Deep-MacroFin: Informed Equilibrium Neural Network for Continuous Time Economic Models

ArXiv ID: 2408.10368 “View on arXiv”

Authors: Unknown

Abstract

In this paper, we present Deep-MacroFin, a comprehensive framework designed to solve partial differential equations, with a particular focus on models in continuous time economics. This framework leverages deep learning methodologies, including Multi-Layer Perceptrons and the newly developed Kolmogorov-Arnold Networks. It is optimized using economic information encapsulated by Hamilton-Jacobi-Bellman (HJB) equations and coupled algebraic equations. The application of neural networks holds the promise of accurately resolving high-dimensional problems with fewer computational demands and limitations compared to other numerical methods. This framework can be readily adapted for systems of partial differential equations in high dimensions. Importantly, it offers a more efficient (5$\times$ less CUDA memory and 40$\times$ fewer FLOPs in 100D problems) and user-friendly implementation than existing libraries. We also incorporate a time-stepping scheme to enhance training stability for nonlinear HJB equations, enabling the solution of 50D economic models.

Keywords: Deep Learning, Partial Differential Equations, Hamilton-Jacobi-Bellman Equations, Kolmogorov-Arnold Networks, High-Dimensional Problems, Economics (General)

Complexity vs Empirical Score

  • Math Complexity: 9.5/10
  • Empirical Rigor: 2.5/10
  • Quadrant: Lab Rats
  • Why: The paper is highly theoretical, focusing on advanced mathematics like high-dimensional PDEs, Hamilton-Jacobi-Bellman equations, and neural network architectures (KANs), with no empirical backtesting or real financial data used; its ‘rigor’ is computational (memory/FLOP benchmarks) rather than financial.
  flowchart TD
    A["Research Goal:<br>Solve high-dimensional continuous-time economic models"] --> B["Methodology:<br>Deep-MacroFin Framework"]
    
    B --> C1["Neural Network Architectures:<br>MLPs & Kolmogorov-Arnold Networks"]
    B --> C2["Input Equations:<br>HJB & Coupled Algebraic Equations"]
    
    C1 --> D["Computational Process:<br>Optimization & Time-Stepping Scheme"]
    C2 --> D
    
    D --> E["Key Outcomes:<br>5x less CUDA memory<br>40x fewer FLOPs<br>50D economic model solutions"]