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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Possible and Necessary Allocations via Sequential Mechanisms
Authors: Haris Aziz, Toby Walsh, Lirong Xia
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 16,203 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,037 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present characterizations of the allocations that result respectively from the classes, which extend the well-known characterization by Brams and King [2005] for policies without restrictions. In addition, we examine the computational complexity of possible and necessary allocation problems for these classes. |
| Researcher Affiliation | Academia | Haris Aziz NICTA and UNSW, Sydney 2033, Australia EMAIL Toby Walsh NICTA and UNSW, Sydney 2033, Australia EMAIL Lirong Xia RPI NY 12180, USA EMAIL |
| Pseudocode | No | The paper describes algorithms in prose (e.g., mentioning 'Algorithm 1') but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve training models on datasets; therefore, it does not provide information about publicly available or open datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments requiring dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on computational complexity analysis, not empirical experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not involve empirical experiments requiring software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments requiring details about the experimental setup such as hyperparameters or training settings. |