This chapter is the shortest path from a Python environment to a model you can change. It assumes basic Python and the idea of a finite-horizon dynamic program, but no JAX, DAG, or pylcm knowledge.
Follow the pages in order:
Choose the right starting declaration before adapting the example to your own economics.
The tiny model deliberately uses the broad Regime + GridSearch route. It teaches the
common workflow; it is not a universal template. If you expect to use an endogenous-grid
solver, write the model as a ConsumptionSavingsRegime or
NestedConsumptionSavingsRegime immediately. Retrofitting solver-specific economic
roles after a model has grown is avoidable work.
For a refresher on dynamic programming, see the QuantEcon Dynamic Programming book.