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].
Ex-post IR Dynamic Auctions with Cost-per-Action Payments
Authors: Weiran Shen, Zihe Wang, Song Zuo
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Firstly, we formalize the credit accounts framework... guarantees approximate IC (see Definition 3.3) and ex-post IR (see Definition 2.4)... Finally, as a complement to the constructed credit account mechanisms, we show that the exact IC and the ex-post IR cannot be achieved simultaneously, unless the mechanism is trivial (Theorem 5.1). The third main result shows that DIC and EX-POST IR cannot be achieved simultaneously, unless the mechanism is trivial. |
| Researcher Affiliation | Academia | 1 Institute for Interdisciplinary Information Sciences, Tsinghua University 2 Institute for Theoretical Computer Science, Shanghai University of Finance |
| Pseudocode | Yes | Mechanism 2.1 (Abstract direct mechanism). For each period t [T]: 1. Each buyer has a private estimation F i t and reports an estimation ˆF i t to the seller;... (Similar structured steps for Mechanism 3.1 and 3.4) |
| Open Source Code | No | No statement explicitly providing or linking to the source code for the methodology described in this paper was found. |
| Open Datasets | No | This paper describes theoretical work and does not use or reference any publicly available datasets for training or other purposes. |
| Dataset Splits | No | This paper describes theoretical work and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | This paper describes theoretical work and does not mention any specific hardware specifications (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | This paper describes theoretical work and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | This paper describes theoretical work and does not include details about an experimental setup, such as hyperparameters or training configurations. |