We propose a generic computational framework for solving large-scale infinite-horizon, discrete-time dynamic incentive problems with persistent hidden types. First, we combine set-valued dynamic programming techniques with unsupervised machine learning to determine irregularly-shaped feasible...

We present the first computational framework that can compute global solutions to very-high-dimensional dynamic stochastic economic models on arbitrary state space geometries.This framework can also resolve value and policy functions' local features and perform uncertainty quantification, in a...