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

TL;DR

This paper studies how a monopoly platform can optimally set prices and reporting bonuses to incentivize agents to explore and share information about a risky product, balancing learning speed and profit.

Contribution

It introduces a dynamic bonus scheme framework for optimal pricing and reporting incentives, identifying four main optimal strategies and analyzing their behavior over time.

Findings

Optimal bonus and pricing schemes are classified into four types.

Dynamic switching occurs among scheme types during the learning process.

Learning is efficient but slower than the planner’s optimal solution.

Abstract

A monopoly platform sells either a risky product (with unknown utility) or a safe product (with known utility) to agents who sequentially arrive and learn the utility of the risky product by the reporting of previous agents. It is costly for agents to report utility; hence the platform has to design both the prices and the reporting bonus to motivate the agents to explore and generate new information. By allowing sellers to set bonuses, we are essentially enabling them to dynamically control the supply of learning signals without significantly affecting the demand for the product. We characterize the optimal bonus and pricing schemes offered by the profit-maximizing platform. It turns out that the optimal scheme falls into one of four types: Full Coverage, Partial Coverage, Immediate Revelation, and Non-Bonus. In a model of exponential bandit, we find that there is a dynamical switch of the types along the learning trajectory. Although learning stops efficiently, information is revealed too slowly compared with the planner's optimal solution.