Research map / Learning theory and algorithms / Fairness
Fast & Fair: A Collaborative Platform for Fair Division Applications
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Presents Fast & Fair, an open-source collaborative Streamlit web platform hosting three fair division algorithms (weighted picking sequence, fair two-sided matching, envy-free house assignment) with explainable outcomes and a community contribution pipeline.
Problem
Fair division algorithms from the theoretical literature remain mostly confined to research papers and are inaccessible to the public, with existing platforms implementing only a handful of algorithms.
Approach
An open-source, collaborative web platform where the community can contribute new fair allocation algorithms via a code template generator, with standardized input widgets and explanations of fairness properties for outcomes.
Key results
- Fast & Fair currently implements three fair division applications: weighted picking sequence (WEF(x,1-x)), fair two-sided matching (EF[1,1], balanced, swap stable), and envy-free house assignment.
- The platform provides a code template generator so community members can contribute new algorithm implementations, which developers review before integration.
Limitations
- Only three algorithms implemented so far; the authors cannot implement a significant fraction of the literature themselves.
- Contributions require developer review before integration into the platform.
Experimental setup
Metrics: running time
Baselines: Spliddit Kajibuntan
Builds on: Spliddit Kajibuntan
Closest papers by meaning
- Mixed Fair Division: A Survey (AAAI 2024 · similarity 0.89)
- Several Stories about High-Multiplicity EFx Allocation (Student Abstract) (AAAI 2024 · similarity 0.87)
- Online Fair Division Meets Reordering Buffers (NeurIPS 2026 · similarity 0.86)
- Temporal Fair Division (AAAI 2025 · similarity 0.86)
- Almost Envy-Free Allocations of Indivisible Goods or Chores with Entitlements (AAAI 2024 · similarity 0.85)
- Fair Allocation of Items in Multiple Regions (AAAI 2024 · similarity 0.85)
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