Pricing Strategies for Different Accuracy Models from the Same Dataset Based on Generalized Hotelling's Law
This addresses pricing challenges for sellers in data markets where models compete based on accuracy, but it is incremental as it adapts existing economic models to a new context.
The paper tackles the problem of pricing models with varying accuracies derived from the same dataset, using a generalized Hotelling's law to abstract accuracy as distance, and proposes static and dynamic pricing strategies, showing robustness in incomplete information games.
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.