3.3. Conclusion

The use of mathematical models and simulations avoids actual experimentation, which can be costly and time consuming. Instead, mathematical knowledge and computational power is used to solve real-world problems cheaply and in a time efficient manner. As such, modeling and simulation can facilitate understanding a system’s behavior without actually testing the system in the real world. For example, to determine which type of spoiler would improve traction the most while designing a race car, a computer simulation of the car could be used to estimate the effect of different spoiler shapes on the coefficient of friction in a turn. Useful insights about different decisions in the design could be gleaned without actually building the car. In addition, simulation can support experimentation that occurs totally in software, or in human-in-the-loop environments 1 where simulation represents systems or generates data needed to meet experiment objectives. Furthermore, simulation can be used to train persons using a virtual environment that would otherwise be difficult or expensive to produce.

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1

Human-in-the-loop environments are interactive settings in which human operators or participants are directly involved in the simulation or experimentation process. In these environments, simulations generate data or represent systems in real-time, allowing human input to influence the outcomes and decisions. This approach combines the computational power of simulations with human judgment and decision-making to achieve more accurate and relevant results.