Abstract

Interacting particle systems are Markov chains involving infinitely many mutually interacting components. Apart from the obvious connection to statistical physics, since the conception of the field in the 1970s, interacting particle systems such as the well-known voter model and contact process have also been used to model biological populations. An alternative way of studying these biologically motivated systems is to determine the state of individuals living at a given time by tracing their ancestors, or potential ancestors, backwards in time. The collection of all (potential) ancestors with their relationships forms a random network that is called the genealogy of the interacting particle system. There is a close connection to real genealogies of biological populations and questions about the latter, such as what are the effects of population dynamic and evolutionary mechanisms on genealogical trees, continue to stimulate the development of more theoretical topics, such as the theor