Approaches for Streamlining Performance Control by Monte Carlo Modeling

Elena Corina Cipu (), Ruxandra Ioana Cipu and Ştefania Maria Michnea
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Elena Corina Cipu: Faculty of Applied Sciences, Department of Applied Mathematics, Center for Research and Training in Innovative Techniques of Applied Mathematics in Engineering (CiTi), National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania
Ruxandra Ioana Cipu: Faculty of Medical Engineering, Center for Research and Training in Innovative Techniques of Applied Mathematics in Engineering (CiTi), National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania
Ştefania Maria Michnea: Faculty of Medical Engineering, Center for Research and Training in Innovative Techniques of Applied Mathematics in Engineering (CiTi), National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania

Mathematics, 2024, vol. 12, issue 7, 1-17

Abstract: For decades, cancer has remained a persistent health challenge; this project represents a significant stride towards refining treatment approaches and prognostic insights. Proton beam therapy, a radiation therapy modality employing high-energy protons to target various malignancies while minimizing damage to adjacent healthy tissue, holds immense promise. This study analyzes the relationship between delivered radiation doses and patient outcomes, using various approximation functions and graphical representations for comparison. Statistical analysis is performed through the Monte Carlo method based on repeated sampling to estimate the variables of interest in this analysis, namely, the survival rates, financial implications, and medical effectiveness of proton beam therapy. To this end, open-source data from research centers that publish patient outcomes were utilized. The second study considered the estimation of pay gaps that can have long-lasting effects, leading to differences in retirement savings, wealth accumulation, and overall financial security. After finding a representative sample containing the relevant variables that contribute to pay gaps, such as gender, race, experience, education, and job role, MC modeling is used to simulate a range of possible pay gap estimates. Based on the Monte Carlo results, a sensitivity analysis is performed to identify which variables have the most significant impact on pay gaps.

Keywords: Monte Carlo modeling; optimal control; business model; performance control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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