(log t)^(2/3) law holds for the two dimensional asymmetric simple exclusion process

You need to know: Set {\mathbb Z}^2 of points x=(x_1, x_2) with integer coefficients, probability, independent random variables, exponential distribution, covariance \text{cov}(X,Y) of random variables X and Y.

Background: At time t=0, let us put, for each x\in {\mathbb Z}^2, a particle in site x, independently and with probability 1/2. Then, for each particle p, independently generate a random variable t_p from exponential distribution, wait for time t_p, and then jump to an adjacent site up, down, or right, with probabilities 1/4, 1/4, 1/2, respectively, provided that the target site is unoccupied (and otherwise stay). This action is then repeated and performed in parallel for all particles. We call this asymmetric simple exclusion process (ASEP). For any x\in {\mathbb Z}^2, let \eta_x(t) be the number of particles (which can be 0 or 1) in position x at time t. Next define D(t) = \frac{4}{t}\sum\limits_{x \in {\mathbb Z}^2} x_1^2 \cdot \text{cov}(\eta_x(t), \eta_{(0,0)}(0)) , where x_1 is the first coordinate of x.

The Theorem: On 31st January 2002, Horng-Tzer Yau submitted to the Annals of Mathematics a paper in which he proved the existence of a constant \gamma>0 such that, for sufficiently small \lambda>0, \lambda^{-2}|\log \lambda|^{2/3}e^{-\gamma|\log\log\log \lambda|^2} \leq \int_0^{\infty}e^{-\lambda t}t D(t)dt \leq \lambda^{-2}|\log \lambda|^{2/3}e^{\gamma|\log\log\log \lambda|^2}.

Short context: ASEP is one of the simplest models for modelling diffusion process, function D(t) defined above is known as diffusion coefficient, and understanding how fast D(t) grows with t is important for estimating the speed of diffusion process. The Theorem implies that D(t) grows approximately as (\log t)^{2/3} for large t, and can therefore be called “(\log t)^{2/3} law for ASEP”.

Links: The original paper is available here. See also Section 4.3 of this book for an accessible description of the Theorem.

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