Towards Data Auctions with Externalities

The design of data markets has gained in importance as firms increasingly use predictions from machine learning models to make their operations more effective, yet need to externally acquire the necessary training data to fit such models. This is particularly true in the context of the Internet where an ever-increasing amount of user data is being collected and exchanged. A property of such markets that have been given limited consideration thus far is the externality faced by a firm when data is allocated to other, competing firms. Addressing this is likely necessary for progress towards the practical implementation of such markets.

In this work, we capture the problem of allocating and pricing multiple data sets within the framework of an auction of a single digital, or freely replicable, good. We study the resulting mechanism design problem in the presence of negative allocative externalities among bidding firms and obtain forms of the welfare-maximizing and revenue-maximizing auctions. We highlight how the form of the firms’ private information -- whether they know the externalities they exert on others or that others exert on them -- affects the structure of the optimal mechanisms. We find that in all cases, the resulting allocation rules are deterministic single thresholding functions for each firm.