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..
Selfish Knapsack
Authors: Itai Feigenbaum, Matthew Johnson
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a randomized mechanism with attractive strategic properties: it has a price of anarchy of 2 for Bayes-Nash and coarse correlated equilibria. For overstating-only agents, it becomes strategyproof, and has a matching lower bound. For the case of two understating-only agents, we provide a specialized randomized strategyproof 5+4 2 7 1.522-approximate mechanism, and a lower bound of 5 5 9 2 1.09. When all agents but one are honest, we provide a deterministic strategyproof 1+ 5 2 1.618-approximate mechanism with a matching lower bound. The latter two mechanisms are also useful in problems beyond the one in consideration. |
| Researcher Affiliation | Academia | Itai Feigenbaum and Matthew P. Johnson Lehman College and the Graduate Center, City University of New York |
| Pseudocode | Yes | ALGORITHM 1: GREEDY Input: Sets of reported items R X n S , T i NRi while T = do next max T T T\{next} if s(S \{next}) 1 then S S \{next} else break return S |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It links to an online appendix for proofs and additional results, but not source code. |
| Open Datasets | No | This is a theoretical paper that focuses on algorithm design and mathematical analysis, not empirical studies using datasets for training or evaluation. Therefore, it does not provide information about publicly available datasets. |
| Dataset Splits | No | This is a theoretical paper that does not involve empirical experiments requiring dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper. It does not describe any empirical experiments that would require hardware specifications. |
| Software Dependencies | No | This is a theoretical paper that designs and analyzes mechanisms. It does not describe implementations of these mechanisms or empirical experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper focusing on mechanism design and analysis. It does not describe an experimental setup with hyperparameters or system-level training settings. |