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PLoS One. 2015 May 6;10(5):e0125077. doi: 10.1371/journal.pone.0125077. eCollection 2015.

Information recovery in behavioral networks.

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Istituto dei Sistemi Complessi, Universitá di Roma "Sapienza", 00185 Rome, Italy.
IFISC (CSIC-UIB), Instituto de Física Interdisciplinar y Sistemas Complejos, Campus Universitat des les Illes Balears, E-07122 Palma de Mallorca, Spain.
Lorentz Institute for Theoretical Physics, University of Leiden, 9506 Leiden, Netherlands.
Graduate School and Giannini Foundation, University of California, Berkeley, CA 94720, United States.


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.

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