 3 dice) = P(1 unique number after 3 dice | 1 unique number after 2 dice) = (1/6)(1/6) = 1/36
* P(2 unique numbers after 3 dice) = P(2 unique numbers after 3 dice | 1 unique number after 2 dice) P(1 unique number after 2 dice) + P(2 unique numbers after 3 dice | 2 unique numbers after 2 dice) P(2 unique numbers after 2 dice) = (4/6)(1/6) + (2/6)(4/6) = 12/36
* P(3 unique numbers after 3 dice) = P(3 unique numbers after 3 dice | 2 unique numbers after 2 dice) P(2 unique numbers after 2 dice) = (2/6)(4/6) = 8/36

Sanity check: these add up to 1. Finally, the 4th die:
* P(7 has been rolled after 4 dice) = P(7 has been rolled after 4 dice | 7 has been rolled after 3 dice) P(7 has been rolled after 3 dice) + P(7 has been rolled after 4 dice | 1 unique number after 4 dice) P(1 unique number after 4 dice) + P(7 has been rolled after 4 dice | 2 unique numbers after 3 dice) P(2 unique numbers after 3 dice) + P(7 has been rolled after 4 dice | 3 unique numbers after 3 dice) P(3 unique numbers after 3 dice) = (1)(15/36) + (1/6)(1/36) + (2/6)(12/36) + (3/6)(8/36) = 139/216 = 59.7%

Of course, all this should be rewritten in the usual mathematical notation so it’s a bit easier to read.

However, I’m having trouble coming up with an elegant solution to what is the likelihood of busting if your columns are 6,7,8 or 5,7,8, etc. I think it becomes a rather tedious counting problem, not much better than just having a computer exhaustively check every possibility. Even the probability of rolling any other number (like 8 ) becomes complicated once we lose the symmetry. That may be an important lesson in itself.

(Games in the Classroom #2 is off-site in my wordpress blog.)
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Subscribe sub options Wed Aug 24, 2011 7:12 pm
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Andy Andersen
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I think my brain synapses stopped firing after I finished this.gulp
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  • Posted Wed Aug 24, 2011 10:06 pm
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David H
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I believe that there might not be an elegant solution. Might have to enumerate all the cases.
 
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  • Posted Thu Aug 25, 2011 3:11 am
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James Fung
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Knave wrote:
I believe that there might not be an elegant solution. Might have to enumerate all the cases.

Hence probably not a good example to use in a classroom setting. If students have access to computers, they can do it as an exercise.
 
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  • Posted Thu Aug 25, 2011 3:26 am
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King Ævil

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For a classroom exercise, it may be better to find the probability of rolling a 2 or a 3; that would bring out the combinatoric aspect without so much enumeration of possibilities.

I've solved the probabilities of crapping out given that you have runners on any {X, Y, Z}. I did not find a better solution than to count them up exhaustively; but with a short script in R and a decent desktop computer, it only took a couple of minutes to generate the complete 3-dimensional probability table.

The results were surprising. Pr(crap out | 6, 7, 8) is only about 8%. Only two (symmetrical) triplets—{2, 3, 12} and {2, 11, 12}—had probabilities of crapping out greater than 1/2.

I'll post the probability tables to BoardGameGeek if I can.

A trickier problem is: If you have runners on the bottom rungs of a given triplet, what is the chance that you can advance one of them all the way to the top without crapping out? I didn't find a way to calculate these probabilities analytically; I had to use an empirical approach. There is also an element of strategy here: if you have the choice of advancing one of two different runners, which do you pick? I made what I thought was a reasonable strategy, and ran gobs of simulations to obtain empirical probabilities. I found that, given runners at the bottom rungs of 6, 7 and 8, the chance of advancing (at least) one all the way to the top without crapping out was about 32% (based on 10000 simulated runs). Again, it only took a couple minutes of CPU time on a desktop PC to run the simulation.
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  • Edited Tue Nov 22, 2011 2:09 am
  • Posted Tue Nov 22, 2011 2:07 am
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James Fung
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robigo wrote:
A trickier problem is: If you have runners on the bottom rungs of a given triplet, what is the chance that you can advance one of them all the way to the top without crapping out? I didn't find a way to calculate these probabilities analytically; I had to use an empirical approach. There is also an element of strategy here: if you have the choice of advancing one of two different runners, which do you pick? I made what I thought was a reasonable strategy, and ran gobs of simulations to obtain empirical probabilities. I found that, given runners at the bottom rungs of 6, 7 and 8, the chance of advancing (at least) one all the way to the top without crapping out was about 32% (based on 10000 simulated runs). Again, it only took a couple minutes of CPU time on a desktop PC to run the simulation.

I would probably treat it as a random process with discrete states. Let state (a,b,c) be the probability of finishing one column when rungs 6, 7, and 8 and a, b, and c from the top respectively. You calculated (1,1,1) to be about 0.92. For here, you can work backward and calculate (2,1,1), (1,2,1), etc. This way you can pick the best state to end up in given the choice rather than have to pre-define it.
 
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  • Posted Tue Nov 22, 2011 3:18 am
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