Convergence of Position Auctions under Myopic Best-Response Dynamics
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Published:2014-07
Issue:3
Volume:2
Page:1-20
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ISSN:2167-8375
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Container-title:ACM Transactions on Economics and Computation
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language:en
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Short-container-title:ACM Trans. Econ. Comput.
Author:
Cary Matthew1,
Das Aparna2,
Edelman Benjamin3,
Giotis Ioannis4,
Heimerl Kurtis5,
Karlin Anna R.1,
Kominers Scott Duke6,
Mathieu Claire7,
Schwarz Michael8
Affiliation:
1. University of Washington
2. Le Moyne College
3. Harvard Business School
4. Universitat Politecnica de Catalunya
5. University of California, Berkeley
6. Harvard University
7. Ecole Normale Superieure and Brown University
8. Yahoo! Labs
Abstract
We study the dynamics of multiround position auctions, considering both the case of exogenous click-through rates and the case in which click-through rates are determined by an endogenous consumer search process. In both contexts, we demonstrate that dynamic position auctions converge to their associated static, envy-free equilibria. Furthermore, convergence is efficient, and the entry of low-quality advertisers does not slow convergence. Because our approach predominantly relies on assumptions common in the sponsored search literature, our results suggest that dynamic position auctions converge more generally.
Funder
a Yahoo! Research Alliance
European Social Fund
Education and Lifelong Learning of the National Strategic Reference Framework
Harvard College Program for Research in Science and Engineering
American Mathematical Society
Division of Computing and Communication Foundations
Publisher
Association for Computing Machinery (ACM)
Subject
Computational Mathematics,Marketing,Economics and Econometrics,Statistics and Probability,Computer Science (miscellaneous)
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