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Journal ArticleDOI

Reaching a Consensus

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TLDR
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.
Abstract
Consider a group of individuals who must act together as a team or committee, and suppose that each individual in the group has his own subjective probability distribution for the unknown value of some parameter. A model is presented which describes how the group might reach agreement on a common subjective probability distribution for the parameter by pooling their individual opinions. The process leading to the consensus is explicitly described and the common distribution that is reached is explicitly determined. The model can also be applied to problems of reaching a consensus when the opinion of each member of the group is represented simply as a point estimate of the parameter rather than as a probability distribution.

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Citations
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Journal ArticleDOI

Consensus of multiple nonholonomic chained form systems

TL;DR: Different to previous assumptions on group reference such as persistent excitation or converging to nonzero constant, the condition on the group reference signal have been further relaxed in this paper.
Journal ArticleDOI

Multi-agent Coordination in Directed Moving Neighborhood Random Networks

TL;DR: This paper considers the consensus problem of dynamical multiple agents that communicate via a directed moving neighborhood random network, and presents sufficient conditions for almost sure asymptotic consensus.
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Reflecting on Social Influence in Networks

TL;DR: In this article, the authors introduce a formal framework for reasoning about an individual's private opinions and public behavior under the dynamics of social influence in social networks, and dig deeper into the involved information dynamics by modeling how individuals can learn about each other.
Journal ArticleDOI

Diffusion and Superposition Distances for Signals Supported on Networks

TL;DR: In this paper, diffusion and superposition distances are introduced as two metrics to compare signals supported in the nodes of a network and prove that both distances define valid metrics and that they are stable to perturbations in the underlying network.
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Opinion dynamics under conformity

TL;DR: In this paper, the authors present a model of opinion formation where individuals repeatedly engage in discussion and update their opinion in a social network similarly to the DeGroot model and identify conditions for convergence to consensus.
References
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Book

Stochastic processes

J. L. Doob, +1 more
Book

A first course in stochastic processes

TL;DR: In this paper, the Basic Limit Theorem of Markov Chains and its applications are discussed and examples of continuous time Markov chains are presented. But they do not cover the application of continuous-time Markov chain in matrix analysis.
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