Abstract
Computer simulation and optimization are mathematical modeling methods designed to provide analysis and decision support for systems-level problems, particularly those involving great uncertainty. They are thus well-suited to and increasingly used in disaster applications. Proper implementation of the methods can benefit greatly from collaboration with social scientists, particularly in the formulation and interpretation phases of the modeling process. This chapter offers a high-level introduction that aims to equip readers with a vocabulary and understanding of simulation and optimization to encourage and enable them to participate effectively on disaster projects involving these methods. Specifically, we provide an overview of engineering mathematical models in general, and for each specific method, we describe the basics of how it works, types, strengths and limitations, and examples applications in disaster research. The opportunities and potential challenges of increased collaboration between social scientists and engineers around the use of computer simulation and optimization are also discussed.
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Notes
- 1.
A random variable is a variable that can take on different possible values each with an associated probability. It is represented by a probability distribution—a probability mass function (pmf) if discrete, or a probability density function (pdf) if continuous.
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Davidson, R.A., Nozick, L.K. (2018). Computer Simulation and Optimization. In: RodrÃguez, H., Donner, W., Trainor, J. (eds) Handbook of Disaster Research. Handbooks of Sociology and Social Research. Springer, Cham. https://doi.org/10.1007/978-3-319-63254-4_17
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