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DeGroot learning

DeGroot learning refers to a rule-of-thumb type of social learning process. The idea was stated in its general form by the American statistician Morris H. DeGroot; antecedents were articulated by John R. P. French and Frank Harary. The model has been used in physics, computer science and most widely in the theory of social networks.

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Convergence of beliefs and consensus

Markov chainStrongly connectedAperiodicEigenvalueDot product
6 related topics

Setup and the learning process

WeightedDirected graphStochastic
3 related topics

Asymptotic properties in large societies: wisdom

IndependentConvergence in probabilityInfluence vectors
3 related topics

Overview

Morris H. DeGrootFrank HararyPhysicsComputer scienceSocial networks
5 related topics

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Overview

Setup and the learning process

Convergence of beliefs and consensus

Asymptotic properties in large societies: wisdom

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DeGroot learning

Nodes22
Edges21
Triples5
Avg. degree1.91
Density0.090909
Components1

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DeGroot learning

Top relations

related to Asymptotic properties in large societies: wisdom · 5
DeGroot learning → Assume, DeGroot, It, Let, Then

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Important terminology

displaystyle beliefs trust consensus social influence matrix limit strongly connected initial belief aperiodic society agents infty individuals learning process general

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
DeGroot learningrelated to Asymptotic properties in large societies: wisdomIt0.60section
DeGroot learningrelated to Asymptotic properties in large societies: wisdomDeGroot0.60section
DeGroot learningrelated to Asymptotic properties in large societies: wisdomLet0.60section
DeGroot learningrelated to Asymptotic properties in large societies: wisdomAssume0.60section
DeGroot learningrelated to Asymptotic properties in large societies: wisdomThen0.60section

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