In a two-stage data-sharing game, the unique equilibrium is either that the firm shares just enough data to stop the platform buying expert data, or that the firm shares an amount that maximizes its payoff while the platform buys all expert data.
Selling Information in Games with Externalities
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abstract
A competitive market is modeled as a game of incomplete information. One player observes some payoff-relevant state and can sell (possibly noisy) messages thereof to the other, whose willingness to pay is contingent on their own beliefs. We frame the decision of what information to sell, and at what price, as a product versioning problem. The optimal menu screens buyer types to maximize profit, which is the payment minus the externality induced by selling information to a competitor, that is, the cost of refining a competitor's beliefs. For a class of games with binary actions and states, we derive the following insights: (i) payments are necessary to provide incentives for information sharing amongst competing firms; (ii) the optimal menu benefits both the buyer and the seller; (iii) the seller cannot steer the buyer's actions at the expense of social welfare; (iv) as such, as competition grows fiercer it can be optimal to sell no information at all.
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Data Sharing with a Generative AI Competitor
In a two-stage data-sharing game, the unique equilibrium is either that the firm shares just enough data to stop the platform buying expert data, or that the firm shares an amount that maximizes its payoff while the platform buys all expert data.