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Title: An opinion-driven behavioral dynamics model for addictive behaviors

Abstract

We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual’s behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Additionally, individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters provide targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. Furthermore, this has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.

Authors:
 [1];  [1];  [2];  [2];  [1];  [1];  [2];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. U.S. Food and Drug Administration, Rockville, MD (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1140312
Report Number(s):
SAND-2014-1225J
Journal ID: ISSN 1434-6028; PII: 1579
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
European Physical Journal. B, Condensed Matter and Complex Systems
Additional Journal Information:
Journal Volume: 88; Journal Issue: 4; Journal ID: ISSN 1434-6028
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; statistical and nonlinear physics

Citation Formats

Moore, Thomas W., Finley, Patrick D., Apelberg, Benjamin J., Ambrose, Bridget K., Brodsky, Nancy S., Brown, Theresa J., Husten, Corinne, and Glass, Robert J. An opinion-driven behavioral dynamics model for addictive behaviors. United States: N. p., 2015. Web. doi:10.1140/epjb/e2015-40462-y.
Moore, Thomas W., Finley, Patrick D., Apelberg, Benjamin J., Ambrose, Bridget K., Brodsky, Nancy S., Brown, Theresa J., Husten, Corinne, & Glass, Robert J. An opinion-driven behavioral dynamics model for addictive behaviors. United States. doi:10.1140/epjb/e2015-40462-y.
Moore, Thomas W., Finley, Patrick D., Apelberg, Benjamin J., Ambrose, Bridget K., Brodsky, Nancy S., Brown, Theresa J., Husten, Corinne, and Glass, Robert J. Wed . "An opinion-driven behavioral dynamics model for addictive behaviors". United States. doi:10.1140/epjb/e2015-40462-y. https://www.osti.gov/servlets/purl/1140312.
@article{osti_1140312,
title = {An opinion-driven behavioral dynamics model for addictive behaviors},
author = {Moore, Thomas W. and Finley, Patrick D. and Apelberg, Benjamin J. and Ambrose, Bridget K. and Brodsky, Nancy S. and Brown, Theresa J. and Husten, Corinne and Glass, Robert J.},
abstractNote = {We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual’s behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Additionally, individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters provide targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. Furthermore, this has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.},
doi = {10.1140/epjb/e2015-40462-y},
journal = {European Physical Journal. B, Condensed Matter and Complex Systems},
number = 4,
volume = 88,
place = {United States},
year = {2015},
month = {4}
}

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