Equilibrium of Data Markets with Externality
This addresses inefficiencies in real-world data markets for platforms and participants, offering a theoretical intervention to improve welfare, though it is incremental as it builds on existing game-theoretic frameworks.
The paper models data markets as a game with negative externalities from data purchases, showing that without intervention, equilibria have poor welfare, but introducing a transaction cost can ensure a pure Nash equilibrium with strong welfare guarantees for standard externality functions, and extends this to settings with learning buyers and richer externality models.
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.