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Competition between traditional platforms is known to improve user utility by aligning the platform's actions with user preferences.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R. Thompson · 1933
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The concept of monopoly and the measurement of monopoly power
A. P. Lerner · 1934
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An introduction to antitrust economics
Ernest Gellhorn · 1975
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Bandit processes and dynamic allocation indices
J. C. Gittins · 1979
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A dynamic allocation index for the discounted multiarmed bandit problem
J. C. Gittins and D. M. Jones · 1979
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Asymptotically efficient adaptive allocation rules
T.L Lai and Herbert Robbins · 1985
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Strategic experimentation
Patrick Bolton and Christopher Harris · 1999
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Experimentation in markets
Dirk Bergemann and Juuso Välimäki · 2000
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Notes on equilibria in symmetric games
Shih-fen Cheng, Daniel M. Reeves, Yevgeniy Vorobeychik, and Michael P. Wellman · 2004
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Strategic experimentation with exponential bandits
Godfrey Keller, Sven Rady, and Martin Cripps · 2005
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Social learning in one-arm bandit problems
Dinah Rosenberg, Eilon Solan, and Nicolas Vieille · 2007
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Bertrand Competition
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The economics of two-sided markets
PMarc Rysman · 2009
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Dueling algorithms
Nicole Immorlica, Adam Tauman Kalai, Brendan Lucier, Ankur Moitra, Andrew Postlewaite, and Moshe Tennenholtz · 2011
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Implementing the "wisdom of the crowd"
Ilan Kremer, Yishay Mansour, and Motty Perry · 2013
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Incentivizing exploration
Peter I. Frazier, David Kempe, Jon M. Kleinberg, and Robert Kleinberg · 2014
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Let the right ‘one’ win: Policy lessons from the new economics of platforms
Glen Weyl and Alexander White · 2014
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Bayesian incentive-compatible bandit exploration
Competition Policy for the digital era : Final report
Jacques Crémer, Yves-Alexandre de Montjoye, and Heike Schweitzer · 2019
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Final report: Stigler committee on digital platforms
Stigler Committee · 2019
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Competing bandits: The perils of exploration under competition
Guy Aridor, Yishay Mansour, Aleksandrs Slivkins, and Zhiwei Steven Wu · 2020
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Multiplayer bandit learning, from competition to cooperation
Simina Brânzei and Yuval Peres · 2021
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Competing AI: how does competition feedback affect machine learning?
Tony Ginart, Eva Zhang, Yongchan Kwon, and James Zou · 2021
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Competing with big data
Jens Prüfer and Christoph Schottmüller · 2021
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Yishay Mansour, Aleksandrs Slivkins, and Vasilis Syrgkanis · 2015
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Bayesian exploration: Incentivizing exploration in bayesian games
Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, and Zhiwei Steven Wu · 2016
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Best response regression
Omer Ben-Porat and Moshe Tennenholtz · 2017
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Learning, Experimentation, and Information Design , volume 1 of Econometric Society Monographs , page 63–98
Johannes Hörner and Andrzej Skrzypacz · 2017
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Regression equilibrium
Omer Ben-Porat and Moshe Tennenholtz · 2019
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Strategic experimentation: the undiscounted case
Patrick Bolton and Christopher Harris
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The price of incentivizing exploration: A characterization via thompson sampling and sample complexity
Mark Sellke and Aleksandrs Slivkins · 2021
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Multi-learner risk reduction under endogenous participation dynamics
Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, and Maryam Fazel · 2022
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Moritz Hardt, Meena Jagadeesan, and Celestine Mendler-Dünner · 2022
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Competition over data: how does data purchase affect users?
Yongchan Kwon, Antonio Ginart, and James Zou · 2022
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