Strategy-Proof and Non-Wasteful Multi-Unit Auction via Social Network
Authors: Takehiro Kawasaki, Nathanael Barrot, Seiji Takanashi, Taiki Todo, Makoto Yokoo2062-2069
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we first propose a mechanism that satisfies all the above properties. We then make a comprehensive comparison with two na ıve mechanisms, showing that the proposed mechanism dominates them in social surplus, seller s revenue, and incentive of buyers for truth-telling. We also analyze the characteristics of the social surplus and the revenue achieved by the proposed mechanism, including the constant approximability of the worst-case efficiency loss and the complexity of optimizing revenue from the seller s perspective. |
| Researcher Affiliation | Academia | 1Kyushu University, Japan, kawasaki@agent.inf.kyushu-u.ac.jp, {todo, yokoo}@inf.kyushu-u.ac.jp 2RIKEN AIP, Japan, nathanaelbarrot@gmail.com 3Kyoto University, Japan, s.takanashi1990@gmail.com |
| Pseudocode | Yes | Definition 6. Given θ , first order the connected buyers ˆN in ascending order of d( ), with arbitrary fixed tie-breaking. Note that d( ) is the distance from s in the original graph, not the distance in T(θ ). The order is called the priority order. For each i ˆN, fi(θ ) = ti(θ ) = 0. For each i ˆN, let ˆN i be the set of all connected buyers except i and its descendants in T(θ ). It then runs as follows: 1: k k, W 2: for each i ˆN selected in the order of do 3: pi v ( ˆN i \ W, k ) 4: if v i pi then 5: fi(θ ) 1, ti(θ ) pi 6: k k 1, W W {i} 7: else 8: fi(θ ) 0, ti(θ ) 0 9: end if 10: end for |
| Open Source Code | No | The paper states 'A full version is available at http://arxiv.org/abs/1911.08809' which links to an arXiv paper, not source code for the methodology. No other specific mention of code release is found. |
| Open Datasets | No | This paper is theoretical and does not conduct empirical studies using datasets, thus no information about public datasets for training is provided. |
| Dataset Splits | No | This paper is theoretical and does not perform experiments with data, so there are no specified training/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments, as it is a theoretical work. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not involve experimental setups, hyperparameters, or training configurations. |