Revenue-Maximizing Auctions: A Bidder’s Standpoint

Author:

Nedelec Thomas12ORCID,Calauzènes Clément2,Perchet Vianney23,El Karoui Noureddine24

Affiliation:

1. Centre Borelli, Ecole Normale Superieure (ENS) Paris Saclay, 91190 Gif-sur-Yvette, France;

2. Criteo AI Lab, 75009 Paris, France;

3. Center for Research in Economics and Statistics (CREST), École Nationale de la Statistique et de l’Administration Économique (ENSAE), 91120 Palaiseau, France;

4. Department of Statistics, University of California, Berkeley, California 94720

Abstract

A vast part of the Internet economy is powered by advertising, much of which is sold at auction. A key question for sellers is how to optimize the auction mechanism they use. Bidders, conversely, try to optimize their bidding strategy. Incentive compatible auctions are a sweet spot: theory predicts that it is in the bidders' interest to bid their values, making it relatively easy for them to bid optimally. However, as they learn bidders' value distributions, sellers can progressively optimize their mechanism and extract more revenue from bidders. We show that, in sharp contrast with most results in the academic literature, bidders should not be bidding their value in incentive compatible auctions when there is no commitment from the seller about using a fixed auction. We provide a mix of theoretical and numerical results and practical methods that can easily be deployed in practice.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Management Science and Operations Research,Computer Science Applications

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