Fair and Efficient Allocations under Lexicographic Preferences

Authors: Hadi Hosseini, Sujoy Sikdar, Rohit Vaish, Lirong Xia5472-5480

AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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.