Equilibrium Characterization for Data Acquisition Games
Authors: Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael Kearns, Zachary Schutzman
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study a game between two firms in which each provides a service based on machine learning. The firms are presented with the opportunity to purchase a new corpus of data, which will allow them to potentially improve the quality of their products. The firms can decide whether or not they want to buy the data, as well as which learning model to build with that data. We demonstrate a reduction from this potentially complicated action space to a one-shot, two-action game in which each firm only decides whether or not to buy the data. The game admits several regimes which depend on the relative strength of the two firms at the outset and the price at which the data is being offered. We analyze the game s Nash equilibria in all parameter regimes and demonstrate that, in expectation, the outcome of the game is that the initially stronger firm s market position weakens whereas the initially weaker firm s market position becomes stronger. Finally, we consider the perspective of the users of the service and demonstrate that the expected outcome at equilibrium is not the one which maximizes the welfare of the consumers. |
| Researcher Affiliation | Academia | University of Pennsylvania {jinshuo, hads}@sas.upenn.edu, {jabbari, mkearns, ianzach}@cis.upenn.edu |
| Pseudocode | No | The paper does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper provides a link to a 'full technical version' (a PDF document), not to any source code repository for the described methodology. |
| Open Datasets | No | This is a theoretical paper that does not involve training models on datasets. No specific dataset is mentioned as being used for training. |
| Dataset Splits | No | This is a theoretical paper that does not involve experimental validation on datasets. No training/test/validation splits are mentioned. |
| Hardware Specification | No | This is a theoretical paper that does not describe empirical experiments requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper and does not mention any specific software dependencies or versions for implementation. |
| Experiment Setup | No | This is a theoretical paper that analyzes a game. It does not involve experimental setup details such as hyperparameters or system-level training settings. |