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].
Spiteful Bidding in the Dollar Auction
Authors: Marcin Waniek, Agata Nieścieruk, Tomasz Michalak, Talal Rahwan
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Theorem 1. Let i be a non-spiteful player (αi = 0) who follows the strategy by O Neill [1986], and let player j be malicious (αj = 1). The optimal strategy of j is to bid: xj = xi + 1 if xi < b (s 1), b otherwise. |
| Researcher Affiliation | Academia | Marcin Waniek University of Warsaw EMAIL Agata Nie scieruk Polish-Japanese Academy of Information Technology EMAIL Tomasz Michalak University of Oxford and University of Warsaw EMAIL Talal Rahwan Masdar Institute of Science and Technology EMAIL |
| Pseudocode | No | The paper describes strategies and proofs in prose and through diagrams (e.g., Figure 1, Figure 2, Figure 3, Figure 4, Figure 5), but it does not contain explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with datasets, thus no information on dataset access is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve an experimental setup with hyperparameters or training configurations. |