About Simulation Models

Simulation Models: A Brief Overview

Simulation models are computerized mathematical programs used to evaluate systems virtually before implementing them in the real world. These models can be used to reduce the complexity necessary in testing a physical system, accurately forecast the probable outcomes and hazards associated in a decision, and gain insight into how the system operates.

The Early Days: Buffon and Laplace

In 1777, the first application of a simulation model took place. In order to determine the likelihood of a needle dropped from a plane with parallel lines landing across a boundary, Claudio Buffon used a mathematical model (Rossetti, 2016). In 1812, Pierre Laplace enhanced Buffon’s design. Notably, a statistician named William Gosset applied mathematical models in experimentation for enhanced barley production levels and development of better barley strains (Rossetti, 2016). Gosset’s model invention known as the “t-distribution” is still used in statistics to date. Gosset’s work created an avenue for the application of simulation models in the experimentation, analysis, and provision of solutions to engineering and technical complexities.

The Rise of 3-D Simulation

The technological developments experienced in the 1940s accelerated the use of simulation models with the introduction of electronic computers. The upshot of the advancement in computers in the 1950s and 1960s was the rapid simulation modeling. The 21st century is now experiencing three-dimensional (3-D) simulation whereby computers create virtual prototypes that closely resemble the real-life systems (Rossetti, 2016). The 3-D simulation produces actual outcomes during analysis which enhances precision in decision making.

Monte Carlo Simulation: A Tool for Risk Assessment

Monte Carlo simulation applications are computer-based mathematical models utilized for risk assessment in decision-making processes and quantitative analysis (Binder & Heermann, 2013). This simulation application produces different outcomes and probabilities of occurrence depending on the random variables input. The variations in the output favor the Monte Carlo simulation in understanding the uncertainty and risks involved in forecasting models.

The Future of Simulation Models

Conclusively, simulation models have been incorporated in all sectors of business, science, and technology. Therefore, the potential use of simulation models in future spreads across all disciplines. The specific examples may include experimentation of complex surgical procedures in the medical field, assessment of risks in oil drilling, experimentation of the climatic conditions in astronomical studies, and understanding atomic behavior in particle physics.

References

Binder, K., & Heermann, D. W. (2013). Monte Carlo Simulation in Statistical Physics: An Introduction (2nd ed.). New York, NY: Springer-Verlag.

Rossetti, M. D. (2016). Simulation Modeling and Arena (2nd ed.). Hoboken, NJ: John Wiley & Sons.