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Original Research Article

POLITICALLY INSTRUMENTALIZED TERRORISM DYNAMICS (PITD): A STOCHASTIC SPATIOTEMPORAL DIFFERENTIAL GAME FRAMEWORK FOR MODELING ELECTORAL VIOLENCE AND SOCIOECONOMIC DISRUPTION IN NIGERIA

ISRAEL JACOB UDOH, Thomas I. Imalerio, Christopher Eraye Michael, Saratu Tanimu Galma

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ISRAEL JACOB UDOH Corresponding Author

APPLIED MATHEMATICS & SIMULATION ADVANCED RESEARCH CENTRE (AMSARC), SHEDA SCIENCE & TECHNOLOGY COMPLEX (SHESTCO)

ORCID: https://orcid.org/0000-0001-6042-6212

Correspondence: ij.udoh@shestco.gov.ng

Received
24 Jun 2026
Published
14 Jul 2026

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.

Keywords: Nonlinear Dynamics, Backward Bifurcation, Stochastic Differential Equations, Complex Networks, Nash Differential Games, Political Economy of Terrorism.

How to Cite

APA

UDOH, I. J., Imalerio, T. I., Michael, C. E., & Galma, S. T. (2026). POLITICALLY INSTRUMENTALIZED TERRORISM DYNAMICS (PITD): A STOCHASTIC SPATIOTEMPORAL DIFFERENTIAL GAME FRAMEWORK FOR MODELING ELECTORAL VIOLENCE AND SOCIOECONOMIC DISRUPTION IN NIGERIA. Journal of Contemporary Academic Research and Methodologies, 1(5). https://doi.org/10.5281/zenodo.21352135

MLA

UDOH, ISRAEL JACOB, Thomas I. Imalerio, Christopher Eraye Michael, and Saratu Tanimu Galma. "POLITICALLY INSTRUMENTALIZED TERRORISM DYNAMICS (PITD): A STOCHASTIC SPATIOTEMPORAL DIFFERENTIAL GAME FRAMEWORK FOR MODELING ELECTORAL VIOLENCE AND SOCIOECONOMIC DISRUPTION IN NIGERIA." Journal of Contemporary Academic Research and Methodologies, vol. 1, no. 5, 2026. DOI: https://doi.org/10.5281/zenodo.21352135

Chicago

UDOH, ISRAEL JACOB, Thomas I. Imalerio, Christopher Eraye Michael, and Saratu Tanimu Galma. "POLITICALLY INSTRUMENTALIZED TERRORISM DYNAMICS (PITD): A STOCHASTIC SPATIOTEMPORAL DIFFERENTIAL GAME FRAMEWORK FOR MODELING ELECTORAL VIOLENCE AND SOCIOECONOMIC DISRUPTION IN NIGERIA." Journal of Contemporary Academic Research and Methodologies 1, no. 5 (2026). https://doi.org/10.5281/zenodo.21352135

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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ISSN 3139-7247
Tracking ID JCARM_JUN_26_134
Article No. 035
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Article Info
Journal JCARM
Volume Vol 1, No 5
Year 2026
Type Original Research Article
Licence CC BY-NC-SA 4.0
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