  We investigate to what extent the interaction dynamics of a population of
wild house mouse (Mus musculus domesticus) in their environment can be
explained by a simple stochastic model. We use a Markov chain model to describe
the transitions of mice in a discrete space of nestboxes, and implement a
multi-agent simulation of the model. We find that some important features of
our behavioural dataset can be reproduced using this simplified stochastic
representation, and discuss the improvements that could be made to our model in
order to increase the accuracy of its predictions. Our findings have
implications for the understanding of the complexity underlying social
behaviour in the animal kingdom and the cognitive requirements of such
behaviour.
