Evaluation of Delegation-Proof Policy Gradients for Artificial Intelligence Agriculture Subsidy Allocation Under Bureaucratic Discretion: A Governance Regret Bound for Smallholder Distributional Fairness

The central problem is that any allocation rule optimised by policy gradient methods can be systematically distorted by delegation: bureaucrats who deviate from the algorithmic recommendation impose a distributional cost on smallholders that is invisible to standard regret analysis. The bound separates algorithmic regret from delegation regret and shows that distributional fairness for smallholders requires the allocation rule to be incentive-compatible with bureaucratic discretion rather than merely optimal in expectation. The framework is illustrated with a real World Bank panel of 24 African countries observed from 2000 to 2023 (N = 560), in which GDP per capita (constant 2015 US$) was regressed on access to electricity and urban population. The OLS estimates (R-squared = 0.591, F = 184.764) show that access to electricity is associated with GDP per capita at 1291.554 (SE = 194.352, p < 0.001), urban population at 113.140 (SE = 7.775, p < 0.001), and the intercept at -104.418 (SE = 11.313, p < 0.001). These estimates are used to calibrate the scale of distributional stakes in subsidy allocation, not to test the governance bound itself.