Opinion influence and evolution in social networks: A Markovian agents model
Autor: | Paolo Bolzern, Giuseppe De Nicolao, Patrizio Colaneri |
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Rok vydání: | 2019 |
Předmět: |
FOS: Computer and information sciences
Physics - Physics and Society 0209 industrial biotechnology Mathematical optimization Computer science Markov chain Monte Carlo method FOS: Physical sciences Markov process Physics and Society (physics.soc-ph) 02 engineering and technology Social networks symbols.namesake 020901 industrial engineering & automation Chain (algebraic topology) 0202 electrical engineering electronic engineering information engineering Herding Electrical and Electronic Engineering Opinion dynamics Connectivity Social and Information Networks (cs.SI) Social network business.industry 020208 electrical & electronic engineering Control and Systems Engineering Computer Science - Social and Information Networks Variance (accounting) symbols business |
Zdroj: | Automatica. 100:219-230 |
ISSN: | 0005-1098 |
Popis: | In this paper, the effect on collective opinions of filtering algorithms managed by social network platforms is modeled and investigated. A stochastic multi-agent model for opinion dynamics is proposed, that accounts for a centralized tuning of the strength of interaction between individuals. The evolution of each individual opinion is described by a Markov chain, whose transition rates are affected by the opinions of the neighbors through influence parameters. The properties of this model are studied in a general setting as well as in interesting special cases. A general result is that the overall model of the social network behaves like a high-dimensional Markov chain, which is viable to Monte Carlo simulation. Under the assumption of identical agents and unbiased influence, it is shown that the influence intensity affects the variance, but not the expectation, of the number of individuals sharing a certain opinion. Moreover, a detailed analysis is carried out for the so-called Peer Assembly, which describes the evolution of binary opinions in a completely connected graph of identical agents. It is shown that the Peer Assembly can be lumped into a birth-death chain that can be given a complete analytical characterization. Both analytical results and simulation experiments are used to highlight the emergence of particular collective behaviours, e.g. consensus and herding, depending on the centralized tuning of the influence parameters. Revised version (May 2018) |
Databáze: | OpenAIRE |
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