End-to-End Python implementation of Azcue et al.'s (2025) stochastic optimal control framework for social protection policy design. Solves PDMP-based Hamilton-Jacobi-Bellman equations using analytical closed-form solutions and Monte Carlo simulation to minimize government intervention costs (through the use of cash transfers and microinsurance).
python scientific-computing scipy monte-carlo-simulation dynamic-programming optimal-control numerical-optimization mathematical-finance poisson-process risk-management computational-economics social-policy development-economics stochastic-control poverty-alleviation actuarial-science markov-processes cash-transfers research-replication microinsurance
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Updated
Nov 16, 2025 - Jupyter Notebook