{"type":"ScholarlyArticle","title":"Equilibrium Characterization for Data Acquisition Games","authors":[{"name":"Jinshuo Dong","slug":"jinshuo-dong","given":"Jinshuo","family":"Dong","affiliation":"University of Pennsylvania"},{"name":"Hadi Elzayn","slug":"hadi-elzayn","given":"Hadi","family":"Elzayn","affiliation":"University of Pennsylvania"},{"name":"Shahin Jabbari","slug":"shahin-jabbari","given":"Shahin","family":"Jabbari","affiliation":"University of Pennsylvania"},{"name":"Michael Kearns","slug":"michael-kearns","given":"Michael","family":"Kearns","affiliation":"University of Pennsylvania"},{"name":"Zachary Schutzman","slug":"zachary-schutzman","given":"Zachary","family":"Schutzman","affiliation":"University of Pennsylvania"}],"abstract":"We study a game between two firms which each provide 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 on 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.","doi":"10.24963/ijcai.2019/36","url":"https://ijcai.org/proceedings/2019/36","pdf":"https://ijcai.org/proceedings/2019/0036.pdf","markdown":"https://ijcai.org/proceedings/2019/36.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/36","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":36,"pages":{"first":252,"last":258},"keywords":["Agent-based and Multi-agent Systems: Noncooperative Games","Agent-based and Multi-agent Systems: Algorithmic Game Theory","Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems"],"videos":[],"references":[{"position":1,"raw":"[Barron, 1994] Andrew Barron. 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