Marginal Optimization Value of Evidence for Constrained Allocation

Allocating limited resources under incomplete information requires choosing which evidence to acquire before choosing an allocation. Yet reducing uncertainty need not resolve which feasible allocation is preferable. We introduce Marginal Optimization Value of Evidence (MOVE), an acquisition rule that targets uncertainty in utility differences between feasible allocations. MOVE pairs predicted covariance reduction with the sensitivity of an entropy-smoothed allocation value, so that constraints shape which observations are useful. We establish an exact allocation-contrast interpretation and connect the implemented score to reoptimization value through explicit virtual-update and prediction-error terms, without assuming that the learned model is a coherent posterior. A masked-evidence model supplies query estimates, and an exact discrete solver returns the final allocation. Across 96 held-out allocation instances on public county data, spanning 32 county subsets and three objectives, MOVE achieves higher overall utility than all evaluated same-budget baselines, with component and objective-wise analyses supporting allocation-sensitive acquisition.