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A framework for fair decision-making over time with time-invariant utilities

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  • Lodi, Andrea
  • Sankaranarayanan, Sriram
  • Wang, Guanyi

Abstract

Fairness is a major concern in contemporary decision problems. In these situations, the objective is to maximize fairness while preserving the efficacy of the underlying decision-making problem. This paper examines repeated decisions on problems involving multiple stakeholders and a central decision maker. Repetition of the decision-making provides additional opportunities to promote fairness while increasing the complexity from symmetry to finding solutions. This paper presents a general mathematical programming framework for the proposed fairness-over-time (FOT) decision-making problem. The framework includes a natural abstraction of how a stakeholder’s acquired utilities can be aggregated over time. In contrast with a natural, descriptive formulation, we demonstrate that if the aggregation function possesses certain basic properties, a strong reformulation can be written to remove symmetry from the problem, making it amenable to branch-and-cut solvers. Finally, we propose a particular relaxation of this reformulation that can assist in the construction of high-quality approximate solutions to the original problem and can be solved using simultaneous row and column generation techniques.

Suggested Citation

  • Lodi, Andrea & Sankaranarayanan, Sriram & Wang, Guanyi, 2024. "A framework for fair decision-making over time with time-invariant utilities," European Journal of Operational Research, Elsevier, vol. 319(2), pages 456-467.
  • Handle: RePEc:eee:ejores:v:319:y:2024:i:2:p:456-467
    DOI: 10.1016/j.ejor.2023.11.030
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    References listed on IDEAS

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    1. Luisa Montanari & Ulrike Schmidt-Kraepelin & Warut Suksompong & Nicholas Teh, 2022. "Weighted Envy-Freeness for Submodular Valuations," Papers 2209.06437, arXiv.org.
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    3. Dimitris Bertsimas & Vivek F. Farias & Nikolaos Trichakis, 2011. "The Price of Fairness," Operations Research, INFORMS, vol. 59(1), pages 17-31, February.
    4. Bailey Flanigan & Paul Gölz & Anupam Gupta & Brett Hennig & Ariel D. Procaccia, 2021. "Fair algorithms for selecting citizens’ assemblies," Nature, Nature, vol. 596(7873), pages 548-552, August.
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