论文标题
价格成型模型的潜在方法
The Potential Method For Price-Formation Models
论文作者
论文摘要
我们考虑Gomes和Saúde引入的平均场地游戏价格形成模型。 在这种MFG模型中，代理商交易了一种商品，其供应可以是确定性或随机性的。考虑到当前和未来价格，代理商最大化利润。供求之间的平衡决定了价格。我们引入了将MFG转换为凸变量问题的潜在功能。这种变分的配方特别适合机器学习方法。在这里，我们使用复发性神经网络来解决此问题。在本文的最后一部分中，我们将结果与已知的分析解决方案进行了比较。
We consider the mean-field game price formation model introduced by Gomes and Saúde. In this MFG model, agents trade a commodity whose supply can be deterministic or stochastic. Agents maximize profit, taking into account current and future prices. The balance between supply and demand determines the price. We introduce a potential function that converts the MFG into a convex variational problem. This variational formulation is particularly suitable for machine learning approaches. Here, we use a recurrent neural network to solve this problem. In the last section of the paper, we compare our results with known analytical solutions.
