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This paper presents a probabilistic approach to continuous-time economic models using backward stochastic differential equations (BSDEs) to address the limitations of traditional partial differential equations (PDEs) in high-dimensional settings. It demonstrates that deep learning can enhance the search for probabilistic solutions, which are less affected by the curse of dimensionality, and introduces two algorithms to assess their performance. Numerical experiments reveal that the computational speed of th

Author

bailid697

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EN