Probability: Expected distance of an accelerating car - with a twist

AI Thread Summary
The discussion revolves around calculating the expected distance a race car travels on a straight track before crashing, with the car's velocity increasing based on random number generation. The initial problem was solved, yielding an average distance of 3540 units before a crash occurs. The more complex challenge is to determine the expected distance after a set number of turns, specifically 60, while accounting for resets in velocity upon crashing. A recurrence relation is proposed to facilitate this calculation, incorporating triangular numbers and probabilities of crashing. The discussion highlights the need for computational assistance to solve the recurrence for any given number of turns.
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I have an expected value probability problem. Unfortunately, I do not know enough about mathematics to determine if it is even remotely feasible to try to solve it. The best way to explain it is to disguise it as a physics problem:

Imagine a race car on a never-ending straight track. Let's call the position it starts at 0 units, and its starting velocity is 0 units per turn. Suppose we generate a random number from 1 to 60. If any number except 60 comes up, its current velocity is increased by 1, and the car is moved forward the number of units corresponding to the new velocity. However, if a 60 is rolled, it crashes: the current velocity is reset to 0, and the car is not moved (as the new velocity is 0u/t).

Now, the initial problem I worked out was how many units, on average, the car will travel before it crashes once. That was fairly easy: because the distance the car travels corresponds to a triangular number [n(n+1)/2], and the probability of the trial ending is always 1/60 each turn, the expected value corresponds to the equation: sum of (n(n+1)/2)(1/60)(59/60)^n from n=0 to n=infinity = 3540

However, I have another problem I am trying to solve that is proving much more difficult:
What is the expected distance of the car after 60 (or in general, n) turns?
In this scenario, when a car crashes, its velocity is reset to 0, but it is still "in the race" for the remaining number of turns.

I have tried all different kinds of techniques to try to figure this out, but to no avail. I'm hoping this isn't too complicated or tedious to compute, but considering there are 966,467 ways to partition 60, all with differing odds of occurring, I'm not too confident. Doing some research, I found that something called a "Markov chain" might help, but I am not familiar with how those work.

Does anyone know how I can tackle this? It is nothing really important, just a thought experiment I was curious about, but it is really bugging me.
 
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One thing you do NOT say is how often this random number is generated. Not knowing long the car moves at a given velocity, you cannot say how far it would go.
 
HallsofIvy said:
One thing you do NOT say is how often this random number is generated. Not knowing long the car moves at a given velocity, you cannot say how far it would go.

It is discrete: each time a number is generated it is a turn, and the velocity is changed and the car is moved forward. For example, given the sequence of generated numbers: 53 04 34 60 23 47, that is 6 turns and the car is moved 1+2+3+0+1+2 = 9 units. What I am trying to figure out is the expected value after 60 turns.
 
Let d_n be the expected distance that the car will travel in a race of duration n. Let T be the last time the car is stopped (so if there is no crash we'll put T = 0). Now we can set up the following recurrence for d_n:
d_n = \sum_{i=0}^n P(T=i)(d_{i-1} + \Delta(n-i))
where \Delta(m) is the mth triangular number, and we assume d_{-1} = d_0 = 0. Now, P(T=i) = (1/60)(59/60)^{n-i}, so this recurrence let's you easily calculate d_n for any n (using a computer, of course).

The explanation of this is simple: if the car crashes for the last time at time i, it traveled an average of d_{i-1} steps in the time between the start of the race and i and travels exactly \Delta(n-i) steps after that time.
 
I was reading a Bachelor thesis on Peano Arithmetic (PA). PA has the following axioms (not including the induction schema): $$\begin{align} & (A1) ~~~~ \forall x \neg (x + 1 = 0) \nonumber \\ & (A2) ~~~~ \forall xy (x + 1 =y + 1 \to x = y) \nonumber \\ & (A3) ~~~~ \forall x (x + 0 = x) \nonumber \\ & (A4) ~~~~ \forall xy (x + (y +1) = (x + y ) + 1) \nonumber \\ & (A5) ~~~~ \forall x (x \cdot 0 = 0) \nonumber \\ & (A6) ~~~~ \forall xy (x \cdot (y + 1) = (x \cdot y) + x) \nonumber...
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