The Monte Carlo Simulation Fair Profit Distribution in a Virtual Team

  • gergana dencheva kalcheva bulgarian
Keywords: simulation, Monte Carlo, virtual team, profit method

Abstract

Abstract: The Monte Carlo simulation model is a stochastic method based on the use of random variables and probabilistic modeling to analyze complex systems and processes. It is widely applied in various fields such as finance, engineering, physics, and logistics, where it is used for uncertainty assessment, optimization, and forecasting. The method allows for the simulation of numerous possible scenarios by generating a large number of random samples, thus providing statistically reliable estimates of the system’s behavior. In this report, the Monte Carlo simulation will be used to distribute profits among members of a virtual team that allocates tasks based on the fair profit method. This approach aims to ensure objectivity and balance between each participant’s contribution and their corresponding compensation.

References

1. Dunn, W.L. and Shultis, J.K., 2009. Exploring Monte Carlo Methods. Burlington: Elsevier.
2. Ekemezie, D., 2015. Understanding Monte Carlo Simulations. Journal of Business and Economic Policy, 2(1), pp.67–74.
3. Environmental Protection Agency (EPA), 1997. Guiding Principles for Monte Carlo Analysis. Washington, DC: United States Environmental Protection Agency.
4. Glasserman, P., 2004. Monte Carlo Methods in Financial Engineering. New York: Springer.
5. Halton, J.H., 1970. A retrospective and prospective survey of the Monte Carlo method. SIAM Review, 12(1), pp.1–63.
6. Hamdy, A.T., 2006. Operations Research: An Introduction. 8th ed. Upper Saddle River: Prentice Hall.
7. Metropolis, N. and Ulam, S., 1949. The Monte Carlo method. Journal of the American Statistical Association, 44(247), pp.335–341.
8. Millares Asset Management, 2009. Monte Carlo Simulation in Investment Planning. [online] Available at: https://www.millaresfunds.com/monte-carlo-simulation [Accessed 18 May 2025].
9. Raychaudhuri, S., 2008. Introduction to Monte Carlo Simulation. In: Proceedings of the 2008 Winter Simulation Conference. Miami, FL, USA, December 2008. Piscataway: IEEE, pp.91–100.
10. Sadus, R.J., 2010. Molecular Simulation of Fluids: Theory, Algorithms and Object-Orientation. Oxford: Elsevier.
11. Saharkhiz, A., 2009. Effective use of Monte Carlo simulation in project risk management. International Journal of Project Management. [online] Available at: https://www.researchgate.net/publication/MonteCarlo_ProjectRisk [Accessed 18 May 2025].
12. Trotter, H.F. and Tukey, J.W., 1954. Conditional Monte Carlo: Some uses.Annals of Mathematical Statistics, 25(3), pp.419–434.
Published
2026-06-30
How to Cite
kalcheva, gergana. (2026). The Monte Carlo Simulation Fair Profit Distribution in a Virtual Team. Vanguard Scientific Instruments in Management, 22(1), 73-85. Retrieved from https://vsim-journal.info/index.php?journal=vsim&page=article&op=view&path[]=581