Fanqi Shi (Eric)
Job Market Candidate

Stanford University
Department of Economics
579 Serra Mall
Stanford, CA 94305
650-796-5253
ericshi@stanford.edu

Curriculum Vitae

Fields:
Microeconomic Theory, Market Design

Expected Graduation Date:
June, 2019

Thesis Committee:
Fuhito Kojima (Primary):
fkojima@stanford.edu

Ilya Segal:
isegal@stanford.edu

Matthew O. Jackson:
jacksonm@stanford.edu

Alvin Roth:
alroth@stanford.edu

Working Papers

Screening with Network Externalities (Job Market Paper, with Yiqing Xing)

We develop a model in which a profit-maximizing monopoly sells a product with positive network externalities and optimally screen buyers based on their influence and susceptibility. We characterize the optimal allocation for both the case of directed networks where each buyer's influence and susceptibility are independent, and the case of undirected networks where the two are identical. In the case of directed networks, we show the optimal allocation can only depend on a buyer's susceptibility and linear in virtual type (susceptibility) with quadratic intrinsic value. In the case of undirected networks, we disentangle the different effects of influence and susceptibility on optimal allocation and show with quadratic intrinsic value, the allocation is a linear combination of a buyer's type and virtual type. Then we contrast the model with complete information pricing and pure screening and show that apart from the screening effects, positive network externalities increase each buyer's allocation at the optimal selling mechanism. We also extend the model to accommodate for weak positive affiliation between a buyer's influence and susceptibility, and the situation where influence and susceptibility are endogenous to the optimal allocation.

Which to Sell First? Optimal Ordering of Heterogeneous Items in Sequential Auctions (with Yiqing Xing)

We study the optimal ordering of heterogeneous items in sequential auctions with unit-demand buyers. The valuation of each item depends on a buyer's private type and an item-specific characteristic (e.g. quality). We consider two settings: (1) "generalized vertical differentiation" (i.e. valuations of all items increasing in buyers' types); and (2) horizontal differentiation (i.e. valuations of two items moving in opposite directions in types). In the first setting, it is optimal to sell items in decreasing level of quality: it achieves full efficiency if valuations exhibit strict increasing differences (SID) in item quality and buyers' types; and in addition, it maximizes revenue among all mechanisms that satisfy BIC, IIR, and an all-sold condition, if (SID) also holds for buyers' virtual values. By contrast, in the second case, ordering does not matter: either ordering delivers full efficiency and the same revenue to the seller. As a generalization, we extend our insights on efficient ordering to a third setting that combines both vertical and horizontal differentiations. Our analysis provides justification for employing sequential auctions in the sale of multiple items based on efficiency and optimal revenue.

Dynamic Matching with One-sided Incomplete Information in Investment (slides attached, updated draft coming)

I study a two-period matching model where one-side of the market has an option to invest and delay matching in the first period. Investment increases each agent's match value in the second period, and the increase is proportional to the agent's investment ability in a match pair. Assuming each agent's investment ability is her private information, I define the set of "sequentially stable" matchings. I show the set of sequentially stable matchings is a superset of the complete information stable matchings and efficient investment is always induced in any such matching. Moreover, when there is high uncertainty in each agent's investment ability, I show pareto efficiency, together with stability within each period, almost characterizes the set of sequentially stable matchings.

Publication

Market Design (with Fuhito Kojima and Akhil Vohra), Encyclopedia of Complexity and Systems Science, 2017

Research in Progress

Dynamic Matching with Heterogeneous Waiting Costs

I study efficient matching where one side of the market has heterogeneous waiting costs. In the static setting, I show efficient matching reduces to the classical assignment problem and can be derived as the solution of a linear program. In the dynamic setting where agents arrive and leave over time, I associate efficient matching to a repeated auction problem and analyze the applicability of the dynamic pivot mechanism.