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Segmenting two-sided markets

Segmenting two-sided markets Segmenting Two-Sided Markets SIDDHARTHA BANERJEE Cornell University and SREENIVAS GOLLAPUDI Google Research and KOSTAS KOLLIAS Google Research and KAMESH MUNAGALA Duke University Recent years have witnessed the rise of many successful e-commerce marketplace platforms like the Amazon marketplace, AirBnB, Uber/Lyft, and Upwork, where a central platform mediates economic transactions between buyers and sellers. A common feature of many of these two-sided marketplaces is that the platform has full control over search and discovery, but prices are determined by the buyers and sellers. We summarize our results from [Banerjee et al. 2017] where, motivated by this application domain, we study the algorithmic aspects of market segmentation via directed discovery in two-sided markets with endogenous prices. We consider a model where an online platform knows each buyer/seller ™s characteristics, and associated demand/supply elasticities. Moreover, the platform can use discovery mechanisms (search, recommendation, etc.) to control which buyers/sellers are visible to each other. We develop e ƒcient algorithms for throughput (i.e. volume of trade) and welfare maximization with provable guarantees under a variety of assumptions on the demand and supply functions. Categories and Subject Descriptors: J.4 [Social and Behavioral Science]: Economics General Terms: Economics, Market Design, Theory Additional Key Words and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGecom Exchanges Association for Computing Machinery

Segmenting two-sided markets

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISSN
1551-9031
DOI
10.1145/3144722.3144726
Publisher site
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Abstract

Segmenting Two-Sided Markets SIDDHARTHA BANERJEE Cornell University and SREENIVAS GOLLAPUDI Google Research and KOSTAS KOLLIAS Google Research and KAMESH MUNAGALA Duke University Recent years have witnessed the rise of many successful e-commerce marketplace platforms like the Amazon marketplace, AirBnB, Uber/Lyft, and Upwork, where a central platform mediates economic transactions between buyers and sellers. A common feature of many of these two-sided marketplaces is that the platform has full control over search and discovery, but prices are determined by the buyers and sellers. We summarize our results from [Banerjee et al. 2017] where, motivated by this application domain, we study the algorithmic aspects of market segmentation via directed discovery in two-sided markets with endogenous prices. We consider a model where an online platform knows each buyer/seller ™s characteristics, and associated demand/supply elasticities. Moreover, the platform can use discovery mechanisms (search, recommendation, etc.) to control which buyers/sellers are visible to each other. We develop e ƒcient algorithms for throughput (i.e. volume of trade) and welfare maximization with provable guarantees under a variety of assumptions on the demand and supply functions. Categories and Subject Descriptors: J.4 [Social and Behavioral Science]: Economics General Terms: Economics, Market Design, Theory Additional Key Words and

Journal

ACM SIGecom Exchanges – Association for Computing Machinery

Published: Sep 25, 2017

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