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arXiv 1503.08419math.AP
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知识增长的玻尔兹曼平均场模型

On a Boltzmann mean field model for knowledge growth

Martin Burger, Alexander Lorz, Marie-Therese Wolfram

摘要:

本文分析了Lucas和Moll提出的知识增长的Boltzmann型平均场对策模型。我们讨论了基本的数学模型,它由一个耦合系统的Boltzmann型方程的代理密度和一个Hamilton-Jacobi-Bellman方程的最优策略。我们分别研究了每个方程的解析性质,并证明了完全耦合系统解的局部存在性和唯一性。此外,我们专注于特殊的解决方案,这涉及到指数增长性的时间-所谓的平衡增长路径解决方案的建设和存在性。最后,我们通过数值模拟说明了完整系统和平衡增长路径方程解的行为。

英文摘要:

In this paper we analyze a Boltzmann type mean field game model for knowledge growth, which was proposed by Lucas and Moll. We discuss the underlying mathematical model, which consists of a coupled system of a Boltzmann type equation for the agent density and a Hamilton-Jacobi-Bellman equation for the optimal strategy. We study the analytic features of each equation separately and show local in time existence and uniqueness for the fully coupled system. Furthermore we focus on the construction and existence of special solutions, which relate to exponential growth in time - so called balanced growth path solutions. Finally we illustrate the behavior of solutions for the full system and the balanced growth path equations with numerical simulations.

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