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 [1].
Bribery and Control in Stable Marriage
Authors: Niclas Boehmer, Robert Bredereck, Klaus Heeger, Rolf Niedermeier
JAIR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, it might also be interesting to analyze the power of different manipulative actions in real-world scenarios. We already did some very preliminary experiments for all polynomial-computable cases on synthetic data having between 30 and 200 agents, where the preferences of agents were drawn uniformly at random from all possible preferences. The manipulation goal was also set uniformly at random. |
| Researcher Affiliation | Academia | Niclas Boehmer EMAIL Technische Universität Berlin, Algorithmics and Computational Complexity, Berlin, Germany Robert Bredereck EMAIL Technische Universität Berlin, Algorithmics and Computational Complexity, Berlin, Germany, and Humboldt-Universität zu Berlin, Institut für Informatik, Berlin, Germany Klaus Heeger EMAIL Rolf Niedermeier EMAIL Technische Universität Berlin, Algorithmics and Computational Complexity, Berlin, Germany |
| Pseudocode | Yes | Data: An SM instance I, a complete matching M , a budget â„“, and two sets Uadd and Wadd. 1 Set XA := {w Wadd : m U \ Uadd with M (u) = w}; 2 while there exists a lonely woman w Worig XA and some man m Uorig XA with w m M (m) do 3 Add M (w) to XA; 4 if M |Uorig Worig XA is stable and |XA| â„“then 5 return XA; 7 return False; Algorithm 1: Linear-time algorithm for Exact-Exists-Add. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | No | The paper mentions preliminary experiments on "synthetic data" but does not provide concrete access information (link, DOI, repository, citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions preliminary experiments on "synthetic data" but does not provide any specific dataset split information (percentages, sample counts, or detailed splitting methodology). |
| Hardware Specification | No | The paper mentions "preliminary experiments" but does not provide any specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running them. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers used for replicating the experiments or methodology described. |
| Experiment Setup | No | The paper mentions "preliminary experiments for all polynomial-computable cases on synthetic data having between 30 and 200 agents, where the preferences of agents were drawn uniformly at random from all possible preferences. The manipulation goal was also set uniformly at random." However, it does not provide specific experimental setup details such as concrete hyperparameter values or training configurations. |