Multi-Agent Reinforcement Learning for Artificial Intelligence Agriculture Groundwater Irrigation Quotas Under Aquifer Boundary Uncertainty: A Computer Welfare-Regret Evaluation

Groundwater irrigation quotas in agricultural multi-agent settings are typically computed against a known aquifer boundary, yet the boundary itself is an estimate whose uncertainty propagates into every quota decision. The framework separates two regret components: a boundary-regret term arising from mis-specified aquifer extent, and a coordination-regret term arising from the gap between the MARL equilibrium and the social optimum. A BACI design is then analysed under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, across cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points, with 2000 replications per design cell. The design analysis indicates that 54 clusters are required to detect a 5 percentage-point effect at 80% power under this variance structure. The contribution is a formal bridge between MARL quota theory and the evaluation designs that would be needed to test it, together with an explicit statement of the boundary conditions under which welfare-regret bounds remain informative.