We examine the problem of allocating a resource repeatedly over time amongst a set of agents. The utility that each agent derives from consumption of the resource is private information to that agent, and prior to consumption, 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 Cost-Per-Action or Cost-Per-Acquisition (CPA) charging scheme in online advertising. In this scheme, instead of paying per click, the advertisers pay only when a user takes a specific action (e.g. fills out a form) or completes a transaction on their websites.
REF develops an ex ante efficient mechanism for the Cost-Per-Action charging scheme in online advertising.
Dynamic cost-per-action mechanisms and applications to online advertising
In WWW ’08: Proceeding of the 17th international conference on World Wide Web
Computer Science