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Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria

深度强化学习寻找纳什均衡的计算性能

Christoph Graf,Viktor Zobernig,Johannes Schmidt et al.

Computational economics. 2024;63(2):529-576. DOI:10.1007/s10614-022-10351-6

On the Optimal Size and Composition of Customs Unions: An Evolutionary Approach

关于关税同盟的最优规模和构成的一种进化方法

Takfarinas Saber,Dominik Naeher,Philippe De Lombaerde

Computational economics. 2023;62(4):1457-1479. DOI:10.1007/s10614-022-10307-w

Stocks Opening Price Gaps and Adjustments to New Information

股价的缺口以及对新信息的调整反应

Aiche Avishay,Cohen Gil,Griskin Vladimir

Computational economics. 2023 Mar 15:1-15. DOI:10.1007/s10614-023-10363-w

Nonparametric Test for Volatility in Clustered Multiple Time Series

分群多重时间序列波动性的非参数检验方法

Erniel B Barrios,Paolo Victor T Redondo

Computational economics. 2023 Mar 16:1-16. DOI:10.1007/s10614-023-10362-x

The Rise and Fall of Financial Flows in EU 15: New Evidence Using Dynamic Panels with Common Correlated Effects

欧盟15国金融流量的兴衰：基于动态面板公共相关效应的新证据

Mariam Camarero,Alejandro Muñoz,Cecilio Tamarit

Computational economics. 2023 Mar 16:1-40. DOI:10.1007/s10614-023-10366-7

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