Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fair and Efficient Allocations under Lexicographic Preferences
Authors: Hadi Hosseini, Sujoy Sikdar, Rohit Vaish, Lirong Xia5472-5480
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments We now revisit the non-existence result in Example 2 by examining how frequently {EF, EFX, EF1, MMS} + RM allocations exist in synthetically generated data. To that end, we consider a fixed number of agents (n = 5) whose preferences over a set of m goods (where m {5, . . . , 15}) are generated using the Mallows model (Mallows 1957). Given a reference ranking L and a dispersion parameter φ [0, 1], the probability of generating a ranking i L under the Mallows model is given by 1 Z φd( , i), where Z is a normalization constant and d( ) is the Kendall s Tau distance. Thus, φ = 0 induces identical preferences (i.e., i = ) while φ = 1 is the uniform distribution. For each combination of m, n, and φ {0, 0.25, 0.5, 0.75, 1}, we sample 1000 preference profiles, and use an integer linear program to check the existence of {EF, EFX, EF1, MMS}+ RM allocations. Code and data for all our experiments is available at https://github.com/sujoyksikdar/Envy-Freeness With-Lexicographic-Preferences. Figure 2 presents our experimental results. |
| Researcher Affiliation | Academia | Hadi Hosseini,1 Sujoy Sikdar,2 Rohit Vaish,3 Lirong Xia4 1 Pennsylvania State University, University Park, Pennsylvania, USA 16802 2 Binghamton University, Binghamton, New York 13902-6000 3 Tata Institute of Fundamental Research, Mumbai, India 400005 4 Rensselaer Polytechnic Institute, Troy, New York 12180 |
| Pseudocode | Yes | ALGORITHM 1: EFX+PO and ALGORITHM 2: Input: An instance N, M, with lexicographic preferences Parameters: A permutation σ : N N of the agents Output: An allocation A A ( , . . . , ) Execute one round of serial dictatorship according to σ. Assign all remaining goods to the last agent in σ. return A |
| Open Source Code | Yes | Code and data for all our experiments is available at https://github.com/sujoyksikdar/Envy-Freeness With-Lexicographic-Preferences. |
| Open Datasets | No | The paper uses |
| Dataset Splits | No | The paper mentions sampling |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models or cloud instance types used for running experiments. |
| Software Dependencies | No | The paper mentions using an 'integer linear program' but does not provide specific software dependencies or version numbers (e.g., solver names, programming language versions, or library versions). |
| Experiment Setup | Yes | For each combination of m, n, and φ {0, 0.25, 0.5, 0.75, 1}, we sample 1000 preference profiles, and use an integer linear program to check the existence of {EF, EFX, EF1, MMS}+ RM allocations. |