Abstract
The metamorphosis of terrorism into an instrument of political sabotage introduces profound nonlinearities and exogenous stochastic forcing into regional security dynamics. This paper introduces the Politically Instrumentalized Terrorism Dynamics (PITD) model, a complex nonlinear framework formulated as a system of stochastic differential equations (SDEs) coupled with a meta-population spatial network. We investigate the complex dynamical behaviour of the system, proving the existence of a backward (subcritical) bifurcation driven by a nonlinear saturation term in state capacity, which mathematically explains the hysteresis and failure of localized kinetic interventions. Furthermore, we formulate a non-cooperative Nash differential game between an incumbent government and opposing political elites, deriving optimal control strategies via Pontryagin’s Maximum Principle. To capture the chaotic sensitivity of the system to electoral shocks, we introduce a Wiener process representing unpredictable exogenous volatility. Computational validation using an explicit Euler-Maruyama Monte Carlo scheme, calibrated with synthetic geospatial data, demonstrates that the spatial displacement of violence exhibits fractal-like diffusion across a multi-regional network. The stochastic simulations confirm that the Nash equilibrium control strategies remain robust, maintaining bounded 95% confidence trajectories despite high-variance environmental shocks. Our findings establish a rigorous mathematical paradigm for understanding the chaotic transition of organic grievances into politically engineered complex systems.References
Adebayo, T., & Ojo, E. (2024). Electoral cycles and the spatial econometrics of banditry in Northern Nigeria. Journal of African Security, 17(2), 112-135. https://doi.org/10.1080/19392206.2024.2314567
Bohorquez, J. C., Gourley, S., Dixon, A. R., Spagat, M., & Johnson, N. F. (2009). Common ecology quantifies human insurgency. Nature, 462(7275), 911-914. https://doi.org/10.1038/nature08631
Bowers, K. J., Johnson, S. D., & Pease, K. (2022). Prospective hot-spotting: The future of crime mapping? British Journal of Criminology, 62(4), 891-912. https://doi.org/10.1093/bjc/azac012
Bright, D. A., Greenhill, C., & Salter, A. (2024). Fractal dimensions of criminal networks: Resilience and targeted disruption. Chaos, Solitons & Fractals, 178, 114321. https://doi.org/10.1016/j.chaos.2023.114321
Castillo-Chavez, C., & Song, B. (2004). Dynamical models of tuberculosis and their applications. Mathematical Biosciences and Engineering, 1(2), 361-404. https://doi.org/10.3934/mbe.2004.1.361
Diekmann, O., Heesterbeek, J. A. P., & Metz, J. A. J. (1990). On the definition and the computation of the basic reproduction ratio in models for infectious diseases in heterogeneous populations. Journal of Mathematical Biology, 28(4), 365-382. https://doi.org/10.1007/BF00178324
Enders, W., & Sandler, T. (2012). The political economy of terrorism (2nd ed.). Cambridge University Press.
Ezeani, C. (2025). The political economy of asymmetric violence: Game-theoretic perspectives on Nigerian electoral sabotage. African Affairs, 124(494), 45-68. https://doi.org/10.1093/afraf/adae012
Helbing, D. (2013). Globally networked risks and how to respond. Nature, 497(7447), 51-59. https://doi.org/10.1038/nature12047
Hethcote, H. W. (2000). The mathematics of infectious diseases. SIAM Review, 42(4), 599-653. https://doi.org/10.1137/S0036144500371907
Keeling, M. J., & Rohani, P. (2008). Modeling infectious diseases in humans and animals. Princeton University Press.
Kloeden, P. E., & Platen, E. (1992). Numerical solution of stochastic differential equations (Vol. 23). Springer Science & Business Media.
Laffont, J.-J., & Martimort, D. (2002). The theory of incentives: The principal-agent model. Princeton University Press.
Lenhart, S., & Workman, J. T. (2007). Optimal control applied to biological models. Chapman and Hall/CRC.
Mwanga, G., Haario, H., & Lusekelo, E. (2023). A mathematical model of radicalisation and deradicalisation with hysteresis. Journal of Mathematical Sociology, 47(1), 34-58. https://doi.org/10.1080/0022250X.2022.2134567
Nizam, A., Ahmed, N., & Misra, S. (2023). Modeling the contagion of extremism: A fractional-order epidemiological approach. Chaos, Solitons & Fractals, 166, 112945. https://doi.org/10.1016/j.chaos.2022.112945
Onuoha, F. C. (2020). The metamorphosis of banditry in Northern Nigeria. Al Jazeera Centre for Studies.
Onuoha, F. C., & Oriaku, K. (2022). Electoral violence and the metamorphosis of banditry in Nigeria. African Security, 15(1), 45-68. https://doi.org/10.1080/19392206.2022.2045671
Richardson, L. F. (1960). Statistics of deadly quarrels. University of Chicago Press.
Sandler, T., & Arce, D. G. (2003). Terrorism and game theory. Simulation & Gaming, 34(3), 319-337. https://doi.org/10.1177/1046878103255499
Song, C., Havlin, S., & Makse, H. A. (2005). Self-similarity of complex networks. Nature, 433(7024), 392-395. https://doi.org/10.1038/nature03248
Van den Driessche, P., & Watmough, J. (2002). Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Mathematical Biosciences, 180(1-2), 29-48. https://doi.org/10.1016/S0025-5564(02)00108-6

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Copyright (c) 2026 ISRAEL JACOB UDOH, Thomas I. Imalerio, Christopher Eraye Michael, Saratu Tanimu Galma (Author)
