16.4 Conclusion

16.4.1 Summary

Simulations are a programming technique that force us to explicate premises and processes that are easily overlooked when merely relying on verbal problem descriptions.

In this chapter, we introduced and illustrated two basic types of simulations:

  1. by enumeration (explicating the information contained in a problem description)

  2. by sampling (involving actual randomness and the need for managing repetitions)

Both types of simulations involve probabilistic information, but deal with it in different ways.

When simulating non-trivial problems with many assumptions, even basic simulations often yield unexpected and surprising results. In addition, simulations often allow to evaluate the robustness of phenomena (e.g., the variability in results due to random sampling).

16.4.2 Resources

Pointers to related sources of inspirations and ideas:

On simulations

  • Chapter 20 Simulation (by Peng, 2016) provides a basic introduction into generating random numbers and simple simulations.

On the Monty Hall problem

References

Krauss, S., & Wang, X.-T. (2003). The psychology of the Monty Hall problem: Discovering psychological mechanisms for solving a tenacious brain teaser. Journal of Experimental Psychology: General, 132(1), 3–22. https://doi.org/10.1037/0096-3445.132.1.3
Neth, H., Gradwohl, N., Streeb, D., Keim, D. A., & Gaissmaier, W. (2021). Perspectives on the 2x2 matrix: Solving semantically distinct problems based on a shared structure of binary contingencies. Frontiers in Psychology, 11, 567817. https://doi.org/10.3389/fpsyg.2020.567817
Peng, R. D. (2016). R programming for data science. Leanpub. https://bookdown.org/rdpeng/rprogdatascience/