Decision-Focused Bias Correction for Fluid Approximation
- URL: http://arxiv.org/abs/2512.15726v1
- Date: Thu, 04 Dec 2025 23:12:05 GMT
- Title: Decision-Focused Bias Correction for Fluid Approximation
- Authors: Can Er, Mo Liu,
- Abstract summary: We investigate how to identify an alternative point statistic, which is not necessarily the mean.<n>We refer to this statistic as the decision-corrected point estimate (time-varying arrival rate)<n>Under a decomposable network structure, we show that the resulting decision-corrected point estimate is closely related to the classical news solution.
- Score: 0.1120974804429143
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fluid approximation is a widely used approach for solving two-stage stochastic optimization problems, with broad applications in service system design such as call centers and healthcare operations. However, replacing the underlying random distribution (e.g., demand distribution) with its mean (e.g., the time-varying average arrival rate) introduces bias in performance estimation and can lead to suboptimal decisions. In this paper, we investigate how to identify an alternative point statistic, which is not necessarily the mean, such that substituting this statistic into the two-stage optimization problem yields the optimal decision. We refer to this statistic as the decision-corrected point estimate (time-varying arrival rate). For a general service network with customer abandonment costs, we establish necessary and sufficient conditions for the existence of such a corrected point estimate and propose an algorithm for its computation. Under a decomposable network structure, we further show that the resulting decision-corrected point estimate is closely related to the classical newsvendor solution. Numerical experiments demonstrate the superiority of our decision-focused correction method compared to the traditional fluid approximation.
Related papers
- A Principled Approach to Randomized Selection under Uncertainty: Applications to Peer Review and Grant Funding [61.86327960322782]
We propose a principled framework for randomized decision-making based on interval estimates of the quality of each item.<n>We introduce MERIT, an optimization-based method that maximizes the worst-case expected number of top candidates selected.<n>We prove that MERIT satisfies desirable axiomatic properties not guaranteed by existing approaches.
arXiv Detail & Related papers (2025-06-23T19:59:30Z) - Treatment Effect Estimation for Optimal Decision-Making [65.30942348196443]
We study optimal decision-making based on two-stage CATE estimators.<n>We propose a novel two-stage learning objective that retargets the CATE to balance CATE estimation error and decision performance.
arXiv Detail & Related papers (2025-05-19T13:24:57Z) - Adaptive Resampling with Bootstrap for Noisy Multi-Objective Optimization Problems [0.0]
This paper presents a resampling decision function that incorporates the nature of the optimization problem by using bootstrapping and the probability of dominance.<n>The efficiency of this resampling approach is demonstrated by applying it in the NSGA-II algorithm with a sequential resampling procedure under multiple noise variations.
arXiv Detail & Related papers (2025-03-27T13:32:42Z) - Zeroth-Order Methods for Nonconvex Stochastic Problems with Decision-Dependent Distributions [8.90721241624138]
We consider an optimization problem with uncertainty dependent on decision variables.<n>In this problem, the gradient of the objective function cannot be obtained explicitly because the decision-dependent distribution is unknown.<n>Several zeroth-order methods have been proposed, which obtain noisy objective values by sampling and update the iterates.
arXiv Detail & Related papers (2024-12-29T03:05:34Z) - Optimal Baseline Corrections for Off-Policy Contextual Bandits [61.740094604552475]
We aim to learn decision policies that optimize an unbiased offline estimate of an online reward metric.
We propose a single framework built on their equivalence in learning scenarios.
Our framework enables us to characterize the variance-optimal unbiased estimator and provide a closed-form solution for it.
arXiv Detail & Related papers (2024-05-09T12:52:22Z) - Distributed Fractional Bayesian Learning for Adaptive Optimization [12.158466416574448]
This paper considers a distributed adaptive optimization problem, where all agents only have access to their local cost functions with a common unknown parameter.<n>We aim to provide valuable insights for addressing parameter uncertainty in distributed optimization problems and simultaneously find the optimal solution.
arXiv Detail & Related papers (2024-04-17T13:09:33Z) - Inference for an Algorithmic Fairness-Accuracy Frontier [0.7743097066308449]
We propose a debiased machine learning estimator for the fairness-accuracy frontier.<n>We derive its distribution and propose inference methods to test key hypotheses in the fairness literature.<n>We show that our approach yields alternative algorithms that lie on the fairness-accuracy frontier, offering improvements along both dimensions.
arXiv Detail & Related papers (2024-02-14T00:56:09Z) - Estimating Barycenters of Distributions with Neural Optimal Transport [93.28746685008093]
We propose a new scalable approach for solving the Wasserstein barycenter problem.
Our methodology is based on the recent Neural OT solver.
We also establish theoretical error bounds for our proposed approach.
arXiv Detail & Related papers (2024-02-06T09:17:07Z) - Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime [59.27851754647913]
Predictive optimization is the precise modeling of many real-world applications, including energy cost-aware scheduling and budget allocation on advertising.
We develop a modular framework to benchmark 11 existing PtO/PnO methods on 8 problems, including a new industrial dataset for advertising.
Our study shows that PnO approaches are better than PtO on 7 out of 8 benchmarks, but there is no silver bullet found for the specific design choices of PnO.
arXiv Detail & Related papers (2023-11-13T13:19:34Z) - Likelihood Ratio Confidence Sets for Sequential Decision Making [51.66638486226482]
We revisit the likelihood-based inference principle and propose to use likelihood ratios to construct valid confidence sequences.
Our method is especially suitable for problems with well-specified likelihoods.
We show how to provably choose the best sequence of estimators and shed light on connections to online convex optimization.
arXiv Detail & Related papers (2023-11-08T00:10:21Z) - Off-Policy Evaluation with Policy-Dependent Optimization Response [90.28758112893054]
We develop a new framework for off-policy evaluation with a textitpolicy-dependent linear optimization response.
We construct unbiased estimators for the policy-dependent estimand by a perturbation method.
We provide a general algorithm for optimizing causal interventions.
arXiv Detail & Related papers (2022-02-25T20:25:37Z) - Post-hoc loss-calibration for Bayesian neural networks [25.05373000435213]
We develop methods for correcting approximate posterior predictive distributions encouraging them to prefer high-utility decisions.
In contrast to previous work, our approach is agnostic to the choice of the approximate inference algorithm.
arXiv Detail & Related papers (2021-06-13T13:53:27Z) - Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting [41.94295877935867]
We present application-driven learning, a new closed-loop framework in which the processes of forecasting and decision-making are merged and co-optimized.
We show that the proposed methodology is scalable and yields consistently better performance than the standard open-loop approach.
arXiv Detail & Related papers (2021-02-26T02:43:28Z) - Distributed Averaging Methods for Randomized Second Order Optimization [54.51566432934556]
We consider distributed optimization problems where forming the Hessian is computationally challenging and communication is a bottleneck.
We develop unbiased parameter averaging methods for randomized second order optimization that employ sampling and sketching of the Hessian.
We also extend the framework of second order averaging methods to introduce an unbiased distributed optimization framework for heterogeneous computing systems.
arXiv Detail & Related papers (2020-02-16T09:01:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.