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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

Sociodynamic modeling of small-scale PV adoption and insights on future expansion without feed-in tariffs

TL;DR: In this paper, a combined socio-economic model based on the sociodynamics framework was developed for the household-level adoption of photovoltaics (PV) in Germany and Italy.
Journal ArticleDOI

Brief paper - Consensus of multi-agent systems: a simultaneous stabilisation approach

TL;DR: In this paper, the authors considered the state consensus problem of multi-agent systems by virtue of novel simultaneous stabilisation techniques, where the distributed controller is proposed using relative outputs of the agents.
Posted Content

Learning and Forecasting Opinion Dynamics in Social Networks

TL;DR: SLANT is introduced, a probabilistic modeling framework of opinion dynamics, which represents users opinions over time by means of marked jump diffusion stochastic differential equations, and allows for efficient model simulation and parameter estimation from historical fine grained event data.
Journal ArticleDOI

Convergence properties of the heterogeneous Deffuant-Weisbuch model

TL;DR: This paper solves the convergence problem for the heterogeneous DW model with the weighting factor not less than $1/2 and establishes that, for any positive confidence bounds and initial values, the opinion of each agent will converge to a limit value almost surely, and the convergence rate is exponential in mean square.
Journal ArticleDOI

Reaching Consensus With Increasing Information

TL;DR: If the network is strongly connected, agents will eventually reach consensus in their beliefs and if in addition the state is globally identifiable, then agents will be able to learn the unknown state and reach consensus and learn the state if the network fails to be connected but the state are locally identifiable.
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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