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Opinion formation in social networks

100%
TAALAIBEKOVA A.
Operations Research and Decisions
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2018
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vol. 28
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issue 2
85-108
EN
A number of selected works on the dynamics of opinions and beliefs in social networks has been discussed. Both Bayesian and non-Bayesian approaches to social learning have been considered, but the analysis has been focused on a simple, tractable and widely used model of updating beliefs – the DeGroot model. The author studied the dynamics of opinions based on the DeGroot model from dif-ferent points of view. First, its attractive features and shortcomings were discussed and then some of its extensions have been presented. These models are based on the DeGroot updating rule, but addition-ally incorporate the possibility of improvements and enrichments of the framework.
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Determining models of influence

88%
GRABISCH M., RUSINOWSKA A.
Operations Research and Decisions
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2016
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vol. 26
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issue 2
69-85
EN
We consider a model of opinion formation based on aggregation functions. Each player modifies his opinion by arbitrarily aggregating the current opinion of all players. A player is influential on another player if the opinion of the first one matters to the latter. Generalization of an influential player to a coalition whose opinion matters to a player is called an influential coalition. Influential players (coalitions) can be graphically represented by the graph (hypergraph) of influence, and convergence analysis is based on properties of the hypergraphs of influence. In the paper, we focus on the practical issues of applicability of the model w.r.t. a standard framework for opinion formation driven by Markov chain theory. For a qualitative analysis of convergence, knowing the aggregation functions of the players is not required, one only needs to know the set of influential coalitions for each player. We propose simple algorithms that permit us to fully determine the influential coalitions. We distinguish three cases: a symmetric decomposable model, an anonymous model, and a general model.
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