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Jim Manzi replies to Ryan Avent
The following is a guest essay from Jim Manzi, CEO of Applied Predictive Technologies (APT), an applied artificial intelligence software company. He writes occasionally for National Review and blogs at The American Scene.
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Last week on this site Ryan Avent presented a thoughtful response to my recent article at The American Scene arguing against a carbon tax. Grist has graciously invited me to reply.
As I understand it, Ryan had three basic criticisms of my logic:
- the impacts of global warming will be more messy, unpredictable, and heartbreaking than I let on,
- I don't understand the economic trade-offs that make a carbon tax an elegant solution to the problem, and
- the technology-focused approach to the problem I propose is insufficiently conservative.
I'll try to address each of these in turn, all with a spirit of open-minded inquiry.
The first objection highlights the fact that productive global warming debates almost always hinge crucially upon predictions of the future. Consider three generic types of predictions: deterministic ("If I let go of this pencil, it will fall"), probabilistic ("If I flip this coin, it has a 50% chance of coming up heads and a 50% chance of coming up tails"), and uncertain predictions, for which we can not specify a reliable distribution of probabilities ("There will be a military coup in Pakistan in 2008"). Economists will immediately recognize the distinction between probabilistic and uncertain forecasts as, in essence, Knight's classic distinction between risk and uncertainty.
Strictly speaking, all predictions are uncertain, but as a practical matter we treat different predictions differently based on the observed reliability of the relevant predictive rules used to generate them.
No serious person believes that even the physical science projections for climate sensitivity (i.e., how many degrees hotter the world will get if we increase atmospheric carbon dioxide concentration according to some emissions scenario), never mind our predictions for how fast the world economy will grow or the economic impact of various degrees of climate change, are deterministic. This is why climate modelers and integrated climate-economics modelers spend so much time developing probability distributions for various outcomes and combining possible outcomes via odds-weighting to develop expected outcomes. When we hear a modeling group say "the expected outcome is X," it doesn't mean they've assumed only the most likely scenario will occur; it does mean, however, they assume their distribution of probabilities is correct (not being idiots, of course they constantly work hard to try to test and improve this distribution of probabilities).
For the moment, let's assume that predictions of global warming outcomes are probabilistic. I go into all this in my posts and articles in much greater detail, but if we take Nordhaus's DICE modeling group at Yale as a benchmark, we can make the following observations about global warming:
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