Conference proceeding
Equilibrium Characterization for Data Acquisition Games
PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, pp.252-258
01 Jan 2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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Details
- Title
- Equilibrium Characterization for Data Acquisition Games
- Creators
- Jinshuo Dong - Univ Penn, Philadelphia, PA 19104 USAHadi Elzayn - Univ Penn, Philadelphia, PA 19104 USAShahin Jabbari - Univ Penn, Philadelphia, PA 19104 USAMichael Kearns - Univ Penn, Philadelphia, PA 19104 USAZachary Schutzman - Univ Penn, Philadelphia, PA 19104 USA
- Contributors
- S Kraus (Editor)
- Publication Details
- PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, pp.252-258
- Conference
- TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 28th
- Publisher
- Ijcai-Int Joint Conf Artif Intell
- Number of pages
- 7
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Identifiers
- 991021868088704721
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- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods