Equilibrium of Data Markets with Externality

TL;DR

This paper models data markets as a game with externalities, analyzing equilibrium existence and welfare, and proposes interventions like transaction costs to improve outcomes, including in dynamic learning settings.

Contribution

It introduces a game-theoretic model of data markets with externalities, characterizes equilibrium properties, and proposes intervention strategies to enhance welfare, including in dynamic learning environments.

Findings

Pure Nash equilibria exist under certain externality functions.

Interventions via transaction costs can improve welfare outcomes.

Learning algorithms can achieve low regret in dynamic settings.

Abstract

We model real-world data markets, where sellers post fixed prices and buyers are free to purchase from any set of sellers, as a simultaneous game. A key component here is the negative externality buyers induce on one another due to data purchases. Starting with a simple setting where buyers know their valuations a priori, we characterize both the existence and welfare properties of the pure Nash equilibrium in the presence of such externality. While the outcomes are bleak without any intervention, mirroring the limitations of current data markets, we prove that for a standard class of externality functions, platforms intervening through a transaction cost can lead to a pure equilibrium with strong welfare guarantees. We next consider a more realistic setting where buyers learn their valuations over time through market interactions. Our intervention is feasible here as well, and we consider learning algorithms to achieve low regret concerning both individual and cumulative utility metrics. Lastly, we analyze the promises of this intervention under a much richer externality model.