Exploring Corruption Through Mathematical Modeling and Optimal Control Analysis: Examining Interactions Between Students and Employees

Corruption, defined as the misuse of authority or resources for personal gain, diverts critical resources away from essential sectors, undermining development, governance, state authority, and social cohesion. It also perpetuates other illicit activities, compounding its societal impact. Despite the ability of mathematical modeling to effectively represent complex processes, its potential in addressing corruption remains largely untapped. This study introduces a deterministic nonlinear model to analyze the dynamics of corruption using optimal control methods. Drawing on compartmental modeling, widely employed in studying the spread of phenomena analogous to infectious diseases, we adapt this framework to explore corruption within education systems. The model’s equilibria are determined, and their stability is rigorously analyzed. By leveraging Lipschitz conditions, we prove the existence and uniqueness of solutions, ensuring mathematical robustness. Our results indicate that a corruption‐free equilibrium is stable when the basic reproduction number, , is less than one. Conversely, when , an endemic equilibrium emerges, signifying the persistence of corruption within the system. To mitigate this, we optimize control strategies based on the Pontryagin’s maximum principle. Analytical findings are further validated through comprehensive numerical simulations, providing actionable insights into the control and reduction of corruption.

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Publication Details

Journal
Journal of Mathematics
Published
2025-01-01
DOI
https://doi.org/10.1155/jom/2621856
Citations
1
Primary Topic
Mathematical and Theoretical Epidemiology and Ecology Models
Type
article
Field-Weighted Citation Impact
1.88

Funders

Controls
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article

Exploring Corruption Through Mathematical Modeling and Optimal Control Analysis: Examining Interactions Between Students and Employees

Aeshah A. Raezah, Yassine Sabbar, Mohammed El Khomssi, Saida Id Ouaziz
1 citations
Journal of Mathematics
Mathematical and Theoretical Epidemiology and Ecology Models
1.88
article

Exploring Corruption Through Mathematical Modeling and Optimal Control Analysis: Examining Interactions Between Students and Employees

Aeshah A. Raezah, Yassine Sabbar, Mohammed El Khomssi, Saida Id Ouaziz
article en
1 citations

Abstract

Corruption, defined as the misuse of authority or resources for personal gain, diverts critical resources away from essential sectors, undermining development, governance, state authority, and social cohesion. It also perpetuates other illicit activities, compounding its societal impact. Despite the ability of mathematical modeling to effectively represent complex processes, its potential in addressing corruption remains largely untapped. This study introduces a deterministic nonlinear model to analyze the dynamics of corruption using optimal control methods. Drawing on compartmental modeling, widely employed in studying the spread of phenomena analogous to infectious diseases, we adapt this framework to explore corruption within education systems. The model’s equilibria are determined, and their stability is rigorously analyzed. By leveraging Lipschitz conditions, we prove the existence and uniqueness of solutions, ensuring mathematical robustness. Our results indicate that a corruption‐free equilibrium is stable when the basic reproduction number, , is less than one. Conversely, when , an endemic equilibrium emerges, signifying the persistence of corruption within the system. To mitigate this, we optimize control strategies based on the Pontryagin’s maximum principle. Analytical findings are further validated through comprehensive numerical simulations, providing actionable insights into the control and reduction of corruption.

Journal of MathematicsVol. 2025(1)
Instituto Superior da Maia (PT), Sidi Mohamed Ben Abdellah University (MA), King Khalid University (SA)
King Khalid University
Openalex Percentile: Top 18%
Mathematical and Theoretical Epidemiology and Ecology Models
1.88
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