Adaptive networks are models of complex systems in which the structure of the interaction network changes on the same time-scale as the status of the nodes. For instance, consider the spread of a disease over a social network that is changing as people try to avoid the infection. In this talk I will try to persuade you that demographic noise (random fluctuations arising from the discrete nature of the components of the network) plays a major role in determining the behaviour of these models. These effects can be studied analytically by employing a reduced-dimension Markov jump process as a proxy.
Epidemics and elections: the importance of demographic noise in adaptive networks
Tim Rogers (Bath)
Tue, 03/12/2013 - 16:00