PhD: Abstract

What my abstract looks like according to wordle:

IME: Coping with Uncertainty: Spatio-temporal heterogeneities

The second uncertainty seminar dealt with spatio-temporal problems. This seminar series is not meant to teach us new techniques but to create an awareness that one shouldn't automatically use the stock standard stochastic tools available like averaging, regression etc.

When analysing spatio-temporal problems they often have multiple scales, high dimensionality and multimodal dimensions. There are methods available to deal with this, most notably discounting and Fast Monte Carlo simulations. Consider the scatter plot above, its multimodal. How do you describe the multi-dimensional density function?
The Adaptive Monte Carlo STO procedure can be used for optimization module (structural and non-structural decision, production allocation).

All these techniques are all of interest to me currently. I'm currently designing a decision framework and to cope with the uncertainty one needs to implement some sort of robust solution. Note the some sort, I haven't got there yet.

IME: Coping with Uncertainty: Concept of Robust Solutions

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)

Although its impossible to predict human-driven systems, it is possible to find robust solutions good against all uncertainties. These robust policies assist in the long-term stability of systems performance.

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

Decision Analysis: How certain are you?

Its amazing when one wonders through the library at IIASA. Its like stepping back in time to an age of paper journals and stacks. You even have to fill out paper loan forms. The librarian is so helpful and put together a unique reading list to deal with my topics and methodologies. It is the kind of personal service that makes you want to go back time and again. Imagine UCT library with a personalised service.

My current reading list is full of Decision Analysis and Robust Solutions. The founders of decision analysis were all affiliated to IIASA in some form, Ronald Howard being one of them. Its quite scary to grasp just how far their network of influence goes.

Decision Analysis has been around since the 60's, so why am I reading about it now? Bayesian networks, influence diagrams, decision networks are all founded on this concise technique that captures the most incredible amount of data, both subjective and objective. My pile of papers is growing...