Crystal ball gazing, the academic way

The excitement of international science workshops

So I'm in this international research project VECTORS, dealing with many exciting and fascinating issues, working with scientists from all over Europe. But if I tell you I was at a VECTORS workshop in Edinburgh last week, it's not exactly like we were sipping 40 year old Talisker in Edinburgh Castle with Queen Elisabeth. A business hotel near the airport is more like it: easier to reach, lots of meeting rooms, good food, and no distractions.

Scenarios were the main theme this time. A lot of the developments that VECTORS deals with are highly uncertain. How warm (or cold) will our waters be in the coming 50 years? How many sunbathers will visit Mediterranean beaches? How many international transport vessels will visit the ports of Rotterdam and Antwerp?

You may be tempted to address these questions through some sort of sensitivity analysis: do your calculations for different values of the uncertain variables, or rather, combinations of different values. If you know the probability of some outcome of some variable (say, "the probability that temperatures increase by 1 degree or more is 30%") you could estimate the probability of some outcome of your models' output ("the probability that average income increases by €100 per month or more is 50%"). But you will run into a number of problems:

• You don't know probabilities.
• Even if you knew the probabilities they would not be independent. A high nutrient load is more likely if there are many people around spending their money on lots of meat or highly fertilised crops than if the population is small and poor.
• The number of possible combinations grows exponentially with the number of variables. Suppose you want to consider three levels of each variable. Then one variable gives you three possible outcomes; two variables have nine possible outcomes; three variables have 27 possible outcomes; 10 variables have 59,049 possible outcomes. And then the ecologists in your team tell you that running one such outcome on their model takes one month. So if you have 5000 years you can do a sensitivity analysis of all 10 variables!
• There are many other possible developments that are difficult to capture in numbers, such as changes in regulations, customs, technologies, and so on.
• Policy makers have neither the time nor the energy to read your entire sensitivity analysis. They want something you can summarise in one page (which they skim rather than read).

So what do we do? We develop scenarios. But what are scenarios?

A scenario is not a prediction of the future. It is more like a story line that describes how the world might develop in the future, taking into account different possible trends. It should be consistent, and ideally you have a set of scenarios that spans a fairly wide range of possible developments.

A scenario is not a policy option. When you see the different scenarios from, say, the IPCC, you may be tempted to say: "let's go for this one." But although you may certainly like some outlooks more than others, the idea is that you don't know which one will come true - and you have no influence on which one comes true.

At least, scenarios can help us to make sense of the complexity of the different social, economic, political, and biophysical changes that may take place in the future. A few consistent story lines are easier to understand than countless histograms and plots, for scientists as well as policy makers. And they still allow us to explore how different policy choices may work out in the future.

At some point I joked that we should have Philip Pullman write our scenarios, but I was only half joking. It's at least as much an art as it is a science.