论文标题
在社会学习和昂贵报告的情况下，最佳定价方案
Optimal Pricing Schemes in the Presence of Social Learning and Costly Reporting
论文作者
论文摘要
垄断平台将危险的产品（具有未知公用事业）或安全产品（带有已知实用程序）出售给依次到达并通过以前的代理商报告的风险产品的实用性的代理商。代理商报告公用事业是昂贵的；因此，平台必须设计价格和报告奖金，以激发代理商探索和生成新信息。通过允许卖方设定奖金，我们本质上是使他们能够动态控制学习信号的供应，而不会显着影响对产品的需求。我们表征了利润最大化平台提供的最佳奖金和定价方案。事实证明，最佳方案属于四种类型之一：全面覆盖，部分覆盖范围，即时启示和非统治。在指数匪徒的模型中，我们发现沿着学习轨迹的类型有动态切换。尽管学习有效地停止，但与计划者的最佳解决方案相比，信息的揭示得太慢。
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.
