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cs.GT2021
On the benefits of being constrained when receiving signals
Shih-Tang Su, David Kempe, Vijay G. Subramanian
We study a Bayesian persuasion setting in which the receiver is trying to match the (binary) state of the world. The sender's utility is partially aligned with the receiver's, in t…
cs.GT2018
Social Learning with Questions
Grant Schoenebeck, Shih-Tang Su, Vijay Subramanian
This work studies sequential social learning (also known as Bayesian observational learning), and how private communication can enable agents to avoid herding to the wrong action/s…
cs.GT2016
A Descending Price Auction for Matching Markets
Shih-Tang Su, Jacob D. Abernethy, Grant Schoenebeck +1
This work presents a descending-price-auction algorithm to obtain the maximum market-clearing price vector (MCP) in unit-demand matching markets with m items by exploiting the comb…