Random 1D Walk with different step sizes.

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In summary, the conversation discusses a random walk scenario where a walker starts at a position A and makes decisions to walk either b steps to the right or c steps to the left with probabilities p and (1-p) respectively. The main questions are about calculating the expectation value of the walker's position after n decisions and what happens as n approaches infinity. The possibility of the walker stopping at position 0 and its impact on the problem is also discussed. The use of Markov chains and transition matrices is mentioned as a way to model the problem and find the expectation value. The conversation ends with suggestions for approaching the problem, including constructing a sparse matrix or using a series expansion based on a binomial tree.
  • #1
chrisphd
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I am interested in the following random walk scenario, where a walker starts at a defined position greater than 0, say A, and then makes a "decision" to walk to either walk "b steps to the right" or walk "c steps to the left." He will choose the first option with probability p, and the second option with probability (1-p). If the walker gets to position 0 he stops.

I wish to calculate:
- the expectation value of the walker's position after a total of n decisions.
- what happens as n approaches infinite?
 
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  • #2
How are things that you want to calculate related to the fact walker stops at 0 - seems to me like this is unrelated to the problem. But perhaps I am missing something.

Writing a program that will simulate the walker so that you can test it and find numerical solution should take an hour. Perhaps two.

At least that's what I did in the past when faced with a related problem. See http://onlinelibrary.wiley.com/doi/10.1002/elan.1140040603/abstract
 
  • #3
Borek said:
How are things that you want to calculate related to the fact walker stops at 0 - seems to me like this is unrelated to the problem. But perhaps I am missing something.

It is not unrelated to the problem at all and could have a profound impact on the problem. For example, in the special case that b=c, the condition will take care that it the end, the walker almost always ends up in 0. If the walker does not stop at 0, then the walker would end up visiting every position an infinite number of time.

Anyway, this is a classical problem with stochastic processes. For example, the situation can be modeled using a Markov chain. Do you know Markov chains?? What would be the transition matrix in this case?
 
  • #4
Not sure how I could use Markov chains because the transition matrix would have infinite dimensions.
 
  • #5
chrisphd said:
Not sure how I could use Markov chains because the transition matrix would have infinite dimensions.

I don't really see that as a problem, it just makes the calculations more difficult.
 
  • #6
Do you know the procedure for finding expectation value once I've determined a transition matrix?
 
  • #7
chrisphd said:
Do you know the procedure for finding expectation value once I've determined a transition matrix?

It's been a long time ago since I've done such a thing. I'll have a look at it and answer later, if nobody else answered.
 
  • #8
micromass said:
It is not unrelated to the problem at all and could have a profound impact on the problem. For example, in the special case that b=c, the condition will take care that it the end, the walker almost always ends up in 0. If the walker does not stop at 0, then the walker would end up visiting every position an infinite number of time.

OK. If so, for n approaching infinity, final position seems to be always zero and expected value of the displacement is -A.

Or am I wrong again?
 
  • #9
My intuition would say that it is possible for the final position to approach infinite as n approaches infinite. For example, let's say b = 100000 and c = 1, and p = 90% and say A = 100. There is a 90% chance the walker will move 100000 units further from 0, and then only a 10% chance he will move a mere 1 unit back towards 0. I think in a situation like this, after many decisions, the walker will move further and further from 0.
 
  • #10
Hey chrisphd.

You need to setup a transition matrix with an absorbing state at 0 and the other states relevant to where you are and how many steps you move.

For a finite n you can construct a matrix with the right values (it will be very sparse).

In terms of where it should end up at infinite steps, if you can show the system will always end up at the absorbent state then you have shown that the probability that you will eventually end up at 0 is 1 given enough time.

I would suggest apart from the sparse matrix approach that you try and construct a series expansion based on a binomial tree (which was suggested) and then see if you can use this finite summation in terms of n to evaluate your expression.

The summation would be based on difference equation in which you can take that and see if you can get something global for evaluating the expectation in terms of n.
 

Related to Random 1D Walk with different step sizes.

1. What is a random 1D walk with different step sizes?

A random 1D walk with different step sizes is a mathematical concept in which an object or particle takes a series of steps in a one-dimensional space, with each step having a random size or length. This concept is often used in modeling various natural phenomena, such as the movement of molecules and the fluctuations of stock prices.

2. How is the step size determined in a random 1D walk?

The step size in a random 1D walk is determined by a probability distribution. This distribution assigns a probability to each possible step size, and the step size is then randomly chosen based on these probabilities. Common distributions used for this purpose include the normal distribution and the uniform distribution.

3. Can the step sizes in a random 1D walk be correlated?

Yes, the step sizes in a random 1D walk can be correlated. This means that the size of each step is not completely independent of the previous steps, and there may be some relationship or pattern between them. For example, in a biased random walk, the step sizes are correlated because they are influenced by a certain factor or condition.

4. How does the choice of step size distribution affect the random 1D walk?

The choice of step size distribution can have a significant impact on the behavior of a random 1D walk. For instance, a normal distribution will result in a more predictable and symmetric walk, while a uniform distribution will lead to a more erratic and unpredictable walk. The choice of distribution can also affect the likelihood of the object or particle reaching a certain point or returning to its starting point.

5. What are some real-life applications of random 1D walks with different step sizes?

Random 1D walks with different step sizes have various applications in science and engineering. In physics, this concept is used to model the movement of particles in a gas or liquid, as well as the behavior of polymers. In finance, it can be used to simulate stock prices and predict market trends. In computer science, random 1D walks are used in algorithms for generating random numbers and for simulating the behavior of robots and other autonomous systems.

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