The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Truthful Mechanisms for One-Parameter Agents
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Towards a Characterization of Truthful Combinatorial Auctions
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Online ascending auctions for gradually expiring items
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Robbing the bandit: less regret in online geometric optimization against an adaptive adversary
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Truthful auctions for pricing search keywords
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Prediction, Learning, and Games
Prediction, Learning, and Games
An incentive-compatible multi-armed bandit mechanism
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
Noisy binary search and its applications
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
AdWords and generalized online matching
Journal of the ACM (JACM)
Algorithmic Game Theory
Dynamic cost-per-action mechanisms and applications to online advertising
Proceedings of the 17th international conference on World Wide Web
Multi-armed bandits in metric spaces
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
On characterizations of truthful mechanisms for combinatorial auctions and scheduling
Proceedings of the 9th ACM conference on Electronic commerce
Theory of Sponsored Search Auctions
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
The Bayesian Learner is Optimal for Noisy Binary Search (and Pretty Good for Quantum as Well)
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
On the Hardness of Being Truthful
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
The price of truthfulness for pay-per-click auctions
Proceedings of the 10th ACM conference on Electronic commerce
Click fraud resistant methods for learning click-through rates
WINE'05 Proceedings of the First international conference on Internet and Network Economics
The price of truthfulness for pay-per-click auctions
Proceedings of the 10th ACM conference on Electronic commerce
Truthful mechanisms with implicit payment computation
Proceedings of the 11th ACM conference on Electronic commerce
Value of learning in sponsored search auctions
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Incentive design for adaptive agents
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Deviations of stochastic bandit regret
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Repeated budgeted second price ad auction
SAGT'11 Proceedings of the 4th international conference on Algorithmic game theory
Dynamic pricing with limited supply
Proceedings of the 13th ACM Conference on Electronic Commerce
Proceedings of the 13th ACM Conference on Electronic Commerce
A truthful learning mechanism for multi-slot sponsored search auctions with externalities
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Learning and incentives in user-generated content: multi-armed bandits with endogenous arms
Proceedings of the 4th conference on Innovations in Theoretical Computer Science
Dynamic Pay-Per-Action Mechanisms and Applications to Online Advertising
Operations Research
Multi-parameter mechanisms with implicit payment computation
Proceedings of the fourteenth ACM conference on Electronic commerce
Online learning for auction mechanism in bandit setting
Decision Support Systems
Robustness of stochastic bandit policies
Theoretical Computer Science
Machine learning in an auction environment
Proceedings of the 23rd international conference on World wide web
Repeated Budgeted Second Price Ad Auction
Theory of Computing Systems
Hi-index | 0.00 |
We consider a multi-round auction setting motivated by pay-per-click auctions for Internet advertising. In each round the auctioneer selects an advertiser and shows her ad, which is then either clicked or not. An advertiser derives value from clicks; the value of a click is her private information. Initially, neither the auctioneer nor the advertisers have any information about the likelihood of clicks on the advertisements. The auctioneer's goal is to design a (dominant strategies) truthful mechanism that (approximately) maximizes the social welfare. If the advertisers bid their true private values, our problem is equivalent to the multi-armed bandit problem, and thus can be viewed as a strategic version of the latter. In particular, for both problems the quality of an algorithm can be characterized by regret, the difference in social welfare between the algorithm and the benchmark which always selects the same "best" advertisement. We investigate how the design of multi-armed bandit algorithms is affected by the restriction that the resulting mechanism must be truthful. We find that truthful mechanisms have certain strong structural properties -- essentially, they must separate exploration from exploitation -- and they incur much higher regret than the optimal multi-armed bandit algorithms. Moreover, we provide a truthful mechanism which (essentially) matches our lower bound on regret.