Social Learning in a Changing World
- Rafael M. FrongilloAffiliated withLancaster UniversityComputer Science Department, University of California
- , Grant SchoenebeckAffiliated withCarnegie Mellon UniversityComputer Science Department, Princeton University
- , Omer TamuzAffiliated withCarnegie Mellon UniversityWeizmann Institute
We study a model of learning on social networks in dynamic environments, describing a group of agents who are each trying to estimate an underlying state that varies over time, given access to weak signals and the estimates of their social network neighbors.
We study three models of agent behavior. In the fixed response model, agents use a fixed linear combination to incorporate information from their peers into their own estimate. This can be thought of as an extension of the DeGroot model to a dynamic setting. In the best response model, players calculate minimum variance linear estimators of the underlying state.
We show that regardless of the initial configuration, fixed response dynamics converge to a steady state, and that the same holds for best response on the complete graph. We show that best response dynamics can, in the long term, lead to estimators with higher variance than is achievable using well chosen fixed responses.
The penultimate prediction model is an elaboration of the best response model. While this model only slightly complicates the computations required of the agents, we show that in some cases it greatly increases the efficiency of learning, and on complete graphs is in fact optimal, in a strong sense.
Keywordssocial networks Bayesian agents social learning
- Social Learning in a Changing World
- Book Title
- Internet and Network Economics
- Book Subtitle
- 7th International Workshop, WINE 2011, Singapore, December 11-14, 2011. Proceedings
- pp 146-157
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag GmbH Berlin Heidelberg
- Additional Links
- social networks
- Bayesian agents
- social learning
- Industry Sectors
- eBook Packages
- Editor Affiliations
- 16. GECAD, Instituto Superior de Engenharia do Porto
- 17. School of Physical and Mathematical Sciences, SPMS-MAS-03-01, Nanyang Technological University
- 18. Department of Informatics, University of Athens
- Author Affiliations
- 19. Computer Science Department, University of California, Berkeley, USA
- 20. Computer Science Department, Princeton University, USA
- 21. Weizmann Institute, Israel
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