Information Recovery In Behavioral Networks

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.