Tatiana Ermolieva (IME/LUC) presented what will probably be one of the most useful lectures of the YSSP for my research. Uncertainty is inherent in every decision one takes, how do you design a system that copes with it?
There are two types of systems (processes), one is the traditional natural system like physics that is governed by fixed relations, the other is human-related systems governed by old and new policies dependent on decisions of various agents/actors. From this we can distinguish knowable and unknowable (inherent: natural systems) uncertainties.
Human-related systems require a new approaches to stability (security) analysis. There are multiple reasons, the lack of observations, the expense of experimentation, observations contaminated by old policies and observations of results/impacts may come with a delay or cause irreversible change (climate change for instance)
Popular security analysis includes aggregate analysis, least-squares analysis etc but these are not robust solutions, all are sensitive to outliers. I've appropriated the image above because it is one of the best examples I've ever seen of a mean/median/mode argument. The mean is in red, the median in green and the mode in maroon. Notice how most of the information is lost in the case of the mean. Remember: the mean is MEANINGLES! The median is more robust.
A common feature of most systems is a range of possible outcomes resulting from different scenarios or the variability (eg weather). The uncertainty is which scenario comes next?
I'm currently making my way through 'Coping with Uncertainty: Robust Solutions'. Its written by the IME group at IIASA and works through various techniques for robust systems mostly through real-world applications. The UCT library is in the process of buying a copy, it should arrive sometime in the next year :P