Inverse Optimality for Fair Digital Twins: A Preference-based approach

• URL: http://arxiv.org/abs/2512.01650v2
• Date: Tue, 09 Dec 2025 09:01:08 GMT
• Title: Inverse Optimality for Fair Digital Twins: A Preference-based approach
• Authors: Daniele Masti, Francesco Basciani, Arianna Fedeli, Girgio Gnecco, Francesco Smarra,
• Abstract summary: This work proposes a framework that introduces fairness as a learnable objective within optimization-based Digital Twins.<n>A dedicated Siamese neural network is developed to generate convex quadratic cost functions conditioned on contextual information.<n>The effectiveness of the approach is demonstrated on a COVID-19 hospital resource allocation scenario.
• Score: 1.5756571514779074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital Twins (DTs) are increasingly used as autonomous decision-makers in complex socio-technical systems. However, their mathematically optimal decisions often diverge from human expectations, revealing a persistent mismatch between algorithmic and bounded human rationality. This work addresses this challenge by proposing a framework that introduces fairness as a learnable objective within optimization-based Digital Twins. In this respect, a preference-driven learning workflow that infers latent fairness objectives directly from human pairwise preferences over feasible decisions is introduced. A dedicated Siamese neural network is developed to generate convex quadratic cost functions conditioned on contextual information. The resulting surrogate objectives drive the optimization procedure toward solutions that better reflect human-perceived fairness while maintaining computational efficiency. The effectiveness of the approach is demonstrated on a COVID-19 hospital resource allocation scenario. Overall, this work offers a practical solution to integrate human-centered fairness into the design of autonomous decision-making systems.

Related papers

• Towards Human-AI Complementarity in Matching Tasks [18.703064369029022]
  We propose a data-driven algorithmic matching system that takes a collaborative approach.<n>Comatch selects only the decisions that it is the most confident in, deferring the rest to the human decision maker.<n>The results demonstrate that the matching outcomes produced by comatch outperform those generated by either human participants or by algorithmic matching on their own.
  arXiv  Detail & Related papers  (2025-08-18T18:02:45Z)
• Adaptive collaboration for online personalized distributed learning with heterogeneous clients [22.507916490976044]
  We study the problem of online personalized learning with $N$ statistically heterogeneous clients collaborating to accelerate local training.<n>An important challenge in this setting is to select relevant collaborators to reduce variance while mitigating the introduced bias.
  arXiv  Detail & Related papers  (2025-07-09T13:44:27Z)
• Online Decision-Focused Learning [74.3205104323777]
  Decision-focused learning (DFL) is an increasingly popular paradigm for training models whose predictive outputs are used in decision-making tasks.<n>In this paper, we regularize the objective function to make it different and investigate how to overcome nonoptimality function.<n>We also showcase the effectiveness of our algorithms on a knapsack experiment, where they outperform two standard benchmarks.
  arXiv  Detail & Related papers  (2025-05-19T10:40:30Z)
• Contextual Online Uncertainty-Aware Preference Learning for Human Feedback [13.478503755314344]
  Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm in artificial intelligence.<n>We propose a novel statistical framework to simultaneously conduct the online decision-making and statistical inference on the optimal model.<n>We apply the proposed framework to analyze the human preference data for ranking large language models on the Massive Multitask Language Understanding dataset.
  arXiv  Detail & Related papers  (2025-04-27T19:59:11Z)
• Towards Objective and Unbiased Decision Assessments with LLM-Enhanced Hierarchical Attention Networks [6.520709313101523]
  This work investigates cognitive bias identification in high-stake decision making process by human experts. We propose bias-aware AI-augmented workflow that surpass human judgment. In our experiments, both the proposed model and the agentic workflow significantly improves on both human judgment and alternative models.
  arXiv  Detail & Related papers  (2024-11-13T10:42:11Z)
• Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation [6.980546503227467]
  Operational decisions in healthcare, logistics, and public policy increasingly involve algorithms that recommend candidate solutions, while leaving the final choice to human decision-makers.<n>We propose generative curation, a framework that optimally generates recommendation sets when desirability depends on both observable objectives and unobserved qualitative considerations.<n>Our framework provides decision-makers with a principled way to design algorithms that complement, rather than replace, human judgment.
  arXiv  Detail & Related papers  (2024-09-17T20:13:32Z)
• Learning Constrained Optimization with Deep Augmented Lagrangian Methods [54.22290715244502]
  A machine learning (ML) model is trained to emulate a constrained optimization solver. This paper proposes an alternative approach, in which the ML model is trained to predict dual solution estimates directly. It enables an end-to-end training scheme is which the dual objective is as a loss function, and solution estimates toward primal feasibility, emulating a Dual Ascent method.
  arXiv  Detail & Related papers  (2024-03-06T04:43:22Z)
• MaxMin-RLHF: Alignment with Diverse Human Preferences [101.57443597426374]
  Reinforcement Learning from Human Feedback (RLHF) aligns language models to human preferences by employing a singular reward model derived from preference data.<n>We learn a mixture of preference distributions via an expectation-maximization algorithm to better represent diverse human preferences.<n>Our algorithm achieves an average improvement of more than 16% in win-rates over conventional RLHF algorithms.
  arXiv  Detail & Related papers  (2024-02-14T03:56:27Z)
• 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)
• Data-Efficient Interactive Multi-Objective Optimization Using ParEGO [6.042269506496206]
  Multi-objective optimization seeks to identify a set of non-dominated solutions that provide optimal trade-offs among competing objectives. In practical applications, decision-makers (DMs) will select a single solution that aligns with their preferences to be implemented. We propose two novel algorithms that efficiently locate the most preferred region of the Pareto front in expensive-to-evaluate problems.
  arXiv  Detail & Related papers  (2024-01-12T15:55:51Z)
• Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism [91.52263068880484]
  We study offline Reinforcement Learning with Human Feedback (RLHF) We aim to learn the human's underlying reward and the MDP's optimal policy from a set of trajectories induced by human choices. RLHF is challenging for multiple reasons: large state space but limited human feedback, the bounded rationality of human decisions, and the off-policy distribution shift.
  arXiv  Detail & Related papers  (2023-05-29T01:18:39Z)
• Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints [51.12047280149546]
  A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints. We formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints. We demonstrate the effectiveness of our approach on real-world data under different fairness metrics.
  arXiv  Detail & Related papers  (2022-12-23T22:29:08Z)
• Efficient Learning of Decision-Making Models: A Penalty Block Coordinate Descent Algorithm for Data-Driven Inverse Optimization [12.610576072466895]
  We consider the inverse problem where we use prior decision data to uncover the underlying decision-making process. This statistical learning problem is referred to as data-driven inverse optimization. We propose an efficient block coordinate descent-based algorithm to solve large problem instances.
  arXiv  Detail & Related papers  (2022-10-27T12:52:56Z)
• Human-AI Collaboration in Decision-Making: Beyond Learning to Defer [4.874780144224057]
  Human-AI collaboration (HAIC) in decision-making aims to create synergistic teaming between humans and AI systems. Learning to Defer (L2D) has been presented as a promising framework to determine who among humans and AI should take which decisions. L2D entails several often unfeasible requirements, such as availability of predictions from humans for every instance or ground-truth labels independent from said decision-makers.
  arXiv  Detail & Related papers  (2022-06-27T11:40:55Z)
• The Statistical Complexity of Interactive Decision Making [126.04974881555094]
  We provide a complexity measure, the Decision-Estimation Coefficient, that is proven to be both necessary and sufficient for sample-efficient interactive learning. A unified algorithm design principle, Estimation-to-Decisions (E2D), transforms any algorithm for supervised estimation into an online algorithm for decision making.
  arXiv  Detail & Related papers  (2021-12-27T02:53:44Z)
• 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)
• Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications [119.81868223344173]
  The paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications. Under this framework, we propose two algorithms that solve FSO resource allocation problems.
  arXiv  Detail & Related papers  (2020-07-27T17:38:51Z)
• Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [80.49176924360499]
  We establish a framework for directing a society of simple, specialized, self-interested agents to solve sequential decision problems. We derive a class of decentralized reinforcement learning algorithms. We demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.
  arXiv  Detail & Related papers  (2020-07-05T16:41:09Z)

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.