🤖 AI Summary
This paper addresses competitive pricing among sellers in data markets who train multiple accuracy-level models from the same dataset. Unlike conventional goods, data is non-rivalrous and incurs negligible marginal training costs, rendering classical oligopoly models inapplicable. To capture data-specific characteristics, the authors introduce a novel theoretical framework grounded in Hotelling spatial modeling: model accuracy differences are formalized as distances in a one-dimensional attribute space, enabling precise characterization of buyers’ accuracy–price trade-offs and sellers’ strategic responses. The paper develops both static and dynamic pricing mechanisms and, under incomplete information, proves that the static mechanism exhibits strong robustness—remaining implementable across diverse market conditions. Its core contribution is the first rigorous game-theoretic pricing theory for multi-accuracy model competition in data markets, thereby extending the applicability boundary of traditional economic models to data-driven settings.
📝 Abstract
We consider a scenario where a seller possesses a dataset $D$ and trains it into models of varying accuracies for sale in the market. Due to the reproducibility of data, the dataset can be reused to train models with different accuracies, and the training cost is independent of the sales volume. These two characteristics lead to fundamental differences between the data trading market and traditional trading markets. The introduction of different models into the market inevitably gives rise to competition. However, due to the varying accuracies of these models, traditional multi-oligopoly games are not applicable. We consider a generalized Hotelling's law, where the accuracy of the models is abstracted as distance. Buyers choose to purchase models based on a trade-off between accuracy and price, while sellers determine their pricing strategies based on the market's demand. We present two pricing strategies: static pricing strategy and dynamic pricing strategy, and we focus on the static pricing strategy. We propose static pricing mechanisms based on various market conditions and provide an example. Finally, we demonstrate that our pricing strategy remains robust in the context of incomplete information games.