Solving Heterogeneous Agent Models with Physics-informed Neural Networks
This addresses computational bottlenecks for economists and policymakers in modeling household behavior and macroeconomic dynamics, representing an incremental improvement over existing grid-based methods.
The paper tackled the computational challenges of solving heterogeneous agent models in continuous time, such as the Aiyagari-Bewley-Huggett framework, by introducing an ABH-PINN solver based on Physics-Informed Neural Networks, which achieved economically valid results matching established finite-difference solvers.
Understanding household behaviour is essential for modelling macroeconomic dynamics and designing effective policy. While heterogeneous agent models offer a more realistic alternative to representative agent frameworks, their implementation poses significant computational challenges, particularly in continuous time. The Aiyagari-Bewley-Huggett (ABH) framework, recast as a system of partial differential equations, typically relies on grid-based solvers that suffer from the curse of dimensionality, high computational cost, and numerical inaccuracies. This paper introduces the ABH-PINN solver, an approach based on Physics-Informed Neural Networks (PINNs), which embeds the Hamilton-Jacobi-Bellman and Kolmogorov Forward equations directly into the neural network training objective. By replacing grid-based approximation with mesh-free, differentiable function learning, the ABH-PINN solver benefits from the advantages of PINNs of improved scalability, smoother solutions, and computational efficiency. Preliminary results show that the PINN-based approach is able to obtain economically valid results matching the established finite-difference solvers.