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..
Online (Budgeted) Social Choice
Authors: Joel Oren, Brendan Lucier
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that no algorithm (even randomized) can achieve an approximation factor better than O( log log m log m ). In contrast, if the agents arrive in random order, we present a (1 1 e o(1))approximate algorithm, matching a lower bound for the offline problem. |
| Researcher Affiliation | Collaboration | Joel Oren University of Toronto, Canada EMAIL Brendan Lucier Microsoft Research, New England,USA EMAIL |
| Pseudocode | Yes | Algorithm 1: Online Candidate Selection Algorithm Input: Candidate set A, parameters k and n, online sequence of preference profiles 1 Let t n2/3(log n + k log m); 2 Observe the first t agents, T = {σ(1), . . . , σ(t)}; 3 S Greedy(T, k); 4 Choose all candidates in S and let the process run to completion; |
| Open Source Code | No | The paper does not mention or provide any links to open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe the use of any publicly available or open datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not specify any training/validation/test splits, as it does not involve empirical data. |
| Hardware Specification | No | The paper does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details about hyperparameters or system-level training settings, as it does not involve empirical experiments. |