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].
Competition, Alignment, and Equilibria in Digital Marketplaces
Authors: Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To study this question from a theoretical perspective, we introduce a duopoly market where platform actions are bandit algorithms and the two platforms compete for user participation. Our work takes a step towards building a theoretical foundation for studying competition in digital marketplaces. |
| Researcher Affiliation | Academia | University of California, Berkeley EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not use or describe datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not describe dataset splits for validation. |
| Hardware Specification | No | This is a theoretical paper and does not mention specific hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not include details about an experimental setup or hyperparameters. |