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..
Low-Distortion Social Welfare Functions
Authors: Gerdus Benadè, Ariel D. Procaccia, Mingda Qiao1788-1795
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our computational results demonstrate that it is practical to compute deterministic distortion-minimizing rankings for instances with up to 10 alternatives. This constraint on the instance size is not unreasonable, as 98.3% of Robo Vote instances have 10 or fewer alternatives. For larger instances we test several heuristics and find that the Borda and Kemeny rules typically lead to low distortion and near-optimal social welfare. |
| Researcher Affiliation | Academia | Gerdus Benad e Tepper School of Business Carnegie Mellon University Ariel D. Procaccia Computer Science Department Carnegie Mellon University Mingda Qiao Computer Science Department Stanford University |
| Pseudocode | No | The paper describes the construction of its social welfare function in text (e.g., "Sort the alternatives into a ranking ν such that score(ν(1)) score(ν(2)) score(ν(m)). Let tmax = log2 m and α = m ln m. Draw t uniformly at random from [tmax] and set m t = min ( 2tα , m). With probability 1/2, return a uniformly random permutation of [m]. Otherwise, shuffle the first m t elements of ν uniformly at random, and return the resulting ordering.") but does not present it in a formally structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes a synthetic data generation process for its experiments rather than using a predefined public dataset, stating: "Every alternative a is assigned a quality ca, and ui({a}) is drawn from a truncated normal distribution around ca. Vector ui = (ui({a}))a [m] induces σi. Weights wi are drawn uniformly at random in [0, 1], and ordered." |
| Dataset Splits | No | The paper describes how the synthetic data was generated for experiments but does not provide specific details on train/validation/test splits, such as percentages or sample counts. |
| Hardware Specification | Yes | Runtime (in seconds) for increasing instance size, on an a machine with an Intel Core i5-4200U CPU and 8 GB RAM. |
| Software Dependencies | No | The paper mentions the use of Gurobi 8.0.0 for solving the optimization problem, but it does not specify versions for other general software components or libraries beyond this specialized solver. |
| Experiment Setup | No | The paper describes the generation of synthetic data and the problem formulation but does not provide specific hyperparameter values or detailed system-level training settings. |