Calculating the Probability of Two Men Meeting Again with a Double Random Walk

In summary, Ricwhat's post seems to be in the right direction. A few thoughts:1.) "Find the probability that they meet again after N steps" -- does this mean the probability that they ever meet again after N steps, or the probability of meeting again at the end of exactly N steps? I presume the latter but wanted to make sure. 2.) You have (nearly) identified the isomorphic problem of a single random walker who moves left one step with probability ##\frac{1}{4}## and right one step with probability ##\frac{1}{4}## and stays in place with probability ##\frac{1}{2}##... there are
  • #1
dRic2
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Homework Statement
Two drunks start out together at the origin, each having equal probability of making step to the left or to the right along the x axis. Find the probability that they meet again after N steps. It is also understood that the men make the steps simultaneously. (It may be helpful to consider the relative motion)
Relevant Equations
Maybe Multinomial coefficients ?
I set up the problem in the following way: considering the relative motion, at each step there is a probability that
- they take a step in the opposite direction going away from each other, so the distance increases and the associated probability is 1/4
- they take a step in the opposite direction but facing each other, so the distance decreases and the associated probability is again 1/4
- the move in the same direction (both to the left or to the right), so the distance stays the same and the associated probability is 1/2

The probability associated to a particular sequence of step is thus:
$$\left ( \frac 1 4 \right)^a \left ( \frac 1 4 \right)^b \left ( \frac 1 2 \right)^c $$
Where a, b, c are the number of steps associated with one of the above mentioned scenarios. Of course I have the constraint that ##a + b + c = N## where ##N## is the total number of steps. Since I do not care about the particular sequence of steps taken but only abut the final distance between the two men after N steps, I need to take into account al the possible permutations:
$$P_{N, a, b} = \frac {N!} {a! b! (N-a-b)!} \left ( \frac 1 4 \right)^a \left ( \frac 1 4 \right)^b \left ( \frac 1 2 \right)^{(N-a-b)} $$
This probability distribution is of course normalized because, by the multinomial theorem
$$\sum_{a, b} P = \left( \frac 1 4 + \frac 1 4 + \frac 1 2 \right)^N = 1$$
Finally, since in order for the tow men to meet again, I need a = b (the distance must increase and decease pf the same number of units) I get that the probability that the 2 men meet again after N step is:
$$P = \sum_{a = 0}^N \frac {N!} {a! a! (N-2a)!} \left ( \frac 1 4 \right)^a \left ( \frac 1 4 \right)^a \left ( \frac 1 2 \right)^{(N-2a)} $$

Is this method correct up until now ?

My problem is that the result is different. I believe the author of the problem found some trick to evaluate the sum, but I can't find any.Thanks
Ric
 
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  • #2
what you have seems to be in the right direction. A few thoughts:

1.) "Find the probability that they meet again after N steps" -- does this mean the probability that they ever meet again after N steps, or the probability of meeting again at the end of exactly N steps? I presume the latter but wanted to make sure.

2.) You have (nearly) identified the isomorphic problem of a single random walker who moves left one step with probability ##\frac{1}{4}## and right one step with probability ##\frac{1}{4}## and stays in place with probability ##\frac{1}{2}##... there are two nice ways to interpret this-- (i) this can be interpretted as a regular simple random walk starting at the origin, considered 2 steps at a time (and then purge all odd nodes since such a walk is bipartite and odd nodes aren't reachable on even steps) or (ii) or if you look at the (countably infinite) transition matrix for a regular simple random walk given by ##P##, what you have is the lazy random walk given by ##\frac{1}{2}(P+I)##. I think (i) will be more amenable to combinatorial methods.

3.) The one nit I see with modelling this as ##\frac{1}{4}## and right one step with probability ##\frac{1}{4}## and stays in place with probability ##\frac{1}{2}##, is what about when their distance is zero? I'd consider this being equivalent to a random walk and 'the origin'. Here I see they stay at the origin with probability ##\frac{1}{2}## and distance increases 'one unit' with probability ##\frac{1}{2}##.

I think you can turn this into an appropriate ballot problem and get the answer though I try to avoid combinatorial methods until the very end.
 
  • #3
Thanks for the replay

1) yes, the latter

2) Mhm... too advanced for me... what's an isomorphic problem ?

3) Mhm I don't think there is a problem with the distance being zero. It's true what you say but looking at the expression
##P_{a, b, N} = \frac {N!} {a!b!(N-a-b)!} \left( \frac 1 4 \right)^a \left( \frac 1 4 \right)^b
\left( \frac 1 2 \right)^{N-a-b}##
for N=1 the constraint ##N = a + b + c## means that I can only have
- ##a = 1##, ##b = 0## and ##c = 0##
- ##a = 0##, ##b = 1## and ##c = 0##
- ##a = 0##, ##b = 0## and ##c = 1##
In the first two cases I get correctly that ##P = \frac 1 4 + \frac 1 4 = \frac 1 2## is the probability that the distance increases. Otherwise if they both move in the same direction I also get correctly c = 1 and so ##P = \frac 1 2##

*********

I'm weak. I gave up and looked for a solution. I feel bad, but I must admit that I would have never figured it out. Oh it's so beautiful. Here it is:

If I look at the expression I found for the probability
$$P = \sum_{a=0}^N \frac {N!}{a!a!(N-2a)!} \left( \frac 1 4 \right)^a \left( \frac 1 4 \right)^a \left( \frac 1 2 \right)^{N-2a}$$
you can notice that it can be obtained from the general expression (multinomial theorem)
$$(1/2 + 1/4 + 1/4)^N = \sum_{a+b+c=N}^N \frac {N!}{a!b!c!} \left( \frac 1 4 \right)^a \left( \frac 1 4 \right)^b \left( \frac 1 2 \right)^c$$
by selecting only the terms with ##a = b## . A ver cleaver and beautiful way to "select" those terms is by evaluating instead the following expression
$$\sum_{a+b+c=N}^N \frac {N!}{a!b!c!} \left( \frac x 4 \right)^a \left( \frac 1 {4x} \right)^b \left( \frac 1 2 \right)^c$$
You can easily see that when ##a = b## the ##x## cancel out and so, for ##a = b##, the terms of this expression are exactly equal to the ones belonging to the expansion of ## \left( \frac 1 4 + \frac 1 4 + \frac 1 2 \right)^N## (which are the one that I need to evaluate).

By the multinomial theorem I get
$$\sum_{a+b+c=N}^N \frac {N!}{a!b!c!} \left( \frac x 4 \right)^a \left( \frac 1 {4x} \right)^b \left( \frac 1 2 \right)^c = \left( \frac 1 {4x} + \frac x 4 + \frac 1 2 \right)^N$$

Exploiting the useful identity ##(z^{1/2} + z^{-1/2})^2 = (z + 1/z + 2)## and expanding with the binomial theorem the resulting square therm:
$$ \left( \frac 1 {4x} + \frac x 4 + \frac 1 2 \right)^N = \left( \frac 1 2 \right)^{2N} \sum_{k = 0}^{2N} \frac {(2N)!} {k!(2N-k)!}(x^{1/2})^k(x^{-1/2})^{2N-k}$$
Now I have to remember that I did all this mess to find the terms where the ##x## cancel out. Looking at the above expression I see that this happens only for ##k = 2N - k##, that is ##k = N##.
So finally I get that
$$P = \left( \frac 1 2 \right)^{2N} \frac {(2N)!}{(N!)N!}$$
 
  • #4
dRic2 said:
2) Mhm... too advanced for me... what's an isomorphic problem ?
a fancy term for 'equivalent'...

dRic2 said:
...
So finally I get that
$$P = \left( \frac 1 2 \right)^{2N} \frac {(2N)!}{(N!)N!}$$
yes, but there's a very easy way to this result. If you understand that your problem is equivalent to a simple random walk (1 person) but viewed 2 steps at a time, this literally reads, on the ##2N##th iteration, what's the probability of getting N heads and N tails (i.e. starting at the origin and a heads being a +1 and a tails being -1, what's the probability of being at the origin on the 2Nth toss), then binomial distribution for coin tossing tells you this happens with probability ##\left( \frac 1 2 \right)^{2N} \frac {(2N)!}{(N!)N!}##. That's really it.
 
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  • #5
I'm having some difficulties imaging the equivalent problem of a single random walk with doubled steps. I don't get the equivalence
 
  • #6
Ahhh. Got it. Now I can see the equivalence. It took a stroll under the rain ahaha
 
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1. What is a double random walk?

A double random walk is a mathematical model that simulates the movement of two individuals in a random environment. Each individual takes a series of steps in random directions, with the length of each step determined by a probability distribution. The paths of the two individuals are independent of each other and can cross or intersect at any point.

2. How is the probability of two men meeting again calculated in a double random walk?

The probability of two men meeting again in a double random walk is calculated by determining the probability that their paths will intersect at any point. This can be done by calculating the area under the curve of the probability distribution for each individual and then multiplying the two probabilities together.

3. What factors can affect the probability of two men meeting again in a double random walk?

The probability of two men meeting again in a double random walk can be affected by the initial starting positions of the two individuals, the size and shape of the environment, and the probability distribution used to determine the length of each step. Additionally, the number of steps taken by each individual can also impact the probability of a meeting.

4. Can the probability of two men meeting again in a double random walk be greater than 1?

No, the probability of two men meeting again in a double random walk cannot be greater than 1. This is because the probability of an event occurring cannot exceed 100%, or 1 in decimal form. If the calculated probability is greater than 1, it is likely that an error has been made in the calculations.

5. How is the concept of a double random walk applied in real-world scenarios?

The concept of a double random walk can be applied in various real-world scenarios, such as predicting the likelihood of two people crossing paths in a crowded city, estimating the probability of two molecules colliding in a chemical reaction, or analyzing the chances of two animals encountering each other in a natural habitat. It can also be used in computer simulations to model the movement of particles or individuals in a dynamic environment.

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