  Relapse, the recurrence of a disorder following a symptomatic remission, is a
frequent outcome in substance abuse disorders. Some of our prior results
suggested that relapse, in the context of abusive drinking, is likely an
"unbeatable" force as long as recovered individuals continue to interact in the
environments that lead to and/or reinforce the persistence of abusive drinking
behaviors. Our earlier results were obtained via a deterministic model that
ignored differences between individuals, that is, in a rather simple "social"
setting. In this paper, we address the role of relapse on drinking dynamics but
use models that incorporate the role of "chance", or a high degree of "social"
heterogeneity, or both. Our focus is primarily on situations where relapse
rates are high. We first use a Markov chain model to simulate the effect of
relapse on drinking dynamics. These simulations reinforce the conclusions
obtained before, with the usual caveats that arise when the outcomes of
deterministic and stochastic models are compared. However, the simulation
results generated from stochastic realizations of an "equivalent" drinking
process in populations "living" in small world networks, parameterized via a
disorder parameter $p$, show that there is no social structure within this
family capable of reducing the impact of high relapse rates on drinking
prevalence, even if we drastically limit the interactions between individuals
($p\approx 0$).
