AI & ML
Finite difference methods for a continuous-time heterogeneous agent model with recursive utility
arXiv cs.NA0 views
arXiv:2606.23408v2 Announce Type: replace-cross Abstract: We propose, analyze and test computational methods for solving a continuous-time heterogenous agent model with Epstein-Zin utility. Such recursive utilities allow the model to disentangle between risk aversion and intertemporal substitution. Having discretized the Hamilton-Jacobi-Bellman (HJB) equation arising in the model, we propose a Howard-Newton algorithm for the late resolution preference case, and a Howard-Tarski-Kantorovich algorithm for the early resolution preference case. We prove the convergence of the iterative algorithms. We obtain as a consequence the existence of solutions to the discretized HJB equations. In the late resolution case, we supply a priori estimates between the unique solutions of the continuous and discretized HJB equations.
Source:https://arxiv.org/abs/2606.23408PreviousA Theory-guided Weighted $L^2$ Loss for solving the BGK model via Physics-informed neural networksNextMultimodal User Authentication via Keystroke-Triggered Fusion of Glove-Based Finger Pressure and Hand Kinematics
Related posts
The Autonomous Edge — Issue #13: Cheaper Agents, Harder Governance (Week of November 2, 2026)
0dev.to AI

Plano Físico vs Plano Funcional en AWS Networking: cómo los arquitectos senior resuelven lo que otros no ven
0dev.to AWS
We built Origami Tutor: free paper-craft lessons you can turn and replay in 3D
0dev.to WebDev
From two authorized photos to a short AI rap video (consent-first workflow)
0dev.to WebDev
Check a File Hash in the Browser, Without Uploading the File
0dev.to WebDev
Your AI keys were leaked at a platform. A local guard you can paste catches the abuse before the invoice.
0dev.to Security