Abstract
This chapter will study the Expressed–Private Opinion (EPO) model first introduced in the previous Chap. 3. First, the model is used to revisit and re-examine Asch’s seminal conformity experiments. Analytical calculations are used to establish the opinion evolution of the test individuals in the experiments. With the aid of simulations, it is shown that all of the different ways that individuals were recorded to have reacted in the experiments are captured and predicted by the proposed model. Then, pluralistic ignorance is investigated. This phenomenon considers how the perceived and true positions of the general population on a given topic can be completely different. Using extensive simulations, the role of stubborn extremists (termed zealots) in creating pluralistic ignorance is revealed.
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Notes
- 1.
See Sect. 2.3.3 for the differences between opinion and belief as defined in this thesis.
- 2.
The definition in O’Gorman (1975) is given for two or more individuals.
- 3.
Note that the literature also refers to “the same opinion”, because the opinions considered in the social science literature are often discrete variables. Since this thesis deals with opinions as real values on a continuous interval, the word “similar” is used to mean opinions which are approximately of the same value.
- 4.
The choice of values for \(\lambda _i\) and \(\phi _i\) are arbitrary, and selected only for convenience in running the simulations while ensuring Assumption 3.1 holds. The results and conclusions drawn in this section will hold if zealots have values of \(\lambda _i\) and \(\phi _i\) that are in the neighbourhood of 0.001 and 0.999, respectively. This includes allowing for heterogeneous parameter values between the zealots.
- 5.
For details on transitivity and shortest-path distance, see [Newman 2010, Chap. 7].
- 6.
It is standard practise in network science to consider several different graph models, e.g. small-world, scale-free, Erdős-Rényi. A number of standard software packages exist for graph generation, and graphical properties such as transitivity, clustering coefficient etc. are well studied. They are widely accepted, despite each model’s shortcomings (which are different between the models) in resembling real-world networks.
- 7.
Such a condition on the edge weights is not needed for the theoretical results reported in Chap. 3.
- 8.
Note that this is slightly different to the original small-world model, which removed an edge and then added an edge between two nodes as describe above, with probability p. Both the network generation method used in this paper, and the original method are described in detail in Newman (2010). The modified generation method ensures that there is no risk that removal of an edge results in disconnected sub-graphs.
- 9.
Roughly speaking, the algorithm begins with a node \(v_1\). Then, new nodes are added to the network one at a time and edges are added. Specifically, each new node \(v_i\) is connected to m nodes \(v_{j_1}, \ldots , v_{j_m}\), with \(j_1, \ldots , j_m < i\). The probability \(p_{j}\) of connecting \(v_i\) to \(v_j\) is proportional to the degree of \(v_j\) prior to any connection of \(v_i\) and any nodes added after \(v_i\).
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Ye, M. (2019). The EPO Model’s Connections with Social Psychology Concepts. In: Opinion Dynamics and the Evolution of Social Power in Social Networks. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-030-10606-5_4
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