Abstract: A recommendation platform sequentially collects information about a new product revealed from past consumer trials and uses it to better guide later consumers. Because consumers do not internalize the value of information they bring to others, their incentive for trying out the product can be socially insufficient. Given such a challenge, I study how the platform can improve social welfare by designing its recommendation policy. In a model with binary product quality and general trial-generated signals, I find that the optimal design features a U-shaped sequence of recommendation standards over the product's life, and the optimal learning dynamic can involve temporary suspensions following negative consumer feedback when the product is young. Various extensions and comparative statics regarding the optimal recommendation standards are provided. My analysis also illustrates the usefulness of a Lagrangian duality approach for dynamic information design. 

Abstract: I study optimal information provision by a search goods seller. While the seller controls a consumer's pre-search information, he cannot control post-search information because the consumer will inevitably learn the product's match after search. A relaxed problem approach is developed to solve the optimal design, which accommodates both continuous value distributions and ex-ante heterogeneous consumers with privately known outside options. The optimal design is shown to crucially depend on the outside option value distribution, and can be implemented by a simple upper-censorship signal under certain regularity conditions. Several applications are provided, including comparing information designs of search goods and experience goods, and studying the effect of competition with a large number of sellers.

Abstract: This paper studies optimal disclosure regulation for entrepreneur public financing with a post-financing moral hazard problem. I show that partial disclosure can improve social welfare over full disclosure through reducing efficiency loss caused by the moral hazard problem. As a result, a properly designed partial disclosure rule would be optimal without assuming any disclosure cost. This remains true after allowing for endogenous entrepreneur types with adverse selection concerns. With (constrained) Bayesian persuasion tools, the optimal disclosure rule is fully characterized. Although the paper is developed mainly around entrepreneur equity financing, its intuition can be more generally applicable. For instance, I also adapt the basic model to debt financing and provide an application to banking system disclosure.

Abstract: Technological developments have spurred interest in innovation of multi-asset market-clearing mechanisms. This paper examines the corresponding design problem in a family of designs with rich cross-asset demand conditioning. We show that a suitably designed market-clearing technology can improve efficiency relative to the independent clearing of all assets. We also identify a subclass of designs that can be implemented as dynamic designs that do not require an investment in technology that clears multiple assets simultaneously. Efficient designs may involve withholding or delayed disclosure of price data in their dynamic implementations. Our results help to determine in what markets investments in different market clearing technologies are worthwhile.

Abstract: Futures contracts have been highly successful financial securities, with substantial trading volumes and active underlying asset markets. In a dynamic market framework, we show that imperfect competition in future spot markets motivates trades in futures contracts in earlier periods. The introduction of futures contracts can improve the risk sharing among traders and thus their equilibrium payoffs by mitigating the liquidity distortion due to cross-round price inference and allowing traders to overcome the winner's curse.