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Your search keyword '"Balcı, Mehmet Ali"' showing total 2 results
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2 results on '"Balcı, Mehmet Ali"'

1. Distributed order model of labor migration.

Author

Balcı, Mehmet Ali

Subjects
*LABOR mobility, *FRACTIONAL differential equations, *PARTIAL differential equations, *LABOR supply, *HUMAN migration patterns
Abstract

In this study, we present a mathematical model of socio-economic process of labor force migration. We present our model in the terms of distributed order fractional partial differential equation. The proposed model involves time varying diffusion coefficient. To solve this model we propose an implicit discrete scheme by using graded meshes, and study numerically labor migration flow data from Turkey along with other countries throughout the period of 1966–2014. [ABSTRACT FROM AUTHOR]

Published
2023
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2. Fractional virus epidemic model on financial networks

Author

Mehmet Ali Balcı, MÜ, Fen Fakültesi, Matematik Bölümü, and Balcı, Mehmet Ali

Subjects
Financial networks, General Mathematics, fractional calculus, 01 natural sciences, 010305 fluids & plasmas, Differential transform method, Differential Transform Method, Network Modelling, Stock Market Network, Caputo Fractional Derivative, 0103 physical sciences, QA1-939, 65c20, 010306 general physics, 26a33, Mathematics, 05c82, network modelling, caputo fractional derivative, differential transform method, Fractional calculus, 62p05, 97m30, Fractional Calculus, stock market network, Epidemic model, Mathematical economics
Abstract

In this study, we present an epidemic model that characterizes the behavior of a financial network of globally operating stock markets. Since the long time series have a global memory effect, we represent our model by using the fractional calculus. This model operates on a network, where vertices are the stock markets and edges are constructed by the correlation distances. Thereafter, we find an analytical solution to commensurate system and use the well-known differential transform method to obtain the solution of incommensurate system of fractional differential equations. Our findings are confirmed and complemented by the data set of the relevant stock markets between 2006 and 2016. Rather than the hypothetical values, we use the Hurst Exponent of each time series to approximate the fraction size and graph theoretical concepts to obtain the variables.

Published
2016

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