论文标题
价格形成模型的机器学习体系结构
Machine Learning architectures for price formation models
论文作者
论文摘要
在这里，我们研究机器学习（ML）体系结构，以解决价格形成模型中出现的平均场景（MFGS）系统。我们制定了培训过程，该过程依赖于最佳控制和价格变量的最小特征。我们的主要理论贡献是将后验估计的发展作为评估训练过程收敛性的工具。我们通过线性动力学以及二次模型和非二次模型的数值实验来说明我们的结果。
Here, we study machine learning (ML) architectures to solve a mean-field games (MFGs) system arising in price formation models. We formulate a training process that relies on a min-max characterization of the optimal control and price variables. Our main theoretical contribution is the development of a posteriori estimates as a tool to evaluate the convergence of the training process. We illustrate our results with numerical experiments for linear dynamics and both quadratic and non-quadratic models.
