eprintid: 2830
rev_number: 13
eprint_status: archive
userid: 69
dir: disk0/00/00/28/30
datestamp: 2015-11-05 14:21:49
lastmod: 2018-03-08 16:58:02
status_changed: 2015-11-05 14:21:49
type: article
metadata_visibility: show
creators_name: Squartini, Tiziano
creators_name: Ser-Giacomi, Enrico
creators_name: Garlaschelli, Diego
creators_name: Judge, George
creators_id: tiziano.squartini@imtlucca.it
creators_id: 
creators_id: diego.garlaschelli@imtlucca.it
creators_id: 
title: Information Recovery in Behavioral Networks
ispublished: pub
subjects: HB
subjects: QA
divisions: EIC
full_text_status: public
keywords: Entropy, Network analysis, Optimization, Probability distribution
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.
date: 2015
date_type: published
publication: PloS One
volume: 10
number: 5
publisher: Public Library of Science
pagerange: e0125077
id_number: 10.1371%2Fjournal.pone.0125077
refereed: TRUE
issn: 1932-6203
official_url: http://dx.doi.org/10.1371%2Fjournal.pone.0125077
projects: EU FET project MULTIPLEX Nr. 317532
projects: PNR National Project CRISIS-Lab
citation:   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)  
document_url: http://eprints.imtlucca.it/2830/1/journal.pone.0125077.pdf