Propensity and stickiness in the naming game: Tipping fractions of minorities
arXiv:1409.1282 · doi:10.1103/PhysRevE.90.042809
Abstract
Agent-based models of the binary naming game are generalized here to represent a family of models parameterized by the introduction of two continuous parameters. These parameters define varying listener-speaker interactions on the individual level with one parameter controlling the speaker and the other controlling the listener of each interaction. The major finding presented here is that the generalized naming game preserves the existence of critical thresholds for the size of committed minorities. Above such threshold, a committed minority causes a fast (in time logarithmic in size of the network) convergence to consensus, even when there are other parameters influencing the system. Below such threshold, reaching consensus requires time exponential in the size of the network. Moreover, the two introduced parameters cause bifurcations in the stabilities of the system's fixed points and may lead to changes in the system's consensus.
9 pages, 10 figures
References in corpus (14)
- Statistical physics of social dynamics
- Sharp transition towards shared vocabularies in multi-agent systems
- On the Role of Zealotry in the Voter Model
- The role of inflexible minorities in the breaking of democratic opinion dynamics
- Non-equilibrium dynamics of language games on complex networks
- Non-equilibrium phase transition in negotiation dynamics
- The Naming Game in Social Networks: Community Formation and Consensus Engineering
- Agreement dynamics on small-world networks
- In-depth analysis of the Naming Game dynamics: the homogeneous mixing case
- Role of feedback and broadcasting in the naming game
- Naming Games in Two-Dimensional and Small-World-Connected Random Geometric Networks
- Social Influencing and Associated Random Walk Models: Asymptotic Consensus Times on the Complete Graph
- Microscopic activity patterns in the Naming Game
- Analytic Treatment of Tipping Points for Social Consensus in Large Random Networks