Towards Data Auctions with Externalities

TL;DR

This paper explores the design of data auctions considering externalities among firms, proposing mechanisms that maximize revenue and welfare by modeling data utility through prediction accuracy improvements.

Contribution

It introduces a novel auction framework for data with externalities, reducing complex allocations to single-good auctions and analyzing the impact of private information on optimal mechanisms.

Findings

Optimal revenue increases when firms can pay to prevent data allocation to competitors.

The optimal allocation rule is a single threshold per firm, leading to all-or-nothing data distribution.

Mechanisms vary depending on whether externalities are known or private information.

Abstract

The design of data markets has gained importance as firms increasingly use machine learning models fueled by externally acquired training data. A key consideration is the externalities firms face when data, though inherently freely replicable, is allocated to competing firms. In this setting, we demonstrate that a data seller's optimal revenue increases as firms can pay to prevent allocations to others. To do so, we first reduce the combinatorial problem of allocating and pricing multiple datasets to the auction of a single digital good by modeling utility for data through the increase in prediction accuracy it provides. We then derive welfare and revenue maximizing mechanisms, highlighting how the form of firms' private information - whether the externalities one exerts on others is known, or vice-versa - affects the resulting structures. In all cases, under appropriate assumptions, the optimal allocation rule is a single threshold per firm, where either all data is allocated or none is.