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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |