Equilibrium Characterization for Data Acquisition Games
This addresses strategic data acquisition in competitive markets for firms using ML, but it is incremental as it builds on existing game theory models.
The paper tackles the problem of how two competing firms decide to purchase new data for machine learning services, showing that the game reduces to a simple buy-or-not decision and leads to the initially stronger firm weakening while the weaker one strengthens, with consumer welfare not maximized at equilibrium.
We study a game between two firms in 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 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.