Title:  Global information from local observations of the noisy voter model

Abstract:

The noisy voter model on a graph is defined as follows. Initially every vertex chooses its opinion from 0 and 1 uniformly at random and independently. After that, at every integer time moment, every vertex either chooses a random opinion (with probability epsilon), or chooses the opinion of its random neighbor. In this talk we will focus on the following question. Is it possible to distinguish between two graphs by observing the outcomes of the process only at one fixed vertex in each of the graphs?

Joint work with I. Benjamini and H. Helman Tov