关于如何利用随机参数差分方程给出的总体

Pub Date : 2022-06-01 DOI:10.35634/vm220204
Rodin A.A., Rodina L.I., Chernikova A.V.
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引用次数: 2

摘要

我们考虑了一个由依赖于随机参数的差分方程给出的被开发齐次总体的模型。在没有开发的情况下,人口的发展由等式$$X(k+1)=f\bigl(X(k)\bigr), \quad k=1,2,\ldots,$$描述,其中$X(k)$是人口规模或时间上的生物资源量$k,$$f(x)$是在$I=[0,a]$上定义的实可微函数,因此$f(I)\subseteq I.$在$k=1,2,\ldots$时刻,从人口中提取资源$\omega(k)\in\omega\subseteq[0,1]$的随机部分。当收获资源的份额超过一定值$u(k)\in[0,1)$时,可以停止收获过程,以保持尽可能多的种群。那么,提取资源的份额将等于$\ell(k)=\min (\omega(k),u(k)).$,提取资源的平均临时收益$H_*$等于在$k\to\infty.$时刻$1,2,\ldots,k$提取资源量的算术平均值$X(k)\ell(k)$的极限,我们解决了选择收获过程控制的问题,其中$H_*$的值可以从下面估计,概率为1。越大越好。平均时间效益的估计取决于函数$f(x)$的性质,它决定了种群的动态;这些估计是对具有$f(x)$的三类方程的估计,它们具有一定的性质。通过基于动态规划的数值算例说明,人口开发过程是一个马尔可夫决策过程。
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On how to exploit a population given by a difference equation with random parameters
We consider a model of an exploited homogeneous population given by a difference equation depending on random parameters. In the absence of exploitation, the development of the population is described by the equation $$X(k+1)=f\bigl(X(k)\bigr), \quad k=1,2,\ldots,$$ where $X(k)$ is the population size or the amount of bioresources at time $k,$ $f(x)$ is a real differentiable function defined on $I=[0,a]$ such that $f(I)\subseteq I.$ At moments $k=1,2,\ldots$, a random fraction of the resource $\omega(k)\in\omega\subseteq[0,1]$ is extracted from the population. The harvesting process can be stopped when the share of the harvested resource exceeds a certain value of $u(k)\in[0,1)$ to keep as much of the population as possible. Then the share of the extracted resource will be equal to $\ell(k)=\min (\omega(k),u(k)).$ The average temporary benefit $H_*$ from the extraction of the resource is equal to the limit of the arithmetic mean from the amount of extracted resource $X(k)\ell(k)$ at moments $1,2,\ldots,k$ when $k\to\infty.$ We solve the problem of choosing the control of the harvesting process, in which the value of $H_*$ can be estimated from below with probability one, as large a number as possible. Estimates of the average time benefit depend on the properties of the function $f(x)$, determining the dynamics of the population; these estimates are obtained for three classes of equations with $f(x)$, having certain properties. The results of the work are illustrated, by numerical examples using dynamic programming based on, that the process of population exploitation is a Markov decision process.
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