Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/213451 
Year of Publication: 
2017
Series/Report no.: 
CSIO Working Paper No. 0144
Publisher: 
Northwestern University, Center for the Study of Industrial Organization (CSIO), Evanston, IL
Abstract: 
We study mediated many-to-many matching in markets in which valuations evolve over time as the result of shocks, learning through experimentation, or a preference for variety. The analysis uncovers the key tradeoffs that platforms face in the design of their matching protocols. It shows that the dynamics that maximize either the platform's profits or welfare can often be sustained through auctions implementing the matches with the highest bilateral score up to capacity. In equilibrium, bidding is straight-forward and myopic. The analysis also sheds light on the merits of regulating such markets. When match values are positive, profit maximization involves fewer and shorter interactions than welfare maximization. This conclusion need not extend to markets where certain agents dislike certain interactions.
Subjects: 
matching
experimentation
platforms
bandit problems
asymmetric information
learning
dynamic auctions
JEL: 
D82
C73
L1
Document Type: 
Working Paper

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