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A monopoly platform sells either a risky product (with unknown utility) or asafe product (with known utility) to agents who sequentially arrive and learnthe utility of the risky product by the reporting of previous agents. It iscostly for agents to report utility; hence the platform has to design both theprices and the reporting bonus to motivate the agents to explore and generatenew information. By allowing sellers to set bonuses, we are essentiallyenabling them to dynamically control the supply of learning signals withoutsignificantly affecting the demand for the product. We characterize the optimalbonus and pricing schemes offered by the profit-maximizing platform. It turnsout that the optimal scheme falls into one of four types: Full Coverage,Partial Coverage, Immediate Revelation, and Non-Bonus. In a model ofexponential bandit, we find that there is a dynamical switch of the types alongthe learning trajectory. Although learning stops efficiently, information isrevealed too slowly compared with the planner's optimal solution.

Optimal Pricing Schemes in the Presence of Social Learning and Costly Reporting