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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

Beliefs, Politics, and Environmental Policy

TL;DR: In this article, the authors pointed out that regulatory responses to environmental issues often do not coincide with consensus expert recommen... and that experts and the general public often perceive environmental problems differently.
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A model of opinion and propagation structure polarization in social media

TL;DR: A model of opinion dynamics centered around the notion that opinion changes are invoked by news exposure, which successfully exhibited not only polarization of opinion, but also segregated propagation structure.
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Effects of leaders and social power on opinion formation in complex networks

TL;DR: The results show the appropriateness of scale-free topology in hierarchal organizations where leaders posit influence on peripheral agents, and show that the consensus time could be significantly decreased by introducing social power.
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Systemic risk in a network fragility model analyzed with probability density evolution of persistent random walks

TL;DR: The dynamics of the probability density function of the average fragility converges to a unique stationary distribution which can be computed numerically and can be used to estimate the systemic risk as afunction of the parameters of the model.
Journal ArticleDOI

On the control of opinion dynamics in social networks

TL;DR: A framework to analyze the controllability of opinion dynamics in social networks using DeGroot model (DeGroot, 1974) is presented and it is found that the opinion fluctuation is determined by the smallest negative eigenvalue of an influence matrix.
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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