On Social Envy-Freeness in Multi-Unit Markets
Authors: Michele Flammini, Manuel Mauro, Matteo Tonelli
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For all the above cases we show the hardness of the revenue maximization problem and give corresponding approximation results. All our approximation bounds are optimal or nearly optimal. Moreover, we provide an optimal allocation algorithm for general valuations with item-pricing, under the assumption of social graphs of bounded treewidth. Finally, we determine optimal bounds on the corresponding price of envy-freeness... |
| Researcher Affiliation | Academia | Gran Sasso Science Institute, L Aquila, Italy University of L Aquila, L Aquila, Italy michele.flammini@univaq.it, manuel.mauro@gssi.it, matteo.tonelli@gssi.it |
| Pseudocode | No | The paper describes algorithms verbally and through mathematical formulations but does not include any explicitly labeled "Pseudocode" or "Algorithm" blocks, or code-like structured steps. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe the use of datasets for training or evaluation, nor does it provide concrete access information for a publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |