A Mathematical Model of the Dynamics of Crypto Currency Mining Addiction

Ali, Inalegwu Michael, Raymond Dominic, Ajor, Emmanuel Otom

doi:https://doi.org/10.59232/MS-V3I3P101

Research Article | Open Access | Download Full Text

Volume 3 | Issue 3 | Year 2025 | Article Id: MS-V3I3P101 DOI: https://doi.org/10.59232/MS-V3I3P101

A Mathematical Model of the Dynamics of Crypto Currency Mining Addiction

Ali, Inalegwu Michael, Raymond Dominic, Ajor, Emmanuel Otom

Received	Revised	Accepted	Published
01 Jun 2025	02 Jul 2025	03 Aug 2025	15 Aug 2025

Citation

Ali, Inalegwu Michael, Raymond Dominic, Ajor, Emmanuel Otom. “A Mathematical Model of the Dynamics of Crypto Currency Mining Addiction.” DS Journal of Modeling and Simulation, vol. 3, no. 3, pp. 1-15, 2025.

Abstract

In order to understand the dynamics of cryptocurrency mining addiction and possibly eliminate the rate at which people become addicted, a six-compartmental model that includes the susceptible human population, the newbies, the addiction class, counselling, and recovery class was formulated. The local equilibrium points were properly analyzed, and it was shown that the equilibrium points are unstable whenever the fundamental addiction number is greater than one (R0>1) and locally asymptotically stable whenever it is less than one (R0<1). The result of the basic addiction number Ro which determines whether addiction will die off or persist was carefully calculated using the method of next generation matrix and it was revealed that people will become more addicted to crypto currency mining whenever Ro >1and if Ro < 1, the society will become gradually free from such addiction. Sensitivity analysis was performed on basic reproduction number; numerical simulation was carried out using MAPLE 18 software to analyze the transmission/spread of the addiction, and the finding shows that in order to have a society with lesser number of addicted individuals, there should be adequate counseling and awareness of the dangers associated to crypto-currency mining addiction.

Keywords

Cryptocurrency, Mining, Addiction, Mathematical modeling, Sensitivity index.

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