Dynamic Pay-Per-Action Mechanisms and Applications to Online Advertising

Authors:
Hamid Nazerzadeh;Amin Saberi;Rakesh Vohra
Affiliations:
Marshall School of Business, University of Southern California, Los Angeles, California 94305;Management Science and Engineering Department, Stanford University, Stanford, California 94305;Kellogg School of Management, Northwestern University, Evanston, Illinois 60208
Venue:
Operations Research
Year:
2013

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Abstract

We examine the problem of allocating an item repeatedly over time amongst a set of agents. The value that each agent derives from consumption of the item may vary over time. Furthermore, it is private information to the agent, and prior to consumption it may be unknown to that agent. We describe a mechanism based on a sampling-based learning algorithm that under suitable assumptions is asymptotically individually rational, asymptotically Bayesian incentive compatible, and asymptotically ex ante efficient. Our mechanism can be interpreted as a pay-per-action or pay-per-acquisition PPA charging scheme in online advertising. In this scheme, instead of paying per click, advertisers pay only when a user takes a specific action e.g., purchases an item or fills out a form on their websites.