While I was bidding for a product I so wanted to buy on eBay, I had several calculations going in my mind. When you bid for a product there is a certain minimum increment you need to add to your bid in order to outbid the current highest bidder. However, you could have your very own personal upper cap on the product price after which you should (need to) let it go away. And, then there is a time factor, the sooner the bid is going to end, more is the competition (usually). These three parameters and their combinations and poor me! Unfortunately, I managed to lose the bid even if I could have paid more than the winning bid but somehow I lost the product and I couldn’t figure out why. I couldn’t even blame the models. Why? Because I did not use any! Or did I? Of course I did use a model; I just cannot give an expression for it. Apart from the mathematical nature of bidding system, there is my own behavioural influence which played its part given that I could manage to do correct calculations.
There could have been so many combinations of those right tweaks in my own mental eBay bidding model. But hey, my PhD is about the role of modelling uncertainty in decision-making and here I definitely expressed the uncertainty in my model parameters but still I lost. Was my model wrong (whatever it was ;))? Or the tweaks I gave it? Well, I would never know until my next bidding but this forced me to think about the other side of the uncertainty based decision-making process.
During my on-going PhD, I got opportunities to interact with people working in the wastewater management industry through the QUICS training network, conferences etc. where I could get some insights from these practitioners on the possible implications of my research. My research talks about the role of modelling uncertainty in the decision-making process involving water quality failure and at the face of it, it does look very exciting. Because when we talk about uncertainty based decision making, we tend to focus on the benign nature of uncertainty quantification of models and the fruit it should bear when we apply it.
These comments might have a slight pessimistic flavour and seem ironic too, given my PhD heavily relies on demonstrating the benign aspects of the aforementioned process. But just like any tool, application of uncertainty in decision-making can have some serious downsides too if it’s not done ‘properly’.
“How do we define what is the ‘proper’ way?” And, more importantly “who defines this ‘proper’ way?”
There are three major players who can influence the impact of sewer system on the rivers, lakes or ponds: the government, water utility companies and academia. Academia finds better ways (read models) to explain the physical behaviour of the system. Water utility companies (should) apply the state of the art to make decisions or action plans. Government directly/indirectly facilitates the exchange between academia and the water utilities. In addition, water quality regulations are put into place by the government such as the Environment Agency (EA) in the UK, to ensure that the water utilities within its jurisdiction adhere to their commitments to preserve the sanctity of the water bodies.
In all these aforementioned activities or transactions, models serve as currency. Similar to any currency, there have been initiatives to standardise this currency as well. For example, the Urban Drainage Group (UDG, formerly WaPUG) from the Chartered Institution of Water and Environmental Management (CIWEM) in the UK issues guidelines and codes of practices for hydraulic and quality modelling of sewer systems. In theory, this ensures that a particular modelling standard is adopted across different water utility companies. However, the extent to which these codes of practices are applied across the industry is a matter of further discussion but the underlying message is that there is an existing effort to promote uniform modelling practice on which basis the environmental regulation authorities can further build up a standardised regulation framework on the sewer system emissions.
Unless we live in a utopian world, these models do have inherent uncertainty in their representation. Research Projects like QUICS ITN strive towards finding better ways to quantify the uncertainty in these models and promote enthusiasm for increasing practical applications. There are multiple factors in an uncertainty quantification process affecting the outcome of the analysis such as the choice of uncertain model components, their uncertain range, and the types of probability distributions used to represent the uncertainty, and the time horizon.
Various recommendations from academia are available in the form of research articles to guide modellers to control these factors in the uncertainty analysis and use the results to make decisions. Different recommendations or preferences for these multiple factors might result into very different decision outcomes or solutions as the optimal solutions and given the individual choices or guidelines across the industry, all these optimal solutions can be justified unless there is provision to test the performance of these optimal solutions.
In a sense, the use of uncertainty analysis results in making decisions can only be regulated on ad hoc basis unless there is an effort to standardise it like the codes of practices. From my little experience, I could gather that the application of uncertainty analyses is still in early stage across the wastewater management industry. Perhaps this is the right time and a huge step forward if the environmental regulatory authorities with the help of academia and the practitioners could come up with a set of guidelines on uncertainty quantification of hydrodynamic and water quality models which can serve as a standard for communication between these three stakeholders.
ESR, University of Sheffield