Economic Recommendation Systems
@article{Bahar2015EconomicRS, title={Economic Recommendation Systems}, author={Gal Bahar and Rann Smorodinsky and Moshe Tennenholtz}, journal={ArXiv}, year={2015}, volume={abs/1507.07191} }
In the on-line Explore and Exploit literature, central to Machine Learning, a central planner is faced with a set of alternatives, each yielding some unknown reward. The planner's goal is to learn the optimal alternative as soon as possible, via experimentation. A typical assumption in this model is that the planner has full control over the experiment design and implementation. When experiments are implemented by a society of self-motivated agents the planner can only recommend experimentation… CONTINUE READING
Topics from this paper
15 Citations
Bayesian Exploration: Incentivizing Exploration in Bayesian Games
- Computer Science, Economics
- EC
- 2016
- 45
- PDF
Bayesian Incentive-Compatible Bandit Exploration
- Computer Science, Economics
- EC
- 2015
- 69
- Highly Influenced
- PDF
Competing Bandits: The Perils of Exploration under Competition
- Computer Science, Economics
- ArXiv
- 2019
- 6
- PDF
References
SHOWING 1-10 OF 18 REFERENCES
Sequential voting with externalities: herding in social networks
- Economics, Computer Science
- EC '12
- 2012
- 18
- PDF
The Impact of Locality and Authority on Emergent Conventions: Initial Observations
- Computer Science
- AAAI
- 1994
- 29
- PDF
Informational externalities and emergence of consensus
- Economics, Computer Science
- Games Econ. Behav.
- 2009
- 86
- PDF
A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades
- Psychology
- Journal of Political Economy
- 1992
- 5,653
- PDF