Aligning Learning and Endogenous Decision-Making

• URL: http://arxiv.org/abs/2507.00851v1
• Date: Tue, 01 Jul 2025 15:22:56 GMT
• Title: Aligning Learning and Endogenous Decision-Making
• Authors: Rares Cristian, Pavithra Harsha, Georgia Perakis, Brian Quanz,
• Abstract summary: We introduce an end-to-end method under endogenous uncertainty to train ML models to be aware of their downstream.<n>We also introduce a robust optimization variant that accounts for uncertainty in ML models.<n>We prove guarantees that this robust approach can capture near-optimal decisions with high probability as a function of data.
• Score: 5.84228364962637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many of the observations we make are biased by our decisions. For instance, the demand of items is impacted by the prices set, and online checkout choices are influenced by the assortments presented. The challenge in decision-making under this setting is the lack of counterfactual information, and the need to learn it instead. We introduce an end-to-end method under endogenous uncertainty to train ML models to be aware of their downstream, enabling their effective use in the decision-making stage. We further introduce a robust optimization variant that accounts for uncertainty in ML models -- specifically by constructing uncertainty sets over the space of ML models and optimizing actions to protect against worst-case predictions. We prove guarantees that this robust approach can capture near-optimal decisions with high probability as a function of data. Besides this, we also introduce a new class of two-stage stochastic optimization problems to the end-to-end learning framework that can now be addressed through our framework. Here, the first stage is an information-gathering problem to decide which random variable to poll and gain information about before making a second-stage decision based off of it. We present several computational experiments for pricing and inventory assortment/recommendation problems. We compare against existing methods in online learning/bandits/offline reinforcement learning and show our approach has consistent improved performance over these. Just as in the endogenous setting, the model's prediction also depends on the first-stage decision made. While this decision does not affect the random variable in this setting, it does affect the correct point forecast that should be made.

Related papers

• A Principled Approach to Randomized Selection under Uncertainty: Applications to Peer Review and Grant Funding [68.43987626137512]
  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)
• Online Decision-Focused Learning [63.83903681295497]
  Decision-focused learning (DFL) is an increasingly popular paradigm for training predictive models whose outputs are used in decision-making tasks.<n>We investigate DFL in dynamic environments where the objective function does not evolve over time.<n>We establish bounds on the expected dynamic regret, both when decision space is a simplex and when it is a general bounded convex polytope.
  arXiv  Detail & Related papers  (2025-05-19T10:40:30Z)
• Sufficient Decision Proxies for Decision-Focused Learning [2.7143637678944454]
  Decision-focused learning aims at learning a predictive model such that decision quality, instead of prediction accuracy, is maximized.<n>This paper investigates for the first time problem properties that justify using either assumption.<n>We show the effectiveness of presented approaches in experiments on problems with continuous and discrete variables, as well as uncertainty in the objective function and in the constraints.
  arXiv  Detail & Related papers  (2025-05-06T20:10:17Z)
• Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks [4.202961704179733]
  In many real-world settings, some of these parameters are unknown or uncertain. Recent research focuses on predicting the value of unknown parameters using available contextual features. We propose a novel framework that models uncertainty Neural Networks (BNNs) and propagates this uncertainty into the mathematical solver.
  arXiv  Detail & Related papers  (2024-06-05T09:11:46Z)
• Overcoming Overconfidence for Active Learning [1.2776312584227847]
  We present two novel methods to address the problem of overconfidence that arises in the active learning scenario. The first is an augmentation strategy named Cross-Mix-and-Mix (CMaM), which aims to calibrate the model by expanding the limited training distribution. The second is a selection strategy named Ranked Margin Sampling (RankedMS), which prevents choosing data that leads to overly confident predictions.
  arXiv  Detail & Related papers  (2023-08-21T09:04:54Z)
• Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize [57.22851616806617]
  We show that our method achieves state-of-the-art results in four domains from the literature. Our approach outperforms the best existing method by nearly 200% when the localness assumption is broken.
  arXiv  Detail & Related papers  (2023-05-26T11:17:45Z)
• Limitations of a proposed correction for slow drifts in decision criterion [0.0]
  We propose a model-based approach for disambiguating systematic updates from random drifts. We show that this approach accurately recovers the latent trajectory of drifts in decision criterion. Our results highlight the advantages of incorporating assumptions about the generative process directly into models of decision-making.
  arXiv  Detail & Related papers  (2022-05-22T19:33:19Z)
• Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning [52.74071439183113]
  We study the predict-then-optimize framework in the context of sequential decision problems (formulated as MDPs) solved via reinforcement learning. Two significant computational challenges arise in applying decision-focused learning to MDPs.
  arXiv  Detail & Related papers  (2021-06-06T23:53:31Z)
• Modeling the Second Player in Distributionally Robust Optimization [90.25995710696425]
  We argue for the use of neural generative models to characterize the worst-case distribution. This approach poses a number of implementation and optimization challenges. We find that the proposed approach yields models that are more robust than comparable baselines.
  arXiv  Detail & Related papers  (2021-03-18T14:26:26Z)
• Diffusion Approximations for a Class of Sequential Testing Problems [0.0]
  We study the problem of a seller who wants to select an optimal assortment of products to launch into the marketplace. Motivated by emerging practices in e-commerce, we assume that the seller is able to use a crowdvoting system to learn these preferences.
  arXiv  Detail & Related papers  (2021-02-13T23:21:29Z)
• Stein Variational Model Predictive Control [130.60527864489168]
  Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when dealing with complex distributions. We show that this framework leads to successful planning in challenging, non optimal control problems.
  arXiv  Detail & Related papers  (2020-11-15T22:36:59Z)

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.