{"ID":2834498,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.01650","arxiv_id":"2512.01650","title":"Inverse Optimality for Fair Digital Twins: A Preference-based approach","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.","short_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 prop...","url_abs":"https://arxiv.org/abs/2512.01650","url_pdf":"https://arxiv.org/pdf/2512.01650v2","authors":"[\"Daniele Masti\",\"Francesco Basciani\",\"Arianna Fedeli\",\"Girgio Gnecco\",\"Francesco Smarra\"]","published":"2025-12-01T13:23:27Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.SE\",\"math.OC\"]","methods":"[]","has_code":false}