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Observing algorithmic marketplaces in-the-wild

Observing algorithmic marketplaces in-the-wild Observing Algorithmic Marketplaces In-the-Wild LE CHEN and CHRISTO WILSON Northeastern University In this letter, we briefly summarize two recent works from our group that use observational data to study the mechanisms used by two large markets. First, we examine Uber's surge price algorithm, and observe that its incentive model may not be effective at changing driver behavior. Second, we study the adoption of dynamic pricing strategies by sellers on Amazon Marketplace, and investigate how these strategies interact with Amazon's "Buy Box" matching algorithm. We make our data available to the research community. Categories and Subject Descriptors: K.4.4 [Computing Milieux]: Computers and Society-- Electronic Commerce; J.4 [Computer Applications]: Social and Behavioral Sciences--Economics General Terms: Observational Study, Market Design, Pricing Additional Key Words and Phrases: Empirical, Amazon, Uber, Ridesharing, Dynamic Pricing 1. INTRODUCTION Much of the classic literature in economics deals with mechanism design, i.e., the construction of markets that maximize some useful quantity like revenue or welfare. As commerce has moved online, it has become easier to directly apply these ideas from economic theory in practice. One obvious example of this are online advertising auctions, but more broadly, many companies are now experimenting with differential [Mikians et al. 2012; http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGecom Exchanges Association for Computing Machinery

Observing algorithmic marketplaces in-the-wild

Chen, Le; Wilson, Christo
ACM SIGecom Exchanges , Volume 15 (2) – Feb 24, 2017
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References (11)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISSN
1551-9031
DOI
10.1145/3055589.3055594
Publisher site
See Article on Publisher Site

Abstract

Observing Algorithmic Marketplaces In-the-Wild LE CHEN and CHRISTO WILSON Northeastern University In this letter, we briefly summarize two recent works from our group that use observational data to study the mechanisms used by two large markets. First, we examine Uber's surge price algorithm, and observe that its incentive model may not be effective at changing driver behavior. Second, we study the adoption of dynamic pricing strategies by sellers on Amazon Marketplace, and investigate how these strategies interact with Amazon's "Buy Box" matching algorithm. We make our data available to the research community. Categories and Subject Descriptors: K.4.4 [Computing Milieux]: Computers and Society-- Electronic Commerce; J.4 [Computer Applications]: Social and Behavioral Sciences--Economics General Terms: Observational Study, Market Design, Pricing Additional Key Words and Phrases: Empirical, Amazon, Uber, Ridesharing, Dynamic Pricing 1. INTRODUCTION Much of the classic literature in economics deals with mechanism design, i.e., the construction of markets that maximize some useful quantity like revenue or welfare. As commerce has moved online, it has become easier to directly apply these ideas from economic theory in practice. One obvious example of this are online advertising auctions, but more broadly, many companies are now experimenting with differential [Mikians et al. 2012;

Journal

ACM SIGecom Exchanges – Association for Computing Machinery

Published: Feb 24, 2017

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