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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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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