- Series
- IMPACT Distinguished Lecture
- Time
- Friday, March 17, 2017 - 3:00pm for 1 hour (actually 50 minutes)
- Location
- Skiles 006
- Speaker
- Rick Durett – Duke University – rtd@math.duke.edu
- Organizer
- Megan Bernstein
In
the latent voter model, which models the spread of a technology
through a social network, individuals who have just changed their
choice have a latent period, which is exponential with rate λ during
which they will not buy a new device. We study site and edge versions
of this model on random graphs generated by a configuration model in
which the degrees d(x) have 3 ≤ d(x) ≤ M. We show that if the
number of vertices n → ∞ and log n << λn
<< n then the latent voter model has a quasi-stationary state
in which each opinion has probability ≈ 1/2 and persists in this
state for a time that is ≥ nm
for any m <∞. Thus, even a very small latent period drastically
changes the behavior of the voter model.