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Information Recovery in Behavioral Networks

Squartini, Tiziano and Ser-Giacomi, Enrico and Garlaschelli, Diego and Judge, George Information Recovery in Behavioral Networks. PloS One, 10 (5). e0125077. ISSN 1932-6203 (2015)

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Abstract

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.

Item Type: Article
Identification Number: 10.1371%2Fjournal.pone.0125077
Projects: EU FET project MULTIPLEX Nr. 317532, PNR National Project CRISIS-Lab
Uncontrolled Keywords: Entropy, Network analysis, Optimization, Probability distribution
Subjects: H Social Sciences > HB Economic Theory
Q Science > QA Mathematics
Research Area: Economics and Institutional Change
Depositing User: Caterina Tangheroni
Date Deposited: 05 Nov 2015 14:21
Last Modified: 07 Apr 2016 10:10
URI: http://eprints.imtlucca.it/id/eprint/2830

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