Abstract:Organizations routinely make strategic budget allocations under operational constraints, but often lack a principled way to assess whether realized allocations were close to the best feasible choices in hindsight. We present a retrospective auditing framework based on hindsight regret, defined as the opportunity cost of the realized allocation relative to a constraint-faithful benchmark under the same budget and stability guardrails. The framework estimates regime-specific spend--response functions from historical logs, computes feasible hindsight allocations via constrained optimization, and propagates uncertainty through Monte Carlo evaluation to produce regret distributions, expected lift, and probability-of-improvement summaries. This separates allocation inefficiency from uncertainty in the estimated response surfaces. Experiments on real marketing allocation logs show that the framework yields interpretable post-hoc diagnostics and reveals a practical trade-off between allocation flexibility and detectability: moderate feasible reallocations often capture most measurable gain, while larger shifts move into weak-support regions with higher uncertainty. The result is a practical method for auditing historical budget decisions when online experimentation is costly or infeasible.
| Comments: | 6 pages, 8 figures |
| Subjects: | Econometrics (econ.EM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Portfolio Management (q-fin.PM) |
| ACM classes: | H.4.2; G.3; I.2.6; I.2.8; J.4 |
| Cite as: | arXiv:2604.25977 [econ.EM] |
| (or arXiv:2604.25977v1 [econ.EM] for this version) | |
| https://doi.org/10.48550/arXiv.2604.25977 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Nilavra Pathak [view email]
[v1]
Tue, 28 Apr 2026 13:57:02 UTC (3,131 KB)
