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Proceedings ArticleDOI

Diffusion social learning over weakly-connected graphs

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TLDR
It is shown that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations, and useful closed-form expressions are derived which can be used to motivate design problems to control it.
Abstract
In this paper, we study diffusion social learning over weakly-connected graphs. We show that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations. Under some circumstances that we clarify in this work, a scenario of total influence (or "mind-control") arises where a set of influential agents ends up shaping the beliefs of non-influential agents. We derive useful closed-form expressions that characterize this influence, and which can be used to motivate design problems to control it. We provide simulation examples to illustrate the results.

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Journal ArticleDOI

Social Learning Over Weakly Connected Graphs

TL;DR: It is shown that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations, and useful closed-form expressions are derived which can be used to motivate design problems to control it.
Proceedings ArticleDOI

A tutorial on distributed (non-Bayesian) learning: Problem, algorithms and results

TL;DR: In this paper, the authors consider different approaches to the distributed learning problem and its algorithmic solutions for the case of finitely many hypotheses for both asymptotic and finite time regimes.
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Belief Control Strategies for Interactions over Weakly-Connected Graphs

TL;DR: This article develops mechanisms by which influential agents can lead receiving agents to adopt certain beliefs and examines whether receiving agents can be driven to arbitrary beliefs and whether the network structure limits the scope of control by the influential agents.
Posted Content

Social Learning over Weakly-Connected Graphs

TL;DR: In this paper, the authors study diffusion social learning over weakly-connected graphs and show that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations.
Posted Content

Belief Control Strategies for Interactions over Weak Graphs

TL;DR: In this paper, the authors examined how much freedom influential agents have in controlling the beliefs of the receiving agents and whether the network structure limits the scope of control by the influential agents.
References
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Book

Matrix Analysis and Applied Linear Algebra

TL;DR: The author presents Perron-Frobenius theory of nonnegative matrices Index, a theory of matrices that combines linear equations, vector spaces, and matrix algebra with insights into eigenvalues and Eigenvectors.
Journal ArticleDOI

Reaching a Consensus

TL;DR: In this article, the authors consider a group of individuals who must act together as a team or committee, and assume that each individual in the group has his own subjective probability distribution for the unknown value of some parameter.
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Naïve Learning in Social Networks and the Wisdom of Crowds

TL;DR: It is shown that all opinions in a large society converge to the truth if and only if the influence of the most influential agent vanishes as the society grows.
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Bayesian learning in social networks

TL;DR: This work introduces a social network and assumes that agents can only observe the actions of agents to whom they are connected by this network, and allows agents to choose a different action at each date.
Journal ArticleDOI

Bayesian Learning in Social Networks

TL;DR: The main theorem shows that when the probability that each individual observes some other individual from the recent past converges to one as the social network becomes large, unbounded private beliefs are sufficient to ensure asymptotic learning.
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