NDLI: Network structure and efficiency of observational social learning

Content Provider	IEEE Xplore Digital Library
Author	Molavi, P. Jadbabaie, A.
Copyright Year	2012
Description	Author affiliation: Department of Electrical and Systems Engineering and General Robotics, Automation, Sensing and Perception (GRASP) Laboratory, University of Pennsylvania, Philadelphia, 19104-6228, USA (Molavi, P.; Jadbabaie, A.)
Abstract	This paper explores the relationship between the structure of a network of agents and how efficiently they can learn a common unknown parameter. Agents repeatedly make private observations which are possibly informative about the unknown parameter; they also communicate their beliefs over the set of conceivable parameter values to their neighbors. It has been shown that for agents to learn the realized state, it is sufficient that they incorporate in their beliefs their private observations in a Bayesian way and the beliefs of their neighbors using a fixed linear rule. In this paper we establish upper and lower bounds on the rate by which agents performing such an update learn the realized state and show that the bounds can be tight. These bounds enable us to compare efficiency of different networks in aggregating dispersed information. Our analysis yields an important insight: for agents in large balanced networks learning is much slower compared to that of a central observer regardless of the distribution of information in the network, whereas unbalanced networks result in near efficient learning if the observations of the centrally positioned agents are much more informative than others' observations.
Starting Page	44
Ending Page	49
File Size	305133
Page Count	6
File Format	PDF
ISBN	9781467320658
ISSN	07431546
e-ISBN	9781467320665
e-ISBN	9781467320641
DOI	10.1109/CDC.2012.6426454
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Social network services Bayesian methods Upper bound Probability distribution Silicon Observers Nickel
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Aggregate observational distinguishability is necessary and sufficient for social learning

Observational learning in an uncertain world

Learning without recall in directed circles and rooted trees

Online discrete optimization in social networks

On consensus and exponentially fast social learning

Diffusion of innovations in social networks

Towards effective improvement of the Bayesian Belief Network Structure learning

Learning in Hierarchical Social Networks

A Theory of Bayesian Learning Systems

Network structure and efficiency of observational social learning

Similar Documents

Aggregate observational distinguishability is necessary and sufficient for social learning

Observational learning in an uncertain world

Learning without recall in directed circles and rooted trees

Online discrete optimization in social networks

On consensus and exponentially fast social learning

Diffusion of innovations in social networks

Towards effective improvement of the Bayesian Belief Network Structure learning

Learning in Hierarchical Social Networks

A Theory of Bayesian Learning Systems

Network structure and efficiency of observational social learning