What is the solution for a one-dimensional random walk in potentials?

In summary, the conversation discusses a problem involving a one-dimensional random walk in potentials. It starts with assumptions of a one-dimensional space with a probability distribution to go left and right at each point, and with boundaries at 0 and 1. The walker's position is captured at each time-step, and the goal is to find the steady distribution of the walker's position. The conversation goes on to mention attempts at solving this problem using a differential equation, but with some limitations. It is suggested that this can be modeled as a Markov chain, with the limit of the transition probabilities yielding the long run probabilities. Finally, it is mentioned that letting the step size tend to zero gives a continuous time Markov process. The purpose behind this model
  • #1
blue2script
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I have a problem concerning a one-dimensional random walk in potentials. Assume a one-dimensional space [0,1] and a probability distribution p(x). At every point x we have a probability p(x) to go left and 1-p(x) to go right. Assume some smooth distribution of p(x) with boundaries p(0) = 0 and p(1) = 1. Now begin a random walk at x=0.5 with some step-size dx (e.g. d = 0.01) and capture the position of the walker at every time-step t. The boundary constraint assures that the walker remains inside [0,1].

I would assume that after sufficient time steps I get a steady distribution of the position of the walker. This would be equivalent to the probability distribution of finding the walker at some point in the potential.

However, I have yet no idea how to calculate this distribution from some given p(x). I tried to set up a differential equation using the fact that in the steady case the flow from point x to x + dx and back must be zero. However, I would get a pole at x = 0.5 which is pretty useless. I can post the calculation if someone is interested.

I would be glad for every hint how one could solve this problem. Thanks in advance!
Blue2script
 
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  • #2
blue2script said:
I have a problem concerning a one-dimensional random walk in potentials. Assume a one-dimensional space [0,1] and a probability distribution p(x). At every point x we have a probability p(x) to go left and 1-p(x) to go right. Assume some smooth distribution of p(x) with boundaries p(0) = 0 and p(1) = 1. Now begin a random walk at x=0.5 with some step-size dx (e.g. d = 0.01) and capture the position of the walker at every time-step t. The boundary constraint assures that the walker remains inside [0,1].

I would assume that after sufficient time steps I get a steady distribution of the position of the walker. This would be equivalent to the probability distribution of finding the walker at some point in the potential.

However, I have yet no idea how to calculate this distribution from some given p(x). I tried to set up a differential equation using the fact that in the steady case the flow from point x to x + dx and back must be zero. However, I would get a pole at x = 0.5 which is pretty useless. I can post the calculation if someone is interested.
I assume a fixed step size ; we can let it tend to zero later.
Firstly, the boundary conditions don't warrant that the walker won't trip at 0 or 1 ( for instance, take d=1/300). If you want that constraint with initial position =1/2, the step size must be 1/2n for some integer n , making the walk discrete.
In such a case, this can be modeled as a Markov chain with 2n+1 states ( x=1/2 & other 2n possible positions ;with transition probabilities p(xn)&c. at each state. The limit of the transition probability matrix yields the long run probabilities.
Finally,letting d->0 gives a continuous time Markov process.

P.S. : Is this a model of any physicsl phenomenon? I ask out of sheer curiosity.
 

Related to What is the solution for a one-dimensional random walk in potentials?

1. What is a random walk in potential?

A random walk in potential is a mathematical concept that describes the movement of a particle or object in a potential field, where the direction of movement is determined by random effects or probabilities. It is often used to model the behavior of particles in physical systems, such as diffusion of molecules in a solution or the movement of electrons in a material.

2. How does a random walk in potential differ from a regular random walk?

In a regular random walk, the direction of movement is completely random and does not depend on any external factors. In a random walk in potential, the direction of movement is influenced by the potential field, which can be thought of as a force acting on the particle. This means that the particle is more likely to move in certain directions based on the shape of the potential field.

3. What are some real-world applications of random walk in potential?

Random walk in potential has many applications in various fields, such as physics, biology, and finance. In physics, it is used to model the movement of particles in a medium or material. In biology, it is used to study the diffusion of molecules in a solution or the movement of cells in a tissue. In finance, it is used to model the movement of stock prices.

4. How is a random walk in potential simulated or calculated?

There are various methods for simulating or calculating a random walk in potential, depending on the specific scenario. One common method is to use a Monte Carlo simulation, where random numbers are generated to determine the direction of movement at each step. Another method is to use a numerical integration technique, such as the Euler-Maruyama method, to approximate the movement of the particle over time.

5. Can a random walk in potential be used to accurately predict the behavior of a system?

While a random walk in potential can provide valuable insights and predictions about the behavior of a system, it is not always accurate. This is because it relies on probabilities and random effects, which can lead to unpredictable outcomes. Additionally, the accuracy of the predictions depends on the accuracy of the potential field and the assumptions made in the model. Therefore, it is important to carefully consider the limitations and assumptions when using a random walk in potential to make predictions about a system.

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