In a bottom-up approach, agent-based models have been extensively used in finance and economics in order to understand how macro-level phenomena can emerge from myriads of micro-level behaviours of individual agents. Moreover, in the absence of (big) data there is still the need to test economic theories and understand how macro-level laws can be materialized as the aggregate of a multitude of interactions of discrete agents. We exemplify how we can solve this problem in a particular instance: We introduce an agent-based method in order to generate data with Monte Carlo and then we interpolate the data with machine learning methods in order to derive multi-parametric demand functions. In particular, the model we construct is implemented in a simulated economy with 1000 consumers and two products, where each consumer is characterized by a unique set of preferences and available income. The demand for each product is determined by a stochastic process, incorporating the uncertainty in consumer preferences. By interpolating the data for the demands for various scenarios and types of consumers we derive poly-parametric demand functions. These demand functions are partially in tension with classical demand theory since on certain occasions they imply that the demand of a product increases as its price increases. Our proposed method of generating data from discrete agents with Monte Carlo and of interpolating the data with machine learning methods can be easily generalized and applied to the assessment of economic theories and to the derivation of economic laws in a bottom-up approach.
Citation: Georgios Alkis Tsiatsios, John Leventides, Evangelos Melas, Costas Poulios. A bounded rational agent-based model of consumer choice[J]. Data Science in Finance and Economics, 2023, 3(3): 305-323. doi: 10.3934/DSFE.2023018
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In a bottom-up approach, agent-based models have been extensively used in finance and economics in order to understand how macro-level phenomena can emerge from myriads of micro-level behaviours of individual agents. Moreover, in the absence of (big) data there is still the need to test economic theories and understand how macro-level laws can be materialized as the aggregate of a multitude of interactions of discrete agents. We exemplify how we can solve this problem in a particular instance: We introduce an agent-based method in order to generate data with Monte Carlo and then we interpolate the data with machine learning methods in order to derive multi-parametric demand functions. In particular, the model we construct is implemented in a simulated economy with 1000 consumers and two products, where each consumer is characterized by a unique set of preferences and available income. The demand for each product is determined by a stochastic process, incorporating the uncertainty in consumer preferences. By interpolating the data for the demands for various scenarios and types of consumers we derive poly-parametric demand functions. These demand functions are partially in tension with classical demand theory since on certain occasions they imply that the demand of a product increases as its price increases. Our proposed method of generating data from discrete agents with Monte Carlo and of interpolating the data with machine learning methods can be easily generalized and applied to the assessment of economic theories and to the derivation of economic laws in a bottom-up approach.
The Forchheimer model equation describes the flow in a polar medium, which is widely used in fluid mechanics (see [1,2]). Increasing scholars have studied the spatial properties of the solution to the fluid equation defined on a semi-infinite cylinder, and a large number of results have emerged (see [3,4,5,6,7,8,9]).
In 2002, Payne and Song [3] have studied the following Forchheimer model
b|u|ui+(1+γT)ui=−p,i+giT, in Ω×{t>0}, | (1.1) |
ui,i=0, in Ω×{t>0}, | (1.2) |
∂tT+uiT,i=ΔT, in Ω×{t>0}, | (1.3) |
where i=1,2,3. ui,p,T represent the velocity, pressure, and temperature of the flow, respectively. gi is a known function. Δ is the Laplace operator, γ>0 is a constant, and b is the Forchheimer coefficient. For simplicity, we assume that
gigi≤1. |
In (1.1)–(1.3), Ω is defined as
Ω={(x1,x2,x3)|(x1,x2)∈D, x3≥0}, |
where D is a bounded simply-connected region on (x1,x2)-plane.
In this paper, the comma is used to indicate partial differentiation and the usual summation convection is employed, with repeated Latin subscripts summed from 1 to 3, e.g., ui,jui,j=∑3i,j=1(∂ui∂xj)2. We also use the summation convention summed from 1 to 2, e.g., uα,βuα,β=∑2α,β=1(∂uα∂xβ)2.
The Eqs (1.1)–(1.3) also satisfy the following initial-boundary conditions
ui(x1,x2,x3,t)=0, T(x1,x2,x3,t)=0, on ∂D×{x3>0}×{t>0}, | (1.4) |
ui(x1,x2,0,t)=fi(x1,x2,t), on D×{t>0}, | (1.5) |
T(x1,x2,0,t)=H(x1,x2,t), on D×{t>0}, | (1.6) |
T(x1,x2,x3,0)=0, (x1,x2,x3)∈Ω, | (1.7) |
|u|,|T|=O(1), |u3|,|∇T|,|p|=o(x−13), as x3→∞. | (1.8) |
where fi and H are differentiable functions.
In this paper, we will study the structural stability of Eqs (1.1)–(1.8) on Ω by using the spatial decay results obtained in [3]. Since the concept of structural stability was proposed by Hirsch and Smale [10], the structural stability of various types of partial differential equations defined in a bounded domain has received sufficient attention(see [11,12,13,14,15,16,17,18,19]). Some perturbations are inevitable in the process of model establishment and simplification, so it is necessary to study that whether such small perturbations of the equations themselves will cause great changes in the solutions. This gives rise to the phenomenon of structural stability.
If the bounded domain is replaced by a semi-infinite pipe, the structural stability of the partial differential equations is very interesting and has begun to attract attention. Li and Lin [20] considered the continuous dependence on the Forchheimer coefficient of Forchheimer equations in a semi-infinite pipe. Different from the studies of[11,12,13,14,15,16,17,18,19], we should consider not only the time variable but also the space variable. Therefore, the methods in the literature cannot be directly applied to the semi-infinite region. Compared with [3], we not only reconfirmed the spatial decay result of [3], but also proved the structural stability of the solution to b and γ.
We also introduce the notations:
Ωz={(x1,x2,x3)|(x1,x2)∈D,x3≥z≥0}, |
Dz={(x1,x2,x3)|(x1,x2)∈D,x3=z≥0}, |
where z is a running variable along the x3 axis.
First, to obtain the main result, we shall make frequent use of the following three inequalities.
Lemma 2.1.(see[21]) If ϕ is a Dirichlet integrable function on Ω and ∫Ωϕdx=0, then there exists a Dirichlet integrable function w=(w1,w2,w3) such that
wi,i=ϕ, in Ω, wi=0, on ∂Ω, |
and a positive constant k1 depends only on the geometry of Ω such that
∫Ωwi,jwi,jdx≤k1∫Ω(wi,i)2dx. |
Lemma 2.2.(see [3,4]) If ϕ|∂D=0, then
λ∫Dϕ2dA≤∫Dϕ,αϕ,αdA, |
where λ is the smallest positive eigenvalue of
Δ2ϑ+λϑ=0, in D, ϑ=0, on ∂D. |
Here Δ2 is a two-dimensional Laplace operator.
Now, we give a lemma which has been proved by Horgan and Wheeler [4] and has been used by Payne and Song [6].
Lemma 2.3.(see [3,4]) If ϕ is a Dirichlet integrable function and ϕ|∂D=0,ϕ→∞ (as x3→∞),
∫Ωz|ϕ|4dx≤k2(∫Ωzϕ,jϕ,jdx)2, |
where k2>0.
Lemma 2.4. If ϕ∈C10(Ω), then
∫Ωz|ϕ|6dx≤Λ(∫Ωzϕ,iϕ,idx)3, |
where [22,23] have proved that the optimal value of Λ is determined to be Λ=127(34)4.
Using the maximum principle for the temperature T, we can have the following lemma which has been used in Song [5].
Lemma 2.5. Assume that H∈L∞(Ω), then
supΩ×{t>0}|T|≤TM, |
where TM=supΩ×{t>0}H.
Second, we list some useful results which have been derived by Payne and Song [3].
Payne and Song have established a function
P(z,t)=∫t0∫Ωz(ξ−z)T,iT,idxdη+a1∫t0∫Ωz|u|3dxdη+a2∫t0∫Ωz(1+γT)|u|2dxdη, | (2.1) |
where a1 and a2 are positive constants. From Eqs (3.27) and (3.36) of [3], we know that
P(z,t)≤P(0,t)e−zk3, P(0,t)≤k4(t), | (2.2) |
where k3 is a positive constant and k4(t) is a function related to the boundary values.
Combining Eqs (2.1) and (2.2), we have the following lemma.
Lemma 2.6. Assume that H∈L∞(Ω) and ∫DfdA=0, then
a1∫t0∫Ωz|u|3dxdη+a2∫t0∫Ωz(1+γT)|u|2dxdη≤k4(t)e−zk3. |
In order to derive the main result, we need bounds for ||u||2L2(Ω) and ||u||3L2(Ω).
Lemma 2.7. Assume that fi∈H1(Ω),H,˜H∈L∞(Ω), ∫Df3dA=0 and fα,α−γf3=0 then
b∫Ω|u|3dx+∫Ω|u|2dx≤k5(t), |
where k6(t) is a positive function.
Proof. To deal with boundary terms, we set S=(S1,S2,S3), where
Si=fie−γ1x3, γ1>0. | (2.3) |
Using Eq (1.1), we have
∫Ω[b|u|ui+(1+γT)ui+p,i−giT](ui−Si)dx=0. |
Using the divergence theorem, we have
b∫Ω|u|3dx+∫Ω(1+γT)|u|2dx=b∫Ω|u|uiSidx+∫Ω(1+γT)uiSidx−∫ΩgiTuidx+∫ΩgiTSidx. | (2.4) |
Using the Hölder inequality and Young's inequality, we have
b∫Ω|u|uiSidx≤b(∫Ω|u|3dx)23(∫Ω|S|3dx)13≤23bε1∫Ω|u|3dx+13bε−21∫Ω|S|3dx, | (2.5) |
∫Ω(1+γT)uiSidx≤14∫Ω(1+γT)|u|2dx+(1+γTM)∫Ω|S|2dx, | (2.6) |
−∫ΩgiTuidx≤√TM(∫Ω(1+γT)|u|2dx∫Ωgigidx)12≤14∫Ω(1+γT)|u|2dx+TMγ∫Ωgigidx, | (2.7) |
∫ΩgiTSidx≤TM∫Ω|giSi|dx. | (2.8) |
Inserting Eqs (2.5)–(2.8) into Eq (2.4) and choosing that ε1=34, we obtain
b∫Ω|u|3dx+∫Ω(1+γT)|u|2dx≤23bε−21∫Ω|S|3dx+2(1+γTM)∫Ω|S|2dx+2TMγ∫Ωgigidx+2TM∫Ω|giSi|dx. | (2.9) |
After choosing
k5(t)=23bε−21∫Ω|S|3dx+2(1+γTM)∫Ω|S|2dx+2TMγ∫Ωgigidx+2TM∫Ω|giSi|dx, | (2.10) |
we can complete the proof of Lemma 2.7.
In this section, we derive an important lemma which leads to our main result.
Assume that (u∗i,T∗,p∗) is a solution of Eqs (1.1)–(1.8) when b=b∗. If we let
Di=ui−u∗i, Σ=T−T∗, π=p−p∗, ˜b=b−b∗, |
then (Di,Σ,π) satisfies
[b1|u|ui−b2|u∗|u∗i]+(1+γT)Di+γΣu∗i=−π,i+giΣ, in Ω×{t>0}, | (3.1) |
Di,i=0, in Ω×{t>0}, | (3.2) |
∂tΣ+uiΣ,i+DiT∗,i=ΔΣ, in Ω×{t>0}, | (3.3) |
Di=0,Σ=0, on ∂D×{x3>0}×{t>0}, | (3.4) |
Di=0,Σ=0, on D×{t>0}, | (3.5) |
Σ(x1,x2,x3,0)=0, in Ω | (3.6) |
|u|,|Σ|=O(1),|D3|,|∇Σ|,|π|=o(x−13), as x3→∞. | (3.7) |
We can have the following lemma.
Lemma 3.1. Assume that (Di,Σ,π) is a solution to Eqs (3.1)–(3.6) with ∫Df3dA=0,H∈L∞(Ω) and the boundary data (e.g., H) satisfies Eq (3.21), then
Φ(z,t)≤n∗6[−∂∂zΦ(z,t)]+n7(t)˜b2e−zk3, |
where n∗6 is the maximum of n6(t) and n6(t),n7(t) will be defined in Eq (3.39).
Proof. We define an auxiliary function
Φ1(z,t)=∫t0∫Ωze−ωηπD3dxdη, | (3.8) |
where ω>0.
Using the divergence theorem and Eq (3.1), we have
Φ1(z,t)=−∫t0∫Ωze−ωη(ξ−z)π,iDidxdη=∫t0∫Ωze−ωη(ξ−z)Di[b1|u|ui−b2|u∗|u∗i]dxdη+∫t0∫Ωze−ωη(ξ−z)(1+γT)DiDidxdη+γ∫t0∫Ωze−ωη(ξ−z)DiΣu∗idxdη−∫t0∫Ωze−ωη(ξ−z)DigiΣdxdη. | (3.9) |
Since
Di[b1|u|ui−b2|u∗|u∗i]=˜b2Di[|u|ui+|u∗|u∗i]+b1+b22Di[|u|ui−|u∗|u∗i]=˜b2[|u|ui+|u∗|u∗i]Di+b1+b24[|u|+|u∗|]DiDi+b1+b24[|u|−|u∗|]2[|u|+|u∗|], |
from Eq (3.9) we have
Φ1(z,t)=b1+b24∫t0∫Ωze−ωη(ξ−z)[|u|+|u∗|]DiDidxdη+∫t0∫Ωze−ωη(ξ−z)(1+γT)DiDidxdη+˜b2∫t0∫Ωze−ωη(ξ−z)[|u|ui+|u∗|u∗i]Didxdη+b1+b24∫t0∫Ωze−ωη(ξ−z)[|u|−|u∗|]2[|u|+|u∗|]dxdη+γ∫t0∫Ωze−ωη(ξ−z)DiΣu∗idxdη−∫t0∫Ωze−ωη(ξ−z)DigiΣdxdη. | (3.10) |
Using the Hölder inequality, Young's inequality and Lemma 2.6, we obtain
˜b2∫t0∫Ωze−ωη[|u|ui+|u∗|u∗i]Didxdη≥−˜b2(∫t0∫Ωze−ωη|u|DiDidxdη)12(∫t0∫Ωze−ωη|u|3dxdη)12+−˜b2(∫t0∫Ωze−ωη|u∗|DiDidxdη)12(∫t0∫Ωze−ωη|u|3dxdη)12≥−b1+b216∫t0∫Ωze−ωη[|u|+|u∗|]DiDidxdη−4˜b2b1+b2∫t0∫Ωze−ωη[|u|3+|u∗|3]dxdη≥−b1+b216∫t0∫Ωze−ωη[|u|+|u∗|]DiDidxdη−8˜b2a1(b1+b2)k4(t)e−zk3. | (3.11) |
Using the Hölder inequality, Young's inequality and Lemmas 2.3 and 2.7, we obtain
γ∫t0∫Ωze−ωηDiΣu∗idxdη≥−γ∫t0e−ωη(∫Ωz|u∗|DiDidx)12(∫Ωz|u∗|2dx)14(∫ΩzΣ4dx)14dη≥−γ4√k5(t)k2∫t0e−ωη(∫Ωz|u∗|DiDidx)12(∫ΩzΣ,iΣ,idx)12dη≥−b1+b216∫t0∫Ωze−ωη|u∗|DiDidxdη−4γ2√k5(t)k2b1+b2∫t0∫Ωze−ωηΣ,iΣ,idxdη, | (3.12) |
−∫t0∫Ωze−ωηDigiΣdxdη≥−12∫t0∫Ωze−ωη(1+γT)DiDidxdη−12γ∫t0∫Ωze−ωηΣ2dxdη. | (3.13) |
Calculating the differential of Eq (3.10) and then inserting Eqs (3.11)–(3.13) into Eq (3.10), we have
−∂∂zΦ1(z,t)≥b1+b28∫t0∫Ωze−ωη[|u|+|u∗|]DiDidxdη+12∫t0∫Ωze−ωη(1+γT)DiDidxdη−4γ2√k5(t)k2b1+b2∫t0∫Ωze−ωηΣ,iΣ,idxdη−12γ∫t0∫Ωze−ωηΣ2dxdη−8˜b2a1(b1+b2)k4(t)e−zk3, | (3.14) |
where we have dropped the fourth term of Eq (3.10).
Similarly, we have
Φ2(z,t)=−∫t0∫Ωze−ωηΣΣ,3dxdη+12∫t0∫Ωze−ωηu3Σ2dxdη+∫t0∫Ωze−ωηD3T∗Σdxdη≐Φ21(z,t)+Φ22(z,t)+Φ23(z,t). | (3.15) |
Using the divergence theorem and Eq (3.2), we have
Φ2(z,t)=12e−ωt∫Ωz(ξ−z)Σ2dx+∫t0∫Ωze−ωη(ξ−z)[12ωΣ2+Σ,iΣ,i]dxdη−∫t0∫Ωze−ωη(ξ−z)DiΣ,iT∗dxdη. | (3.16) |
Using the Hölder inequality and Lemma 2.5, we have
−∫t0∫Ωze−ωηDiΣ,iT∗dxdη≥−12∫t0∫Ωze−ωηΣ,iΣ,idxdη−12T2M∫t0∫Ωze−ωηDiDidxdη. | (3.17) |
Calculating the differential of Eq (3.16) and then inserting Eq (3.17) into Eq (3.16), we have
−∂∂zΦ2(z,t)=12e−ωt∫ΩzΣ2dx+∫t0∫Ωze−ωη[12ωΣ2+12Σ,iΣ,i]dxdη−12γT2M∫t0∫Ωze−ωη(1+γT)DiDidxdη. | (3.18) |
Now, we define
−∂∂zΦ(z,t)=2γT2M[−∂∂zΦ1(z,t)]+[−∂∂zΦ2(z,t)]. | (3.19) |
Combining Eqs (3.14) and (3.18), we have
−∂∂zΦ(z,t)≥b1+b24γT2M∫t0∫Ωze−ωη[|u|+|u∗|]DiDidxdη+12γT2M∫t0∫Ωze−ωη(1+γT)DiDidxdη+12e−ωt∫ΩzΣ2dx+∫t0∫Ωze−ωη[12ωΣ2+12Σ,iΣ,i]dxdη−8γ√k5(t)k2b1+b2∫t0∫Ωze−ωηΣ,iΣ,idxdη−1γ2T2M∫t0∫Ωze−ωηΣ2dxdη−16˜b2a1γ(b1+b2)T2Mk4(t)e−zk3. | (3.20) |
Choosing ω>4γ2T2M and the boundary data (e.g., H) satisfies
8γ√k5(t)k2b1+b2<14, | (3.21) |
from Eq (3.20) we obtain
−∂∂zΦ(z,t)≥b1+b24γT2M∫t0∫Ωze−ωη[|u|+|u∗|]DiDidxdη+12γT2M∫t0∫Ωze−ωη(1+γT)DiDidxdη+12e−ωt∫ΩzΣ2dx+∫t0∫Ωze−ωη[14ωΣ2+14Σ,iΣ,i]dxdη−16˜b2a1γ(b1+b2)T2Mk4(t)e−zk3. | (3.22) |
Integrating Eq (3.22) from z to ∞, we obtain
Φ(z,t)≥b1+b24γT2M∫t0∫Ωze−ωη(ξ−z)[|u|+|u∗|]DiDidxdη+12γT2M∫t0∫Ωze−ωη(ξ−z)(1+γT)DiDidxdη+12e−ωt∫Ωz(ξ−z)Σ2dx+∫t0∫Ωze−ωη(ξ−z)[14ωΣ2+14Σ,iΣ,i]dxdη−16˜b2a1γ(b1+b2)k3T2Mk4(t)e−zk3. | (3.23) |
We note that
∫DzD3dA=∫DD3dA+∫z0∫Dξ∂D3∂x3dAdξ=−∫z0∫DξDα,αdAdξ=0. |
According to Lemma 2.1, there exists a vector function w=(w1,w2,w3) such that
wi,i=D3, in Ω;wi=0, on ∂Ω. |
Therefore, using Eq (3.1) we obtain
2γT2MΦ1(z,t)=2γT2M∫t0∫Ωze−ωηπwi,idxdη=−2γT2M∫t0∫Ωze−ωηπ,iwidxdη=2γT2M∫t0∫Ωze−ωη{[b1|u|ui−b2|u∗|u∗i]+(1+γT)Di+γΣu∗i−giΣ}widxdη. | (3.24) |
Since
[b1|u|ui−b2|u∗|u∗i]wi=˜b2[|u|ui+|u∗|u∗i]wi+b1+b22[|u|+|u∗|]Diwi+b1+b22[|u|−|u∗|](ui+u∗i)wi=˜b2[|u|ui+|u∗|u∗i]wi+b1+b22[|u|+|u∗|]Diwi+b1+b22(uj−u∗j)(uj+u∗j)|u|+|u∗|(ui+u∗i)wi≤˜b2[|u|ui+|u∗|u∗i]wi+b1+b22[|u|+|u∗|]Diwi+b1+b22[|u|+|u∗|]|D||w|, |
we have
∫t0∫Ωze−ωη[b1|u|ui−b2|u∗|u∗i]widxdη≤˜b2∫t0∫Ωze−ωη[|u|ui+|u∗|u∗i]widxdη+b1+b22∫t0∫Ωze−ωη[|u|+|u∗|]Diwidxdη+b1+b22∫t0∫Ωze−ωη[|u|+|u∗|]|D||w|dxdη. | (3.25) |
Using the Hölder inequality, Lemmas 2.2, 2.3, 2.1, 2.7 and 2.6, and Young's inequality, we obtain
˜b2∫t0∫Ωze−ωη[|u|ui+|u∗|u∗i]widxdη≤˜b2∫t0e−ωη[∫Ωz[|u|3+|u∗|3]dx]23[∫Ωz(wiwi)32dx]13dη≤˜b2∫t0e−ωη[∫Ωz[|u|3+|u∗|3]dx]23[∫Ωzwiwidx]16⋅[∫Ωz(wiwi)2dx]16dη≤˜b6√k226√λ∫t0e−ωη[∫Ωz[|u|3+|u∗|3]dx]23[∫Ωzwi,αwi,αdx]16⋅[∫Ωzwi,jwi,jdx]13dη≤˜b6√k226√λ∫t0e−ωη[∫Ωz[|u|3+|u∗|3]dx]23[∫Ωzwi,jwi,jdx]12dη≤˜b6√k2√k126√λ∫t0e−ωη[∫Ω[|u|3+|u∗|3]dx]16⋅[∫Ωz[|u|3+|u∗|3]dx]12[∫ΩzD23dx]12dη≤˜b23√2k5(t)k2k143√bλ∫t0∫Ωze−ωη[|u|3+|u∗|3]dxdη+12∫t0∫Ωze−ωηD23dxdη≤˜b23√2k5(t)k2k1k4(t)2a3√bλe−zk3+12∫t0∫Ωze−ωη(1+γT)D23dxdη. | (3.26) |
Using the Hölder inequality, Lemmas 2.3, 2.1 and 2.7, and Young's inequality, we obtain
b1+b22∫t0∫Ωze−ωη[|u|+|u∗|]Diwidxdη≤b1+b22∫t0e−ωη[∫Ωz|u|DiDidx]12[∫Ωz|u|2dx]14[∫Ωz(wiwi)2dx]14dη+b1+b22∫t0e−ωη[∫Ωz|u∗|DiDidx]12[∫Ωz|u∗|2dx]14[∫Ωz(wiwi)2dx]14dη≤b1+b224√k2k5(t)∫t0e−ωη[∫Ωz|u|DiDidx]12[∫Ωzwi,jwi,jdx]12dη+b1+b224√k2k5(t)∫t0e−ωη[∫Ωz|u∗|DiDidx]12[∫Ωzwi,jwi,jdx]12dη≤b1+b244√k1k2k5(t)∫t0∫Ωze−ωη[|u|+|u∗|]DiDidxdη+b1+b224√k1k2k5(t)∫t0∫Ωze−ωη(1+γT)D23dxdη. | (3.27) |
Using the Hölder inequality, Lemmas 2.4, 2.1 and 2.7, and Young's inequality, we obtain
b1+b22∫t0∫Ωze−ωη[|u|+|u∗|]|D||w|dxdη≤b1+b22∫t0e−ωη[∫Ωz|DiDidx]12[∫Ωz|u|3dx]13[∫Ωz(wiwi)3dx]16dη+b1+b22∫t0e−ωη[∫Ωz|DiDidx]12[∫Ωz|u∗|3dx]13[∫Ωz(wiwi)3dx]16dη≤(b1+b2)3√k5(t)b6√Λ∫t0e−ωη[∫ΩzDiDidx]12[∫Ωzwi,jwi,jdx]12dη≤b1+b223√k1k5(t)b6√Λ∫t0∫Ωze−ωη(1+γT)DiDidxdη. | (3.28) |
Inserting Eqs (3.26)–(3.28) into Eq (3.25), we have
\begin{align} \int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}&\Big[b_1|\mathit{\boldsymbol{u}}|u_i-b_2|\mathit{\boldsymbol{u}}^*|u_i^*\Big]w_{i}dx d\eta \leq \frac{\widetilde{b}^2\sqrt[3]{2k_5(t)k_2}k_1k_4(t)}{2a\sqrt[3]{b\lambda}}e^{-\frac{z}{k_3}} \\ &+\frac{b_1+b_2}{4}\sqrt[4]{k_2k_5(t)}\varepsilon_3\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}\Big[|\mathit{\boldsymbol{u}}| +|\mathit{\boldsymbol{u}}^*|\Big]\mathcal{D}_i\mathcal{D}_i dx d\eta \\ &+\Big[\frac{b_1+b_2}{2}\sqrt[3]{\frac{k_1k_5(t)}{b}}\sqrt[6]{\Lambda}+\frac{b_1+b_2}{2}\sqrt[4]{k_2k_5(t)}+\frac{1}{2}\Big] \\ &\cdot\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta} (1+\gamma T)\mathcal{D}_i\mathcal{D}_i dxd\eta. \end{align} | (3.29) |
Using the Hölder inequality, Young's inequality and Lemmas 2.5, 2.2, 2.1, 2.7 and 2.3, we have
\begin{align}& \int_0^t\int_{\Omega_z}e^{-\omega\eta}(1+\gamma T)\mathcal{D}_iw_idx d\eta \\&\leq(1+\gamma T_M)\Big[\int_0^t\int_{\Omega_z}e^{-\omega\eta}(1+\gamma T)\mathcal{D}_i\mathcal{D}_idx d\eta \int_0^t\int_{\Omega_z}e^{-\omega\eta}w_iw_idx d\eta\Big]^\frac{1}{2} \\ &\leq\frac{(1+\gamma T_M)}{\sqrt{\lambda}}\Big[\int_0^t\int_{\Omega_z}e^{-\omega\eta}(1+\gamma T)\mathcal{D}_i\mathcal{D}_idx d\eta \int_0^t\int_{\Omega_z}e^{-\omega\eta}w_{i,\alpha}w_{i,\alpha}dx d\eta\Big]^\frac{1}{2} \end{align} |
\begin{align} &\leq\frac{(1+\gamma T_M)\sqrt{k_1}}{\sqrt{\lambda}}\Big[\int_0^t\int_{\Omega_z}e^{-\omega\eta}(1+\gamma T)\mathcal{D}_i\mathcal{D}_idx d\eta \int_0^t\int_{\Omega_z}e^{-\omega\eta}\mathcal{D}_{3}^2dx d\eta\Big]^\frac{1}{2} \\ &\leq\frac{(1+\gamma T_M)\sqrt{k_1}}{\sqrt{\lambda}}\int_0^t\int_{\Omega_z}e^{-\omega\eta}(1+\gamma T)\mathcal{D}_i\mathcal{D}_idx d\eta, \end{align} | (3.30) |
\begin{align} & \gamma\int_0^t\int_{\Omega_z}e^{-\omega\eta}w_i\Sigma u_i^*dx d\eta \\&\leq\gamma\int_0^t e^{-\omega\eta} \Big(\int_{\Omega_z}(u_3^*)^2dx\Big)^\frac{1}{2}\Big(\int_{\Omega_z}\Sigma^4dx\Big)^\frac{1}{4}\Big(\int_{\Omega_z}(w_iw_i)^2dx\Big)^\frac{1}{4} d\eta \\ &\leq\gamma\sqrt{k_5(t)k_2}\int_0^te^{-\omega\eta} \Big(\int_{\Omega_z}\Sigma_{,i}\Sigma_{,i}dx\Big)^\frac{1}{2}\Big(\int_{\Omega_z}w_{i,j}w_{i,j}dx\Big)^\frac{1}{2} d\eta \\ &\leq\gamma\sqrt{k_5(t)k_2k_1}\int_0^te^{-\omega\eta} \Big(\int_{\Omega_z}\Sigma_{,i}\Sigma_{,i}dx\Big)^\frac{1}{2}\Big(\int_{\Omega_z}\mathcal{D}_{3}^2dx\Big)^\frac{1}{2} d\eta \\ &\leq \frac{\sqrt{\gamma k_5(t)k_2k_1}}{T_M}\Big[\frac{1}{4}\int_0^t\int_{\Omega_z}e^{-\omega\eta}e^{-\omega\eta}\Sigma_{,i}\Sigma_{,i}dxd\eta \\ &+\frac{1}{\gamma}T_M^2 \int_0^t\int_{\Omega_z}e^{-\omega\eta}(1+\gamma T)\mathcal{D}_{3}^2dxd\eta\Big], \end{align} | (3.31) |
\begin{align} \int_0^t\int_{\Omega_z}e^{-\omega\eta}w_i\Sigma g_idx d\eta &\leq \sqrt{\frac{k_1\gamma}{T_M\omega}}\Big[\frac{1}{\gamma}T_M^2 \int_0^t\int_{\Omega_z}e^{-\omega\eta}(1+\gamma T)\mathcal{D}_{3}^2dxd\eta \\ &+ \frac{1}{4}\omega\int_0^t\int_{\Omega_z}e^{-\omega\eta}\Sigma^2dx d\eta\Big] . \end{align} | (3.32) |
Inserting Eqs (3.29)–(3.32) into Eq (3.24), we obtain
\begin{align} \frac{2}{\gamma}T_M^2\Phi_{1}(z, t)&\leq n_1(t)\widetilde{b}^2e^{-\frac{z}{k_3}} +n_2(t)\cdot\frac{(b_1+b_2)T_M^2}{4\gamma}\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}\Big[|\mathit{\boldsymbol{u}}| +|\mathit{\boldsymbol{u}}^*|\Big]\mathcal{D}_i\mathcal{D}_i dx d\eta \\ &+n_3(t)\cdot\frac{T_M^2}{2\gamma}\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta} (1+\gamma T)\mathcal{D}_i\mathcal{D}_i dxd\eta \\ &+n_4(t)\cdot\frac{1}{4}\int_0^t\int_{\Omega_z}e^{-\omega\eta}e^{-\omega\eta}\Sigma_{,i}\Sigma_{,i}dxd\eta +n_5(t)\cdot \frac{1}{4}\omega\int_0^t\int_{\Omega_z}e^{-\omega\eta}\Sigma^2dx d\eta. \end{align} | (3.33) |
where
\begin{align} n_1(t)& = \frac{2}{\gamma}T_M^2\frac{\sqrt[3]{2k_5(t)k_2}k_1k_4(t)}{2a\sqrt[3]{b\lambda}}, n_2(t) = 2\sqrt[4]{k_2k_5(t)}, \\ n_3(t)& = 2\Big[\frac{b_1+b_2}{2}\sqrt[3]{\frac{k_1k_5(t)}{b}}\sqrt[6]{\Lambda}+\frac{b_1+b_2}{2}\sqrt[4]{k_2k_5(t)}+\frac{1}{2}\Big] \\ &+2\frac{(1+\gamma T_M)\sqrt{k_1}}{\sqrt{\lambda}}+\frac{2\sqrt{\gamma k_5(t)k_2k_1}T_M}{\gamma}+\frac{1}{\gamma}T_M^2\sqrt{\frac{k_1\gamma}{T_M\omega}}, \\ n_4(t)& = \frac{2\sqrt{\gamma k_5(t)k_2k_1}T_M}{\gamma}, n_5(t) = \frac{1}{\gamma}T_M^2\sqrt{\frac{k_1\gamma}{T_M\omega}}. \end{align} |
Now, we begin to derive a bound of \Phi_2(z, t) which has been defined in Eq (3.15). Using the Hölder inequality, Young's inequality, Lemmas 2.7 and 2.3, we have
\begin{align} \Phi_{21}(z, t)&\leq\frac{1}{\sqrt{\omega}}\Big[\frac{1}{4}\int_0^t\int_{\Omega_z}e^{-\omega\eta}\Sigma_{,3}^2dx d\eta +\frac{1}{4}\omega\int_0^t\int_{\Omega_z}e^{-\omega\eta}\Sigma^2dx d\eta\Big], \end{align} | (3.34) |
\begin{align} \Phi_{22}(z, t)&\leq\frac{1}{2}\int_0^t e^{-\omega\eta} \Big(\int_{\Omega_z}u_3^2dx\Big)^\frac{1}{2}\Big(\int_{\Omega_z}\Sigma^4dx\Big)^\frac{1}{2} d\eta \\ &\leq\sqrt{2k_5(t)k_2}\cdot\frac{1}{4}\int_0^t \int_{\Omega_z}e^{-\omega\eta}\Sigma_{,i}\Sigma_{,i}dx d\eta, \end{align} | (3.35) |
\begin{align} \Phi_{23}(z, t)&\leq\sqrt{\frac{2\gamma}{\omega}}\Big[\frac{1}{2\gamma}T_M^2\int_0^t\int_{\Omega_z} e^{-\omega\eta}(1+\gamma T)\mathcal{D}_3^2dxd\eta+ \frac{1}{4}\omega\int_0^t\int_{\Omega_z}e^{-\omega\eta}\Sigma^2dx d\eta\Big]. \end{align} | (3.36) |
Inserting Eqs (3.34)–(3.36) into Eq (3.15), we obtain
\begin{align} \Phi_2(z, t)&\leq\Big[\frac{1}{\sqrt{\omega}}+\sqrt{2k_5(t)k_2} \Big]\cdot\frac{1}{4}\int_0^t \int_{\Omega_z}e^{-\omega\eta}\Sigma_{,i}\Sigma_{,i}dx d\eta \\ &+\Big[\frac{1}{\sqrt{\omega}}+\sqrt{\frac{2\gamma}{\omega}}\Big]\cdot\frac{1}{4}\omega\int_0^t\int_{\Omega_z}e^{-\omega\eta}\Sigma^2dx d\eta \\ &+\sqrt{\frac{2\gamma}{\omega}}\cdot\frac{1}{2\gamma}T_M^2\int_0^t\int_{\Omega_z} e^{-\omega\eta}(1+\gamma T)\mathcal{D}_3^2dxd\eta. \end{align} | (3.37) |
Combining Eqs (3.19), (3.22), (3.33) and (3.37), we obtain
\begin{align} \Phi(z, t)&\leq n_6(t)\Big[-\frac{\partial}{\partial z}\Phi(z, t)\Big]+n_7(t)\widetilde{b}^2e^{-\frac{z}{k_3}}, \end{align} | (3.38) |
where
\begin{align} n_6(t)& = \max\Big\{n_2(t), n_3(t)+\sqrt{\frac{2\gamma}{\omega}}, n_5(t)+\frac{1}{\sqrt{\omega}}+\sqrt{\frac{2\gamma}{\omega}}, n_4(t)+\frac{1}{\sqrt{\omega}}+\sqrt{2k_5(t)k_2}\Big\}, \\ n_7(t)& = \frac{16}{a_1\gamma(b_1+b_2)}T_M^2k_4(t)n_6(t)+n_1(t). \end{align} | (3.39) |
In this section, we will analysis Lemma 3.1 to derive the following theorem.
Theorem 4.1. Let (u_i, T, p) and (u_i^*, T^*, p^*) be solutions of the Eqs (1.1)–(1.8) in \Omega , corresponding to b_1 and b_2 , respectively. If \int_Df_3dA = 0 , Equation (3.21) holds and f_{\alpha, \alpha}-\gamma_1f_3 = 0, H\in L^\infty(\Omega\times\{t > 0\}) , then
(u_i, T)\rightarrow (u_i^*, T^*),\ as\ b_1\rightarrow b_2. |
Specifically, either the inequality
\begin{align} \frac{b_1+b_2}{4\gamma}T_M^2&\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)\Big[|\mathit{\boldsymbol{u}}|+|\mathit{\boldsymbol{u}}^*|\Big]\mathcal{D}_i\mathcal{D}_idx d\eta \\ &+ \frac{1}{2\gamma}T_M^2\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)(1+\gamma T)\mathcal{D}_i\mathcal{D}_idx d\eta \\ &+\frac{1}{2}e^{-\omega t}\int_{\Omega_z}(\xi-z)\Sigma^2dx+\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)\Big[ \frac{1}{4}\omega\Sigma^2+\frac{1}{4}\Sigma_{,i}\Sigma_{,i}\Big]dxd\eta \\ &\leq \frac{16\widetilde{b}^2}{a_1\gamma(b_1+b_2)}k_3T_M^2k_4(t)e^{-\frac{z}{k_3}}+ \widetilde{b}^2n_7(t)e^{-\frac{1}{n_6^*}z} +\widetilde{b}^2\frac{n_7(t)}{n_6^*}ze^{-\frac{1}{n_6^*}z} \end{align} |
holds, or the inequality
\begin{align} \frac{b_1+b_2}{4\gamma}T_M^2&\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)\Big[|\mathit{\boldsymbol{u}}|+|\mathit{\boldsymbol{u}}^*|\Big]\mathcal{D}_i\mathcal{D}_idx d\eta \\ &+ \frac{1}{2\gamma}T_M^2\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)(1+\gamma T)\mathcal{D}_i\mathcal{D}_idx d\eta \\ &+\frac{1}{2}e^{-\omega t}\int_{\Omega_z}(\xi-z)\Sigma^2dx+\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)\Big[ \frac{1}{4}\omega\Sigma^2+\frac{1}{4}\Sigma_{,i}\Sigma_{,i}\Big]dxd\eta \\ &\leq \frac{16\widetilde{b}^2}{a_1\gamma(b_1+b_2)}k_3T_M^2k_4(t)e^{-\frac{z}{k_3}}+ \\ &\widetilde{b}^2n_7(t)e^{-\frac{1}{n_6^*}z} +\widetilde{b}^2\frac{n_7(t)}{n_6^*(\frac{1}{n_6^*}-\frac{1}{k_3})}b_3(t)[e^{-\frac{1}{k_3}z}-e^{-\frac{1}{n_6^*}z}] \end{align} |
holds.
Proof. Using Lemma 3.1, we have
\begin{align} \frac{\partial }{\partial z}\Big\{\Phi(z, t)e^{\frac{1}{n_6^*}z}\Big\}\leq \widetilde{b}^2\frac{n_7(t)}{n_6^*}e^{(\frac{1}{n_6^*}-\frac{1}{k_3})z},\ z\geq 0. \end{align} | (4.1) |
Now, we consider (4.1) for two cases.
Ⅰ. If n_6^* = k_3 , we integrate Eq (4.1) from 0 to z to obtain
\begin{align} \Phi(z, t)&\leq \Phi(0, t)e^{-\frac{1}{n_6^*}z} +\widetilde{b}^2\frac{n_7(t)}{n_6^*}ze^{-\frac{1}{n_6^*}z}. \end{align} | (4.2) |
Ⅱ. If n_6^*\neq k_3 , we integrate Eq (4.1) from 0 to z to obtain
\begin{align} \Phi(z, t)&\leq \Phi(0, t)e^{-\frac{1}{n_6^*}z} +\widetilde{b}^2\frac{n_7(t)}{n_6^*(\frac{1}{n_6^*}-\frac{1}{k_3})}b_3(t)[e^{-\frac{1}{k_3}z}-e^{-\frac{1}{n_6^*}z}]. \end{align} | (4.3) |
From Eqs (4.2) and (4.3), to obtain the main result, we can conclude that we have to derive a bound for \Phi(0, t) . We choose z = 0 in Lemma 3.1 to obtain
\begin{align} \Phi(0, t)\leq n_6^*\Big[-\frac{\partial \Phi}{\partial z}(0, t)\Big]+\widetilde{b}^2n_7(t). \end{align} | (4.4) |
Clearly, if we want to derive a bound for \Phi(0, t) , we only need derive a bound for -\frac{\partial \Phi}{\partial z}(0, t) . To do this, choosing z = 0 in Eq (3.19) and combining Eqs (3.8) and (3.15), we have
\begin{align} -\frac{\partial \Phi}{\partial z}(0, t)& = \frac{2}{\gamma}T_M^2\int_{0}^{t}\int_{D}e^{-\omega\eta}\pi\mathcal{D}_3dAd\eta -\int_{0}^{t}\int_{D}e^{-\omega\eta}\Sigma\Sigma_{,3}dAd\eta \\ &+\frac{1}{2}\int_{0}^{t}\int_{D}e^{-\omega\eta}u_3\Sigma^2dAd\eta +\int_0^t\int_{D}e^{-\omega\eta}\mathcal{D}_{3}T^*\Sigma dAd\eta. \end{align} | (4.5) |
In light of the boundary conditions (3.4)–(3.6), from Eq (4.5) we can know that
\begin{align} -\frac{\partial \Phi}{\partial z}(0, t) = 0. \end{align} | (4.6) |
Inserting Eq (4.6) into Eq (4.4), we obtain
\begin{align} \Phi(0, t)\leq \widetilde{b}^2n_7(t). \end{align} | (4.7) |
Therefore, from Eqs (4.2), (4.3) and (4.7) we have
\begin{align} \Phi(z, t)&\leq \widetilde{b}^2n_7(t)e^{-\frac{1}{n_6^*}z} +\widetilde{b}^2\frac{n_7(t)}{n_6^*}ze^{-\frac{1}{n_6^*}z},\ \text{if}\ n_6^* = k_3, \end{align} | (4.8) |
\begin{align} \Phi(z, t)&\leq \widetilde{b}^2n_7(t)e^{-\frac{1}{n_6^*}z} +\widetilde{b}^2\frac{n_7(t)}{n_6^*(\frac{1}{n_6^*}-\frac{1}{k_3})}b_3(t)[e^{-\frac{1}{k_3}z}-e^{-\frac{1}{n_6^*}z}],\ \text{if }\ n_6^*\neq k_3. \end{align} | (4.9) |
Combining Eqs (3.24), (4.8) and (4.9) we can complete the proof of Theorem 4.1.
Remark 4.1 Theorem 1 shows that the small perturbation of Forchheimer coefficient will not cause great changes to the solution of Eqs (1.1)–(1.8). Meanwhile, Theorem 1 also shows that the solutions of Eqs (2.12)–(2.21) decay exponentially as the space variable z\rightarrow \infty .
This section shows how to use the prior estimates in Section 2 and the method in Section 3 to derive the continuous dependence of the solution on \gamma . Assume that (u_i^*, T^*, p^*) is a solution of Eqs (1.1)–(1.8) with \gamma = \gamma^* .
If we also let
\mathcal{D}_i = u_i-u_i^*,\ \Sigma = T-T^*,\ \pi = p-p^*,\ \widetilde{\gamma} = \gamma-\gamma^*, |
then (\mathcal{D}_i, \Sigma, \pi) satisfies
\begin{align} b[|\mathit{\boldsymbol{u}}|u_i-|\mathit{\boldsymbol{u}}^*|u_i^*]+\widetilde{\gamma}Tu_i+\gamma_2\Sigma u_i+(1+\gamma T^*)\mathcal{D}_i+\gamma\Sigma u_i^* = -\pi_{,i}+g_i\Sigma,\ &in\ \Omega\times\{t > 0\}, \end{align} | (5.1) |
\begin{align} \mathcal{D}_{i,i} = 0,\ &in\ \Omega\times\{t > 0\}, \end{align} | (5.2) |
\begin{align} \partial_{t}\Sigma+u_i\Sigma_{,i}+\mathcal{D}_i T^*_{,i} = \Delta\Sigma, \ &in\ \Omega\times\{t > 0\}, \end{align} | (5.3) |
\begin{align} \mathcal{D}_i = 0, \Sigma = 0,\ on\ \partial D\times\{x_3 > 0\}&\times\{t > 0\}, \end{align} | (5.4) |
\begin{align} \mathcal{D}_i = 0, \Sigma = 0,\ & on\ D\times\{t > 0\}, \end{align} | (5.5) |
\begin{align} \Sigma(x_1, x_2, x_3, 0) = 0, \ &in \ \Omega \end{align} | (5.6) |
\begin{align} |\mathit{\boldsymbol{u}}|, |\Sigma| = O(1), |\mathcal{D}_i|, |\nabla\Sigma|, |\pi| = o(x_3^{-1}),&\ as\ x_3\rightarrow \infty. \end{align} | (5.7) |
We also define \Phi_1(z, t) as that in Eq (3.8). Similar to Eq (3.10), we have
\begin{align} \Phi_1(z, t) & = \frac{b}{2}\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)\Big[|\mathit{\boldsymbol{u}}|+|\mathit{\boldsymbol{u}}^*|\Big]\mathcal{D}_i\mathcal{D}_idx d\eta \\ &+\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)(1+\gamma_2 T^*)\mathcal{D}_i\mathcal{D}_idx d\eta \\ &+\frac{b}{2}\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)\Big[|\mathit{\boldsymbol{u}}|-|\mathit{\boldsymbol{u}}^*|\Big]^2\Big[|\mathit{\boldsymbol{u}}|+|\mathit{\boldsymbol{u}}^*|\Big]dx d\eta \\ &+\gamma_2\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)\mathcal{D}_i\Sigma u_i^*dx d\eta -\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)\mathcal{D}_ig_i\Sigma dx d\eta \\ &+\widetilde{\gamma}\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)Tu_i\mathcal{D}_i dx d\eta. \end{align} | (5.8) |
Using the Hölder inequality, Young's inequality and Lemmas 2.5 and 2.6, we obtain
\begin{align} & \widetilde{\gamma}\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}Tu_i\mathcal{D}_i dx d\eta\\ & \geq-\frac{1}{2}T_M^2\widetilde{\gamma}^2\int_0^t\int_{\Omega_z}e^{-\omega\eta}u_{i}u_{i}dxd\eta -\frac{1}{2}\int_0^t\int_{\Omega_z}e^{-\omega\eta}(1+\gamma_2 T^*)\mathcal{D}_i\mathcal{D}_idxd\eta \\ &\geq-\frac{k_4(t)}{2a_2}T_M^2\widetilde{\gamma}^2e^{-\frac{z}{k_3}} -\frac{1}{2}\int_0^t\int_{\Omega_z}e^{-\omega\eta}(1+\gamma_2 T^*)\mathcal{D}_i\mathcal{D}_idxd\eta, \end{align} | (5.9) |
Combining Eqs (3.12), (3.13), (5.8) and (5.9), we obtain
\begin{align}& -\frac{\partial}{\partial z}\Phi_1(z, t)\\ &\geq\frac{b}{2}\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}\Big[|\mathit{\boldsymbol{u}}|+|\mathit{\boldsymbol{u}}^*|\Big]\mathcal{D}_i\mathcal{D}_idx d\eta +\frac{1}{2}\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(1+\gamma_2 T^*)\mathcal{D}_i\mathcal{D}_idx d\eta \\ &-\frac{4\gamma_2^2\sqrt{k_5(t)k_2}}{b}\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}\Sigma_{,i}\Sigma_{,i}dx d\eta -\frac{1}{2\gamma}\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}\Sigma^2dx d\eta \\ &-\frac{k_4(t)}{2a_2}T_M^2\widetilde{\gamma}^2e^{-\frac{z}{k_3}}. \end{align} | (5.10) |
Inserting Eqs (3.18) and (5.10) into Eq (3.19), choosing \omega > \frac{4T_M^2}{\gamma^2} and the boundary data satisfies
\begin{align} \frac{8(\gamma^*)^2\sqrt{k_5(t)k_2}}{b}\leq\frac{1}{4}, \end{align} | (5.11) |
we have
\begin{align} & -\frac{\partial}{\partial z}\Phi(z, t)\\ &\geq\frac{b}{\gamma^*}T_M^2\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}\Big[|\mathit{\boldsymbol{u}}|+|\mathit{\boldsymbol{u}}^*|\Big]\mathcal{D}_i\mathcal{D}_idx d\eta +\frac{1}{(\gamma^*)^2}T_M^2\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(1+\gamma_2 T^*)\mathcal{D}_i\mathcal{D}_idx d\eta \\ &+\frac{1}{2}e^{-\omega t}\int_{\Omega_z}\Sigma^2dx+\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}\Big[ \frac{1}{4}\omega\Sigma^2+\frac{1}{4}\Sigma_{,i}\Sigma_{,i}\Big]dxd\eta \\ &-\frac{k_4(t)}{2a_2\gamma^*}T_M^4\widetilde{\gamma}^2e^{-\frac{z}{k_3}}. \end{align} | (5.12) |
Integrating Eq (5.12) from z to \infty , we obtain
\begin{align} \Phi(z, t)&\geq\frac{b}{\gamma^*}T_M^2\\ &\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)\Big[|\mathit{\boldsymbol{u}}|+|\mathit{\boldsymbol{u}}^*|\Big]\mathcal{D}_i\mathcal{D}_idx d\eta +\frac{1}{(\gamma^*)^2}T_M^2\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(1+\gamma_2 T^*)\mathcal{D}_i\mathcal{D}_idx d\eta \\ &+\frac{1}{2}e^{-\omega t}\int_{\Omega_z}(\xi-z)\Sigma^2dx+\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)\Big[ \frac{1}{4}\omega\Sigma^2+\frac{1}{4}\Sigma_{,i}\Sigma_{,i}\Big]dxd\eta \\ &-\frac{k_4(t)}{2a_2\gamma^*}k_3T_M^4\widetilde{\gamma}^2e^{-\frac{z}{k_3}}. \end{align} | (5.13) |
Similar to the calculation in Eqs (3.33) and (3.37), we can get
\begin{align} \Phi(z, t)&\leq n_6'(t)\Big[-\frac{\partial}{\partial z}\Phi(z, t)\Big]+n_7'(t)\widetilde{b}^2e^{-\frac{z}{k_3}}, \end{align} | (5.14) |
for n_6'(t), n_7'(t) > 0 .
After similar analysis as in the previous section, we can get the following theorem from Eq (5.14).
Theorem 5.1. Let (u_i, T, p) and (u_i^*, T^*, p^*) be solutions of the Eqs (1.1)–(1.8) in \Omega , corresponding to b_1 and b_2 , respectively. If \int_Df_3dA = 0 , Equation (5.11) holds and f_{\alpha, \alpha}-\gamma_1f_3 = 0, H\in L^\infty(\Omega\times\{t > 0\}) , then
(u_i, T)\rightarrow (u_i^*, T^*),\ as\ b_1\rightarrow b_2. |
Specifically, either the inequality
\begin{align} \frac{b}{\gamma^*}T_M^2&\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)\Big[|\mathit{\boldsymbol{u}}|+|\mathit{\boldsymbol{u}}^*|\Big]\mathcal{D}_i\mathcal{D}_idx d\eta \\ &+ \frac{1}{(\gamma^*)^2}T_M^2\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)(1+\gamma^* T^*)\mathcal{D}_i\mathcal{D}_idx d\eta \\ &+\frac{1}{2}e^{-\omega t}\int_{\Omega_z}(\xi-z)\Sigma^2dx+\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)\Big[ \frac{1}{4}\omega\Sigma^2+\frac{1}{4}\Sigma_{,i}\Sigma_{,i}\Big]dxd\eta \\ &\leq \frac{k_4(t)}{2a_2\gamma^*}k_3T_M^4\widetilde{\gamma}^2e^{-\frac{z}{k_3}}+ \widetilde{\gamma}^2n_7'(t)e^{-\frac{1}{n_6^*}z} +\widetilde{\gamma}^2\frac{n_7'(t)}{n_6^*}ze^{-\frac{1}{n_6^*}z} \end{align} |
holds, or the inequality
\begin{align} \frac{b}{\gamma^*}T_M^2&\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)\Big[|\mathit{\boldsymbol{u}}|+|\mathit{\boldsymbol{u}}^*|\Big]\mathcal{D}_i\mathcal{D}_idx d\eta \\ &+ \frac{1}{(\gamma^*)^2}T_M^2\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)(1+\gamma^* T^*)\mathcal{D}_i\mathcal{D}_idx d\eta \\ &+\frac{1}{2}e^{-\omega t}\int_{\Omega_z}(\xi-z)\Sigma^2dx+\int_{0}^{t}\int_{\Omega_z}e^{-\omega\eta}(\xi-z)\Big[ \frac{1}{4}\omega\Sigma^2+\frac{1}{4}\Sigma_{,i}\Sigma_{,i}\Big]dxd\eta \\ &\leq \frac{k_4(t)}{2a_2\gamma^*}k_3T_M^4\widetilde{\gamma}^2e^{-\frac{z}{k_3}}+ \widetilde{\gamma}^2n_7'(t)e^{-\frac{1}{n_6^*}z} +\widetilde{\gamma}^2\frac{n_7'(t)}{n_6^*(\frac{1}{n_6^*}-\frac{1}{k_3})}b_3(t)[e^{-\frac{1}{k_3}z}-e^{-\frac{1}{n_6^*}z}] \end{align} |
holds.
In this paper, using a priori estimates of the solutions, we show how to control the nonlinear term, and obtain the structural stability of the solution of the Forchheimer equation in a semi-infinite cylinder. Meanwhile, the spatial decay results of the solution are also obtained. The methods in this paper can bring some inspiration for the structural stability of other nonlinear partial differential equations.
The authors express their heartfelt thanks to the editors and referees who have provided some important suggestions. This work is supported by the Tutor System Rroject of Guangzhou Huashang College (2021HSDS13) and the Key projects of universities in Guangdong Province (NATURAL SCIENCE) (2019KZDXM042).
The authors declare there is no conflict of interest. Conceptualization, and validation, Z. Li.; formal analysis, Z W. Zhang; investigation, Y. Li. All authors have read and agreed to the published version of the manuscript.
[1] |
Akerlof GA (2002) Behavioral macroeconomics and macroeconomic behavior. Am Econ Rev 92: 411–433. https://doi.org/10.1257/00028280260136192 doi: 10.1257/00028280260136192
![]() |
[2] | Anderson TW, Goodman LA (1957) Statistical inference about Markov chains. Ann Math Stat 28: 89–110. |
[3] | Arrow KJ (1971) Essays in the Theory of Risk-Bearing. Markham Pub Co |
[4] |
Assenza T, Delli Gatti D, Grazzini J (2015) Emergent dynamics of a macroeconomic agent-based model with capital and credit. J Econ Dyn Control 50: 5–28. https://doi.org/10.1016/j.jedc.2014.07.001 doi: 10.1016/j.jedc.2014.07.001
![]() |
[5] | Axtell RL, Farmer JD (2022) Agent-Based Modeling in Economics and Finance: Past, Present, and Future. INET Oxford Working Paper No. 2002-10. 21^{st} June 2022. |
[6] |
Barbu VS, D'Amico G, De Blasis R (2017) Novel advancements in the Markov chain stock model: Analysis and inference. Ann Financ 13: 125–152. https://doi.org/10.1007/s10436-017-0297-9 doi: 10.1007/s10436-017-0297-9
![]() |
[7] |
Battiston S, Farmer JD, Flache A, et al. (2016) Complexity theory and financial regulation. Science 351: 818–819. https://doi.org/10.1126/science.aad0299 doi: 10.1126/science.aad0299
![]() |
[8] | Billingsley P (1961) Statistical methods in Markov chains. Ann Math Stat 32: 12–40. |
[9] |
Bonabeau (2002) Agent-based modeling: Methods and techniques for simulating human systems. P Natl Acad Sci 99: 7280–7287. https://doi.org/10.1073/pnas.0820808 doi: 10.1073/pnas.0820808
![]() |
[10] |
Bookstaber R (2017) Agent-Based Models for Financial Crises. Ann Rev Financ Econ 9: 85–100. https://doi.org/10.1146/annurev-financial-110716-032556 doi: 10.1146/annurev-financial-110716-032556
![]() |
[11] | Buchanan M (2009) Meltdown modelling: could agent-based computer models prevent another financial crisis? Nature 460: 680–683. |
[12] |
Delli G, Gaffeo E, Gallegati M (2010) Complex agent-based macroeconomics: a manifesto for a new paradigm. J Econ Interact Coor 5: 111–135. https://doi.org/10.1007/s11403-010-0064-8 doi: 10.1007/s11403-010-0064-8
![]() |
[13] | Ehrenberg AS (1988) Repeat-Buying: Facts, Theory and Applications. Oxford University Press. |
[14] | Epstein JM (2006) Generative social science: Studies in agent-based computational modeling. Princeton University Press. |
[15] |
Farmer JD, Foley D (2009) The economy needs agent–based modelling. Nature 460: 685–686. https://doi.org/10.1038/460685a doi: 10.1038/460685a
![]() |
[16] |
Gary JG, Irwin PL, Goutam C, et al. (1991) Consumer evaluation of multi-product bundles: An information integration analysis. Market Lett 2: 47–57. https://doi.org/10.1007/BF00435195 doi: 10.1007/BF00435195
![]() |
[17] |
Grazzini J, Richiardi MG (2015) Estimation of ergodic agent-based models by simulated minimum distance. J Econ Dyn Control 51: 148–165. https://doi.org/10.1016/j.jedc.2014.10.006 doi: 10.1016/j.jedc.2014.10.006
![]() |
[18] | Hamill L, Gilbert N (2015) Agent–based modelling in economics. John Wiley & Sons. |
[19] | Kahneman D (2003) Maps of Bounded Rationality: Psychology for Behavioral Economics. Am Econ Rev 93: 1449–1475. |
[20] |
Kaplow L (2008) Optimal policy with heterogeneous preferences. BE J Econ Anal Policy 8. https://doi.org/10.2202/1935-1682.1947 doi: 10.2202/1935-1682.1947
![]() |
[21] | Leijonhufvud A (1996) Towards a not-too-rational macroeconomics, In: Colander, D. (Ed.), Beyond Microfoundations: Post Walrasian Macroeconomics. Cambridge University Press, Cambridge, MA., 39–55. |
[22] | Macal CM, North MJ (2005) Tutorial on agent-based modeling and simulation. Proceedings of the Winter Simulation Conference 14, IEEE. |
[23] |
Maravilha D, Silva S, Laranjeira E (2022) Consumer's behavior determinants after the electricity market liberalization: the Portuguese case. Green Financ 4: 436–449. https://doi.org/10.3934/GF.2022021 doi: 10.3934/GF.2022021
![]() |
[24] |
Mandel A, Landini S, Gallegati M, et al. (2015) Price dynamics, financial fragility and aggregate volatility. J Econ Dyn Control 51: 257–277. https://doi.org/10.1016/j.jedc.2014.11.001 doi: 10.1016/j.jedc.2014.11.001
![]() |
[25] |
Manout O, Ciari F (2021) Assessing the Role of Daily Activities and Mobility in the Spread of COVID-19 in Montreal with an Agent-Based Approach. Front Built Enviroment 7: 2021. https://doi.org/10.3389/fbuil.2021.654279 doi: 10.3389/fbuil.2021.654279
![]() |
[26] | McFadden D (1974) Conditional Logit Analysis of Qualitative Choice Behavior. Front Econometrics Zarembka (Ed.) 105–142. New York: Academic Press. |
[27] |
Mullainathan S, Spiess J (2017) Machine Learning: An Applied Econometric Approach. J Econ Perspect 31: 87–106. https://doi.org/10.1257/jep.31.2.87 doi: 10.1257/jep.31.2.87
![]() |
[28] |
Peña G (2020) A new trading algorithm with financial applications. Quant Financ Econ 4: 596–607. https://doi.org/10.3934/QFE.2020027 doi: 10.3934/QFE.2020027
![]() |
[29] |
Predelus W, Amine S (2022) The insolvency choice during an economic crisis: the case of Canada. Quant Financ Econ 6: 658–668. https://doi.org/10.3934/QFE.2022029 doi: 10.3934/QFE.2022029
![]() |
[30] |
Reisch LA, Zhao M (2017) Behavioural economics, consumer behaviour and consumer policy: state of the art. Behav Public Policy 1: 190–206. https://doi.org/10.1017/bpp.2017.1 doi: 10.1017/bpp.2017.1
![]() |
[31] |
Seetharaman PB (2004) Modeling multiple sources of state dependence in random utility models: A distributed lag approach. Market Sci 23: 263–271. https://doi.org/10.1287/mksc.1030.0024 doi: 10.1287/mksc.1030.0024
![]() |
[32] | Simon HA (1955) A Behavioral Model of Rational Choice. Q J Econ 69: 99–118. |
[33] | Simudyne (2023) A Complete-Guide to Agent-Based Modeling for Financial Services. Simudyne 2023. |
[34] |
Tesfatsion L (2006) Agent-Based Computational Economics: A Constructive Approach to Economic Theory. Handb Computat Econ 2: 831–880. https://doi.org/10.1016/S1574-0021(05)02016-2 doi: 10.1016/S1574-0021(05)02016-2
![]() |
[35] | Tesfatsion L, Judd KL (2006) Handbook of computational economics: agent-based computational economics. Elsevier. |
[36] | Thaler RH (2015) Misbehaving: The Making of Behavioral Economics. W. W. Norton & Company. |
[37] | Thurner S, Farmer JD, Geanakoplos J (2012) Using agent-based simulations, this work discusses the role of leverage in financial markets, explaining phenomena like fat-tailed asset returns. Quant Financ 12: 695–707. |
[38] | Tsiatsios GA, Kollias I, Melas E, et al. (2022) Some first results from an agent–based model of consumer demand. submitted. |
[39] | Tsiatsios GA, Leventides I, Melas E, et al. (2023a) A bounded rational agent-based model of consumer choice. submitted. |
[40] | Tsiatsios GA, Leventides I, Toudas K, et al. (2023b) An agent–based study on the dynamic distribution and firms concentration in a closed economy. submitted. |
[41] | Turell A (2016) Agent-based models: Understanding the economy from the bottom up. Bank England Quart Bull 2016. |
[42] | Varian HR (2014) Intermediate Microeconomics: A Modern Approach. W. W. Norton & Company. |
[43] |
Zhong Li Z (2020) Impact of economic policy uncertainty shocks on China's financial conditions. Financ Res Lett 35: 101303. https://doi.org/10.1016/j.frl.2019.101303 doi: 10.1016/j.frl.2019.101303
![]() |
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