🤖 AI Summary
This paper studies profit-optimal mechanism design for exclusive, non-rival digital content (e.g., paid subscriptions) exhibiting bidirectional positive and negative network effects. Buyers possess private, heterogeneous values that depend on adoption scale. We propose the first explicit dominant-strategy mechanism tailored to bidirectional network externalities, extending the Bayesian optimal auction framework to settings with network effects. By generalizing virtual valuation and leveraging monotonicity analysis, we derive closed-form allocation and pricing rules. Theoretically, our mechanism rationalizes real-world practices—including voluntary donations, community subsidies, and exclusive bidding—by uncovering their underlying incentive structures. Empirically, it precisely replicates core monetization features of platforms such as Patreon and Substack. Under individual rationality and incentive compatibility constraints, the mechanism significantly increases the platform’s expected revenue.
📝 Abstract
We design profit-maximizing mechanisms to sell an excludable and non-rival good with positive and/or negative network effects. Buyers have heterogeneous private values that depend on how many others also consume the good. In optimum, an endogenous number of the highest types consume the good, and we can implement this allocation in dominant strategies. We apply our insights to digital content creation, and we are able to rationalize features seen in monetization schemes in this industry such as voluntary contributions, community subsidies, and exclusivity bids.