**(with Wing Suen and Yimeng Zhang)**

__Coarse Information Design__*Another new version will be coming soon!!*

An alternative explanation for the dual expectation can be

*found*We study an information design problem with continuous state and discrete signal space. Under convex value functions, the optimal information structure is interval-partitional and exhibits a dual expectations property: each induced signal is the conditional mean (taken under the prior density) of each interval; each interval cutoff is the conditional mean (taken under the value function curvature) of the interval formed by neighbouring signals. This property enables examination into which part of the state space is more finely partitioned and facilitates comparative statics analysis. The analysis can be extended to general value functions and adapted to study coarse mechanism design.

__here!__

__Optimal Refund Mechanism__*R&R at RAND*

Abstract: This paper studies the optimal refund mechanism when an uninformed buyer can privately acquire information about his valuation over time. In principle, a refund mechanism can specify the odds that the seller requires the product returned while issuing a (partial) refund, which we call stochastic return. It guarantees the seller a strictly positive minimum revenue and facilitates intermediate buyer learning. However, our main result shows that stochastic return is always sub-optimal. The optimal refund mechanism takes simple forms: the seller either deters learning via a well-designed non-refundable price or encourages full learning via free return. Moreover, the optimal mechanism has the same structure for both positive learning and negative learning framework.

**Information Design in Cheap Talk****(with Wing Suen)**

*A new version coming soon!*

You can check the

__old version__here.

An uninformed sender publicly commits to an informative experiment about an uncertain state, privately observes its outcome, and sends a cheap-talk message to a receiver. We provide an algorithm valid for arbitrary state-dependent preferences that will determine the sender's optimal experiment, and give sufficient conditions for information design to be valuable or not under different payoff structures. These conditions depend more on marginal incentives---how payoffs vary with the state---than on the alignment of sender's and receiver's rankings over actions within a state.

**Archived**

**(with Zheng Gong)**

__Optimal Experimentation Design with Secret Repetition__Abstract: We study a persuasion game with limited commitment in which a biased sender designs and conducts costly experiments to acquire information which he can conceal or reveal. The sender commits to the experiment design, but he can secretly repeat experiments and selectively report the outcomes. In the benchmark model, the optimal experiment turns out to be a one-round experiment and the sender truthfully discloses the experiment outcome. The cost of an experiment is a measure of credibility. Higher credibility leads to less informative experiment which lowers the receiver's payoff. With general payoff function of the sender, the above results remain with mild restrictions. We geometrically characterize the optimal experiment using the same concavification with Kamenica and Gentzkow (2011) but within a refined belief space.