Is the Modern Theory of Stochastic Processes Complete?Example of Markovian Random Walks with

Constant Non-Symmetric Diffusion Coefficients

Kosuke Hijikata,1 Ihor Lubashevsky,2 Alexander Vazhenin 3

University of AizuIkki-machi, Aizu-Wakamatsu, Fukushima 965-8560, Japan

1)m5191113@u-aizu.ac.jp, 2)i-lubash@u-aizu.ac.jp, 3)vazhenin@u-aizu.ac.jp

ABSTRACTA new type non-symmetric diffusion problem is consideredand the corresponding Brownian motion implementing suchdiffusion processes is constructed. As a particular example,random walks with internal causality on a square lattice arestudied in detail. By construction, one elementary step of arandom walker on the lattice may consist of its two succeed-ing jumps to the nearest neighboring nodes along the x- andthen y-axis or the y- and then x-axis ordered, e.g., clock-wise. It is essential that the second fragment of elementarystep is caused by the first one, meaning that the second frag-ment can arise only if the first one has been implemented,but not vice versa. In particular, if for some reasons the sec-ond fragment is blocked, the first one may be not affected,whereas if the first fragment is blocked, the second one can-not be implemented in any case. As demonstrated, on timescales much larger then the duration of one elementary stepthese random walks are characterized by a diffusion matrixwith non-zero anti-symmetric component. The existence ofthis anti-symmetric component is also justified by numericalsimulation.

Categories and Subject DescriptorsG.3 [Probability and Statistics]: stochastic processes

General TermsTheory

KeywordsStochastic process, diffusion matrix, boundary conditions

1. INTRODUCTIONThe present paper poses a fundamental question about the

completeness of the modern formalism of describing stochas-tic processes and, by way of example, the formalism of the

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Fokker-Planck equations, or speaking more strictly, the for-ward Fokker-Planck equations is analyzed.

The Fokker-Planck equation (see, e.g., [1])

∂tG =

N∑i=1

∂i

{N∑j=1

∂j [Dij(x, t)G]− Vi(x, t)G

}(1)

subject to the initial condition

G(x, t|x0, t0)∣∣t=t0

= δ(x− x0) , (2)

where x = {xi}i=Ni=1 ∈ Q ⊂ RN and t > t0, describes a wideclass of Markovian random walks continuous in space andtime for which the first and second moments of walker dis-placement are some finite space-continuous quantities. Thematrix D = ‖Dij‖ of diffusion coefficients and the velocitydrift V = {Vi} in the “phase” space RN are introduced as

Dij(x, t) = limτ→0

1

2τ

⟨(x′i − xi)(x′j − xj)

⟩x′:(t+τ |x,t) , , (3)

Vi(x, t) = limτ→0

1

τ

⟨(x′i − xi)

⟩x′:(t+τ |x,t) . (4)

Due to the form of the Fokker-Planck equation the diffusioncoefficient matrix ‖Dij‖ must be symmetric, Dij = Dji,which follows from definition (3) as well.

Discrete random walks on lattices also admit this descrip-tion on scales t� τ , where τ is the characteristic time of thewalker hopping to the neighboring lattice nodes. An exam-ple of symmetric (i.e. without regular drift, V = 0) randomwalks on a square lattice is illustrated in Fig. 1: “diagramof transitions.” Within one elementary time step τ a walkerhops to one of the nearest lattice nodes with the probabilityp = 1

4(1− ε) or to one of the next shell of nearest neighbors

with the probability q = 14ε, here 0 < ε < 1 is a given pa-

rameter. For these random walks the diffusion matrix is ofthe diagonal form and can be characterized by one diffusioncoefficient D = (1 + ε)a2/(4τ), i.e., Dxx = Dyy = D andDxy = Dyx = 0.

Appealing to the form of the Fokker-Planck equation (1)usually one draws a conclusion that the diffusion flux J ={Ji} is related to the distribution function G via the expres-sion

Ji = −N∑j=1

∂j [Dij(x, t)G] + Vi(x, t)G . (5)

Then ascribing various physical properties to the mediumboundary ∂Q the Fokker-Planck equation is subjected to

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impe

rmea

ble

boun

dary

effect of boundaryon transitions

implementation oftransitions

diagram oftransitionsdiagram oftransitions

structure of diffusion flux:two components

+

Figure 1: The analyzed random walks on the square lattice, from left to right, the diagram showing possibletransitions of the walker within one elementary step and their probabilistic weights, spatial structure ofthese transitions, diagram and probabilistic weights of the walker near a impermeable boundary, the diagramillustrating the relationship between the diffusion flux and possible walker transitions.

impermeable boundary

Figure 2: Distribution function normalized to itsmaximum. In numerical simulation the asymme-try parameter ε = 0.2, the trajectory origin {x0 =150, y0 = 50}, and the number of steps in one trajec-tory N = 3y20 were used.

the corresponding boundary conditions (see, e.g., [1]). Thelatter completes the description of such stochastic processesin the framework of the Fokker-Planck equation. If it were sofor the random walks illustrated in Fig. 1, at a impermeableboundary (third fragment counting from left in Fig. 1) thedistribution function G would meet the boundary condition∂xG = 0 in the continuous approximation.

As far as the relationship between the Fokker-Planck equa-tion (1) and the diffusion flux (5) is concerned, we notethat the replacement Dij =⇒ Dij + Da

ij , where Daij is an

asymmetric component, Daij = −Da

ji, does not change theform of the Fokker-Planck equation (1) but modifies sub-stantially expression (5). The latter, in turn, changes theboundary condition, so, finally, contributes essentially to thedescription of stochastic process. Therefore the statementon the diffusion coefficient symmetry does not follow fromthe derivation of the Fokker-Planck equation but is an ad-dition assumption that can be accepted for some physicalreasons.

2. MODEL AND DISCUSSIONThe example of random walks shown in Fig. 1 illustrates

the stated above proposition, in particular, the diffusion co-efficient matrix Dij entering relationship (5) is of the form

containing antisymmetric component, namely,

Dxx = Dyy =(1 + ε)a2

4τ, Dxy = −Dyx = − εa

2

2τ. (6)

This fact must be reflected in the boundary conditions and,finally, cause the asymmetry of the distribution function forthe diffusion problem in the region with the impermeableboundary with respect to the boundary point nearest to theorigin of random walks. Numerical simulation justifies thisstatement (Fig. 2).

Concluding the obtained results, we pose a question aboutthe completeness of describing stochastic processes in termsof the Fokker-Planck equation or stochastic differential equa-tions. Indeed, this formalism ignores the internal struc-ture of elementary steps, whereas the given example demon-strates the fact that particular spatial details of the walkermotion within one elementary step can affect the macro-scopic behavior of diffusion processes. Diffusion in magneticfield is also discussed as a characteristic example of physicalsystems where such phenomena can be pronounced. Be-sides it should be noted that the considered problem of non-symmetric diffusion coefficient matrix is partly related to theproblem called non-symmetric diffusion dealing with a dif-fusion type stochastic processes governed by equations like

∂tG =

N∑i,j=1

∂i [Dij(x)∂jG] ,

where the diffusion coefficient Dij(x) depends on the spatialcoordinates x and, so, its possible non-symmetry can be re-sponsible for macroscopic effects (see, e.g., [2] and referencestherein).

3. REFERENCES[1] C. Gardiner, Stochastic Methods: A Handbook for the

Natural and Social Sciences (Springer Verlag, 2009),4th ed.

[2] J.-D. Deuschel, T. Kumagai, Markov ChainApproximations to Nonsymmetric Diffusions withBounded Coefficients, Communications on Pure andApplied Mathematics, 66:821–866, 2013.

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