Jim responds with thoughts about how to think of non-GDP losses from Global Warming. You should read it. Several very smart people have brought up Indur Goklany to me now, so I’ll not comment till I become very familar with his work. One more thing about climate change:
In economics there is something called Knightian Uncertainty. In quant circles, it can be called “model risk.” In everyday circles, it can be called “you don’t know what the f*** you are talking about.” Depending on your line of work, you’ve probably been there. You see someone present a model, a presentation, a research idea, or a business investment plan, and there are all kinds of charts and diagrams and numbers and powerpoint. During the Q and A, if you are lucky, someone will say “What if you are wrong?”, and they’ll respond “well if the distribution is misspecified…” and hopefully they’ll be cut off “no, what if everything you have done is completely wrong. Where would that leave us?”
As someone doing financial engineering, it probably would have been helpful to have been asked that question more in the past decade. So I want to ask, “what if all these climate models are radically under-predicting black swams and other tail risk?” To get a sense, I used an idea from Weitzman’s paper on uncertainity and went to the IPPC “The Physical Science Basis” (chapter 10, box 10.2), and got a list of a dozen and a half models that have tried to predict the increase in temperatures. These are all peer-reviewed, and (at the time in 2007) considered the latest and best research from all the fields. How do they compare to each other:
I’m particularly interested in the second diagram. Note at the 50% likely event (.5 cumulative probability, on the Y-axis), most of them are bunched up at the 3 C (5.4 F) mark, with a few less than. On average, these models all predict the same thing. Now look at the .9 mark. This is the 10% unlikely to happen confidence interval. If we under-predicted tail risk, if there are speeding and acceleration mechanisms we did not anticipate, we’ll end up out here. And here the models are all over the place. You can pick any degree between 4 C (7.2 F) and 8.5 C (15.3 F)* and find a model to support it. There’s a bit of a mass around 5 C (9 F) increase, but not like on the average.
Now that is the 10% interval. Weitzman, when he crunches this chart (and another set of companion charts in Chapter 9, Table 9.3), finds the 5% interval at, on average, 7 C (12.6 F) and 1% interval at 10 C (18 F). I won’t go further into his implications of this for pricing global warming (see here for a good overview).
I find it very helpful in modeling, especially with tail risk, to use different models, implementations and assumptions carried out from different people and see how they relate to each other. Looking at this, it seems everyone is in agreement on average. But if things go worse, it can go way worse than expected. Now that we’ve just lived through an empirical experiment in how well the best modeling can predict tail risk, I tend to look closely at that 10% marker. And the uncertainty there has me worried.
I’m reading this as “on average” everyone is in agreement, but “if things go worse than planned” everything is up for grabs, presumably because everyone is looking at different things that could go wrong.
* – See what I meant by having the Fahrenheit unit there?**
** – I’ve started Infinite Summer, so get ready for lots blogging footnotes.