ABSTRACT
We consider a social planner faced with a stream of myopic selfish agents. The goal of the social planner is to maximize the social welfare, however, it is limited to using only information asymmetry (regarding previous outcomes) and cannot use any monetary incentives. The planner recommends actions to agents, but her recommendations need to be Bayesian Incentive Compatible to be followed by the agents.
Our main result is an optimal algorithm for the planner, in the case that the actions realizations are deterministic and have limited support, making significant important progress on this open problem. Our optimal protocol has two interesting features. First, it always completes the exploration of a priori more beneficial actions before exploring a priori less beneficial actions. Second, the randomization in the protocol is correlated across agents and actions (and not independent at each decision time).
Supplemental Material
- Gal Bahar, Rann Smorodinsky, and Moshe Tennenholtz. 2016. Economic Recommendation Systems: One Page Abstract. In Proceedings of the 2016 ACM Conference on Economics and Computation, EC. ACM, New York, NY, USA, 757. Google ScholarDigital Library
- Nicolò Cesa-Bianchi and Gabor Lugosi. 2006. Prediction, learning, and games .Cambridge University Press, New York, NY, USA. Google ScholarDigital Library
- Yeon-Koo Che and Johannes Hörner. 2013. Optimal Design for Social Learning. SSRN Electronic Journal (2013).Google Scholar
- Shaddin Dughmi, David Kempe, and Ruixin Qiang. 2016. Persuasion with Limited Communication. In Proceedings of the ACM Conference on Economics and Computation, EC, Maastricht, The Netherlands, July 24--28 . ACM, New York, NY, USA, 663--680. Google ScholarDigital Library
- Shaddin Dughmi and Haifeng Xu. 2016. Algorithmic Bayesian persuasion. In Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, STOC. ACM, New York, NY, USA, 412--425. Google ScholarDigital Library
- Shaddin Dughmi and Haifeng Xu. 2017. Algorithmic Persuasion with No Externalities. In Proceedings of the 2017 ACM Conference on Economics and Computation, EC, Cambridge, MA, USA, June 26--30, 2017 . ACM, New York, NY, USA, 351--368. Google ScholarDigital Library
- Peter Frazier, David Kempe, Jon M. Kleinberg, and Robert Kleinberg. 2014. Incentivizing exploration. In ACM Conference on Economics and Computation, EC '14, Stanford, CA, USA, June 8--12, 2014. ACM, New York, NY, USA, 5--22. Google ScholarDigital Library
- John Gittins, Kevin Glazebrook, and Richard Weber. 2011. Multi-Armed Bandit Allocation Indices. John Wiley & Sons.Google Scholar
- Emir Kamenica and Matthew Gentzkow. 2011. Bayesian Persuasion . American Economic Review, Vol. 101, 6 (2011), 2590--2615.Google ScholarCross Ref
- Ilan Kremer, Yishay Mansour, and Motty Perry. 2014. Implementing the "Wisdom of the Crowd". J. of Political Economy, Vol. 122 (Oct. 2014), 988--1012. Issue 5. Preliminary version appeared in ACM Conf. on Economics and Computation, 2014. Google ScholarDigital Library
- Yishay Mansour, Aleksandrs Slivkins, and Vasilis Syrgkanis. 2015. Bayesian Incentive-Compatible Bandit Exploration. In Proceedings of the Sixteenth ACM Conference on Economics and Computation (EC '15). ACM, New York, NY, USA, 565--582.Google ScholarDigital Library
- Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, and Zhiwei Steven Wu. 2016. Bayesian Exploration: Incentivizing Exploration in Bayesian Games. In Proceedings of the 2016 ACM Conference on Economics and Computation (EC '16). ACM, New York, NY, USA, 661--661. Google ScholarDigital Library
- Yishay Mansour, Aleksandrs Slivkins, and Zhiwei Steven Wu. 2018. Competing Bandits: Learning Under Competition. In Innovations in Theoretical Computer Science Conference (ITCS 2018). 48:1--48:27.Google Scholar
- Aleksandrs Slivkins. 2017. Incentivizing exploration via information asymmetry. ACM Crossroads, Vol. 24, 1 (2017), 38--41. Google ScholarDigital Library
Index Terms
- Optimal Algorithm for Bayesian Incentive-Compatible Exploration
Recommendations
Bayesian Incentive-Compatible Bandit Exploration
EC '15: Proceedings of the Sixteenth ACM Conference on Economics and ComputationIndividual decision-makers consume information revealed by the previous decision makers, and produce information that may help in future decision makers. This phenomenon is common in a wide range of scenarios in the Internet economy, as well as ...
Incentive compatible budget elicitation in multi-unit auctions
SODA '10: Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete algorithmsIn this paper, we consider the problem of designing incentive compatible auctions for multiple (homogeneous) units of a good, when bidders have private valuations and private budget constraints. When only the valuations are private and the budgets are ...
Incentive-Compatible Learning of Reserve Prices for Repeated Auctions
WWW '19: Companion Proceedings of The 2019 World Wide Web ConferenceMotivated by online advertising market, we consider a seller who repeatedly sells ex ante identical items via the second-price auction. Buyers’ valuations for each item are drawn i.i.d. from a distribution F that is unknown to the seller. We find that ...
Comments