research-article

Incentivizing exploration via information asymmetry

Author:

Aleksandrs SlivkinsAuthors Info & Claims

XRDS: Crossroads, The ACM Magazine for Students, Volume 24, Issue 1

Pages 38 - 41

https://doi.org/10.1145/3123744

Published: 14 September 2017 Publication History

Get Access

Abstract

As self-interested individuals make decisions over time, they utilize information revealed by others in the past and produce information that may help others in the future. So how can we incentivize exploration for the sake of the common good?

References

[1]

Kremer, I., Mansdur, Y., and Perry, M. Implementing the wisdom of the crowd. J. of Political Economy, 122, 5 {2014}. Prelim. version in ACM EC 2014.

Google Scholar

[2]

Che, Y.-K. and Hörner, J. Optimal design for social learning. Preprint, 2015. First draft: 2013.

Google Scholar

[3]

Mansour, Y., Slivkins, A., and Syrgkanis, V. Bayesian incentive-compatible bandit exploration. Working paper, 2017. Prelim. version in ACM EC 2015.

Digital Library

Google Scholar

[4]

Mansour, Y., Slivkins, A., Syrgkanis, V., Wu, S. Bayesian exploration: Incentivizing exploration in bayesian games. Working paper, 2016. Prelim. version in ACM EC 2016.

Digital Library

Google Scholar

[5]

Bahar, G., Smorodinsky, R., and Tennenholtz, M. Economic recommendation systems. In 16th ACM Conf. on Electronic Commerce {EC}, 2016.

Digital Library

Google Scholar

[6]

Bimpikis, K., Papanastasiou, Y., and Savva, N. Crowdsourcing exploration. Management Science, 2017. Forthcoming.

Google Scholar

[7]

Frazier, P., Kempe, D., Kleinberg, J. M., and Kleinberg, R. Incentivizing exploration. In ACM Conf. on Economics and Computation {ACM EC}. ACM, New York, 2014.

Digital Library

Google Scholar

[8]

Kleinberg, R. D., Waggoner, B., and Weyl, E. G. Descending price optimally coordinates search. Working paper, 2016. Prelim. version in ACM EC 2016.

Digital Library

Google Scholar

[9]

Kleinberg, R. D. and Aleksandrs Slivkins. Incentivizing and coordinating exploration, 2017. Tutorial at ACM EC 2017.

Google Scholar

[10]

Bubeck, S. and Cesa-Bianchi, N. Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends in Machine Learning 5, 1 {2012}.

Google Scholar

[11]

Kamenica, E. and Gentzkow, M. Bayesian persuasion. American Economic Review 101, 6 {2011}.

Google Scholar

Cited By

View all

Zhang QPark SMuller MKarger D(2024)"How fancy you are to make us use your fancy tool": Coordinating Individuals' Tool Preference over Group BoundariesProceedings of the ACM on Human-Computer Interaction10.1145/36330698:GROUP(1-31)Online publication date: 16-Feb-2024
https://dl.acm.org/doi/10.1145/3633069
Wang HXu HLi CLiu ZWang H(2023)Incentivizing Exploration in Linear Contextual Bandits under Information GapProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608794(415-425)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608794
Ding Y(2020)Impact of Internet on International-Trade from the Perspective of Information AsymmetryCyber Security Intelligence and Analytics10.1007/978-3-030-43309-3_21(153-159)Online publication date: 11-Mar-2020
https://doi.org/10.1007/978-3-030-43309-3_21
Show More Cited By

Index Terms

Incentivizing exploration via information asymmetry
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics
2. Theory of computation
  1. Design and analysis of algorithms
    1. Online algorithms
      1. Online learning algorithms
  2. Theory and algorithms for application domains
    1. Algorithmic game theory and mechanism design
      1. Algorithmic game theory
      2. Social networks

Recommendations

The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity
EC '21: Proceedings of the 22nd ACM Conference on Economics and Computation

We consider incentivized exploration: a version of multi-armed bandits where the choice of arms is controlled by self-interested agents, and the algorithm can only issue recommendations. The algorithm controls the flow of information, and the ...
Incentivizing exploration
EC '14: Proceedings of the fifteenth ACM conference on Economics and computation

We study a Bayesian multi-armed bandit (MAB) setting in which a principal seeks to maximize the sum of expected time-discounted rewards obtained by pulling arms, when the arms are actually pulled by selfish and myopic individuals. Since such individuals ...
Limits of price competition: cost asymmetry and imperfect information
Abstract
In a class of asymmetric-cost duopoly price competition games, price-elastic individual demand reveals a threshold of informed buyers below which equilibrium is in monopoly prices. Even if the threshold is met, unless all buyers are informed, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image XRDS: Crossroads, The ACM Magazine for Students

XRDS: Crossroads, The ACM Magazine for Students Volume 24, Issue 1

Incentives and Gamification

Fall 2017

61 pages

ISSN:1528-4972

EISSN:1528-4980

DOI:10.1145/3140569

Editors:
Jennifer Jacobs
MIT
,
Okke Schrijvers
Stanford University

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 September 2017

Published in XRDS Volume 24, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Popular
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
330
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zhang QPark SMuller MKarger D(2024)"How fancy you are to make us use your fancy tool": Coordinating Individuals' Tool Preference over Group BoundariesProceedings of the ACM on Human-Computer Interaction10.1145/36330698:GROUP(1-31)Online publication date: 16-Feb-2024
https://dl.acm.org/doi/10.1145/3633069
Wang HXu HLi CLiu ZWang H(2023)Incentivizing Exploration in Linear Contextual Bandits under Information GapProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608794(415-425)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608794
Ding Y(2020)Impact of Internet on International-Trade from the Perspective of Information AsymmetryCyber Security Intelligence and Analytics10.1007/978-3-030-43309-3_21(153-159)Online publication date: 11-Mar-2020
https://doi.org/10.1007/978-3-030-43309-3_21
Cohen LMansour YKarlin AImmorlica NJohari R(2019)Optimal Algorithm for Bayesian Incentive-Compatible ExplorationProceedings of the 2019 ACM Conference on Economics and Computation10.1145/3328526.3329581(135-151)Online publication date: 17-Jun-2019
https://dl.acm.org/doi/10.1145/3328526.3329581

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Digital Edition

View this article in digital edition.

Digital Edition

Magazine Site

View this article on the magazine site (external)

Magazine Site

Abstract

References

Cited By

Index Terms

Recommendations

The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity