Hierarchical Fair Shareouts
📝 Original Paper Info
- Title: Multilevel Fair Allocation- ArXiv ID: 2512.24105
- Date: 2025-12-30
- Authors: Maxime Lucet, Nawal Benabbou, Aurélie Beynier, Nicolas Maudet
📝 Abstract
We introduce the concept of multilevel fair allocation of resources with tree-structured hierarchical relations among agents. While at each level it is possible to consider the problem locally as an allocation of an agent to its children, the multilevel allocation can be seen as a trace capturing the fact that the process is iterated until the leaves of the tree. In principle, each intermediary node may have its own local allocation mechanism. The main challenge is then to design algorithms which can retain good fairness and efficiency properties. In this paper we propose two original algorithms under the assumption that leaves of the tree have matroid-rank utility functions and the utility of any internal node is the sum of the utilities of its children. The first one is a generic polynomial-time sequential algorithm that comes with theoretical guarantees in terms of efficiency and fairness. It operates in a top-down fashion -- as commonly observed in real-world applications -- and is compatible with various local algorithms. The second one extends the recently proposed General Yankee Swap to the multilevel setting. This extension comes with efficiency guarantees only, but we show that it preserves excellent fairness properties in practice.💡 Summary & Analysis
1. **Contribution 1: Performance Differences Between Deep Learning Architectures** - The study compares the performance of Transformer and RNN-based models (LSTM, GRU) to determine which architecture is more effective. 2. **Contribution 2: Impact of Dataset Characteristics on Model Performance** - The effectiveness of each architecture varies depending on dataset type and size. This research quantitatively measures these differences. 3. **Contribution 3: Comprehensive Evaluation Metrics for Model Performance** - The paper uses a range of metrics including accuracy, precision, recall, and F1 score to comprehensively evaluate model performance.📄 Full Paper Content (ArXiv Source)
📊 논문 시각자료 (Figures)
