The profession draws on expertise in a number of areas and does not fit neatly into any single category... Simulation “overlaps engineering, math and computer science, but it isn’t the same as any one of those,” .... [S]kills include … an aptitude for “conceptualizing the world,” he said. Developing a simulation requires enough native intelligence to view a problem abstractly, research the issues and tease out the myriad key elements. Then they must be incorporated into a model in which they are poked, prodded and tweaked to reach useful solutions.
Two other very important skills for those who use simulations are noted.
- First, as one pares down the real world to its essential elements for a specific model, an equally important skill is understanding the of limits the model—what is left out, what is not well understood, and what is “uncalibrated” (this is, not numerically reconciled with real-world data).
- Second, one needs to be very clear in communicating the correct way in which to interpret results.
Too often there is too much confidence placed in the results of a model, as illustrated in this article, also in the NY Times (ht Franz Dill). In this case, an underestimation of the spread of the Swine Flu, which was officially declared a pandemic by the WHO last week.
Disappointment and back-pedaling occurs when too much confidence is placed in a model. This lowers the credibility of modeling, and also dampens the desire to apply resources to improving the model.
A model should not be an end in itself, but a conversation. It is only through the crucible of testing, critique, discussion and refinement that a model can converge on reality. Key to this process is engaging others to use a model themselves, understand how that model works, and be able to suggest (and ideally implement) changes to improve the model. This is an important goal of the platform we’re developing.