AI RESEARCH
Learning to Spend: Model Predictive Control for Budgeting under Non-Stationary Returns
arXiv CS.AI
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ArXi:2604.27186v1 Announce Type: cross We study finite-horizon budget allocation as a closed-loop economic control problem and evaluate receding-horizon Model Predictive Control (MPC) relative to reactive budgeting policies. Budgets are allocated periodically under execution noise and operational constraints, while return efficiency may evolve over time. Using a controlled simulation framework motivated by digital marketing, we compare reactive pacing to MPC across environments with increasing degrees of non-stationarity.