  In the context of agent based modeling and network theory, we focus on the
problem of recovering behavior-related choice information from
origin-destination type data, a topic also known under the name of network
tomography. As a basis for predicting agents' choices we emphasize the
connection between adaptive intelligent behavior, causal entropy maximization
and self-organized behavior in an open dynamic system. We cast this problem in
the form of binary and weighted networks and suggest information theoretic
entropy-driven methods to recover estimates of the unknown behavioral flow
parameters. Our objective is to recover the unknown behavioral values across
the ensemble analytically, without explicitly sampling the configuration space.
In order to do so, we consider the Cressie-Read family of entropic functionals,
enlarging the set of estimators commonly employed to make optimal use of the
available information. More specifically, we explicitly work out two cases of
particular interest: Shannon functional and the likelihood functional. We then
employ them for the analysis of both univariate and bivariate data sets,
comparing their accuracy in reproducing the observed trends.
