A fuzzy transmission model analysis of corruption dynamics

Abstract

In this study, we propose a corruption model that incorporates fuzzy transmission dynamics. We consider a population of individuals, each characterized by a corruption level ranging from 0 to 1, representing their degree of engagement in corrupt practices. The transmission of corruption is not strictly binary but rather influenced by fuzzy logic, where individuals with higher corruption levels are more likely to influence others towards corruption.

To analyze the dynamical behavior of the corruption model, we employ mathematical techniques from fuzzy systems theory and dynamical systems theory. We investigate how the corruption levels evolve over time, considering factors such as social interactions, institutional interventions, and individual tendencies.

Through simulations and mathematical analysis, we explore various scenarios and observe interesting dynamical behaviors. These include the emergence of corruption clusters , the impact of anti-corruption measures on reducing corruption levels, and the potential for corruption to spread or decline based on different initial conditions and external factors.

Our findings highlight the importance of considering fuzzy transmission dynamics in corruption models, as it provides a more realistic representation of corruption in society. This research contributes to a better understanding of the complexity of corruption and provides insights for policymakers and anti-corruption agencies in designing effective strategies to combat corruption and promote ethical behavior in society.