Abstract
Addiction is a complex phenomenon, stemming from environmental, biological and psychological causes. It is defined as a natural response of the body to external stimuli, such as drugs, alcohol, but also job, love and Internet technologies, that become compulsive needs, difficult to remove. At the neurological level, the Dopamine System plays a key role in the addiction process. Mathematical models of the Dopamine System have been proposed to study addiction to nicotine, drugs and gambling. In this paper, we propose a Hybrid Automata model of the Dopamine System, based on the mathematical model proposed by Gutkin et al. Our model allows different kinds of addiction causes to be described. In particular, we consider the problem of Internet addiction and its spread through interaction on social networks. This study is undertaken by performing simulations of virtual social networks by varying the network topology and the interaction propensity of users. We show that scale-free networks favour the emergence of addiction phenomena, in particular when users having a high propensity to interaction are present.
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References
Web page with the scripts used in this paper. http://www.di.unipi.it/msvbio/software/InternetAddiction.html
Alexander, B.K., Hadaway, P.F.: Opiate addiction: the case for an adaptive orientation. Psychol. Bull. 92(2), 367 (1982)
Alur, R., Courcoubetis, C., Henzinger, T.A., Ho, P.-H.: Hybrid automata: an algorithmic approach to the specification and verification of hybrid systems. In: Grossman, R.L., Nerode, A., Ravn, A.P., Rischel, H. (eds.) HS 1991-1992. LNCS, vol. 736, pp. 209–229. Springer, Heidelberg (1993). https://doi.org/10.1007/3-540-57318-6_30
Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Bollobás, B., Borgs, C., Chayes, J., Riordan, O.: Directed scale-free graphs. In: Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 132–139. Society for Industrial and Applied Mathematics (2003)
Corrigall, W.A., Franklin, K.B.J., Coen, K.M., Clarke, P.B.S.: The mesolimbic dopaminergic system is implicated in the reinforcing effects of nicotine. Psychopharmacology 107(2), 285–289 (1992)
Ebel, H., Mielsch, L.I., Bornholdt, S.: Scale-free topology of e-mail networks. Phys. Rev. E 66(3), 035103 (2002)
Greenfield, S.: Mind Change: How Digital Technologies are Leaving Their Mark on Our Brains. Random House, New York (2015)
Gutkin, B.S., Dehaene, S., Changeux, J.-P.: A neurocomputational hypothesis for nicotine addiction. Proc. Natl. Acad. Sci. U.S.A. 103(4), 1106–1111 (2006)
Henzinger, T.A.: The theory of hybrid automata. In: Inan, M.K., Kurshan, R.P. (eds.) Verification of Digital and Hybrid Systems. NATO ASI Series (Series F: Computer and Systems Sciences), vol. 170, pp. 265–292. Springer, Heidelberg (2000). https://doi.org/10.1007/978-3-642-59615-5_13
Hauberg, S., Eaton, J.W., Bateman, D., Wehbring, R.: GNU Octave Version 3.8.1 Manual: A High-Level Interactive Language for Numerical Computations. CreateSpace Independent Publishing Platform (2014). ISBN 1441413006
Kuss, D.J., Griffiths, M.D.: Online social networking and addiction? A review of the psychological literature. Int. J. Environ. Res. Public Health 8(9), 3528–3552 (2011)
Nasti, L.: Modelling and simulation of dopaminergic system. The case of internet addiction. Master thesis, University of Pisa, Largo Bruno Pontecorvo, 3, 56127, Pisa, Italy (2016)
Raskin, R., Terry, H.: A principal-components analysis of the narcissistic personality inventory and further evidence of its construct validity. J. Pers. Soc. Psychol. 54(5), 890 (1988)
Redish, D.A.: Addiction as a computational process gone awry. Science 306(5703), 1944–1947 (2004)
Roberts, A.J., Koob, G.F.: The neurobiology of addiction: an overview. Alcohol Res. Health 21(2), 101 (1997)
Samson, R.D., Frank, M.J., Fellous, J.-M.: Computational models of reinforcement learning: the role of dopamine as a reward signal. Cogn. Neurodyn. 4(2), 91–105 (2010)
Smith, K.P., Christakis, N.A.: Social networks and health. Annu. Rev. Sociol. 34, 405–429 (2008)
Syvertsen, T.: “Caught in the Net”: online and social media disappointment and detox. In: Media Resistance, pp. 77–97. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-46499-2_5
Zhao, S., Grasmuck, S., Martin, J.: Identity construction on Facebook: digital empowerment in anchored relationships. Comput. Hum. Behav. 24(5), 1816–1836 (2008)
Acknowledgement
We thank Prof. Gerald Moore (Durham University) for comments and discussions on the preliminary phases of this work. This work has been supported by the project “Metodologie informatiche avanzate per l’analisi di dati biomedici (Advanced computational methodologies for the analysis of biomedical data)” funded by the University of Pisa (PRA_2017_44).
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Nasti, L., Milazzo, P. (2018). A Computational Model of Internet Addiction Phenomena in Social Networks. In: Cerone, A., Roveri, M. (eds) Software Engineering and Formal Methods. SEFM 2017. Lecture Notes in Computer Science(), vol 10729. Springer, Cham. https://doi.org/10.1007/978-3-319-74781-1_7
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DOI: https://doi.org/10.1007/978-3-319-74781-1_7
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