  In recent years networks have gained unprecedented attention in studying a
broad range of topics, among them in complex systems research. In particular,
multi-agent systems have seen an increased recognition of the importance of the
interaction topology. It is now widely recognized that emergent phenomena can
be highly sensitive to the structure of the interaction network connecting the
system's components, and there is a growing body of abstract network classes,
whose contributions to emergent dynamics are well-understood. However, much
less understanding have yet been gained about the effects of network dynamics,
especially in cases when the emergent phenomena feeds back to and changes the
underlying network topology.
  Our work starts with the application of the network approach to discrete
choice analysis, a standard method in econometric estimation, where the classic
approach is grounded in individual choice and lacks social network influences.
In this paper, we extend our earlier results by considering the endogenous
dynamics of social networks. In particular, we study a model where the behavior
adopted by the agents feeds back to the underlying network structure, and
report results obtained by computational multi-agent based simulations
