Name
Abstract | ||
|---|---|---|
Opinions, and subsequently opinion dynamics, depend not just on interactions among individuals, but also on external influences such as the mass media. The dependence on local interactions, however, has received considerably more attention. In this paper, we use the classical voter model as a basis, and extend it to include external influences. We show that this new model can be understood using the theory of jump diffusion processes. We derive results pertaining to fixation probability and expected consensus time of the process, and find that the contribution of an external influence significantly dwarfs the contribution of the node-to-node interactions in terms of driving the social network to eventual consensus. This result suggests the potential importance of ``macro-levelu0027u0027 phenomena such as the media influence as compared to the ``micro-levelu0027u0027 local interactions, in modelling opinion dynamics. |
Year | Venue | Field |
|---|---|---|
2015 | arXiv: Physics and Society | Mathematical economics,Social network,Jump diffusion,Voter model,Mass media,Artificial intelligence,Opinion dynamics,Mathematics,Machine learning |
DocType | Volume | Citations |
Journal | abs/1511.04160 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jimit Majmudar | 1 | 0 | 0.68 |
| Stephen M. Krone | 2 | 7 | 1.60 |
| Bert Baumgaertner | 3 | 4 | 1.53 |
| Rebecca C. Tyson | 4 | 1 | 0.69 |