The Corner

Discount Rates and Academic Mau-Mauing

Will Wilkinson ably takes on the idea that economists have a whole lot to say about what discount rates should be used in comparing costs and benefits in the debate over global warming. I have a more aggressive take: I dispute that we should care about discount rates, per se, at all.

Let me start with a stylized example to illustrate why. Suppose you were presented with two alternative policies to deal with global warming, A & B. A is roughly speaking, do nothing, and B is roughly speaking, a stiff global carbon tax. Let’s further assume that scientists, agronomists and so forth have constructed a good estimate for the GDP of the planet for each year 2010, 2011, and so on out to, say, 2260 (i.e., 250 years from now). That is, you have for policy A a list of 250 numbers, each of which is the projected global GDP for each year; and you have an analogous list for policy B. Let’s further assume that global GDP over the next 250 years is all you care about, and that these are the only two options. You are emperor of the world. How would you decide which policy to pursue?

One way to do this would be to create a “discounting function,” of greater or lower complexity, that can be applied to any list of GDP estimates by year to generate one number, the present value of this list. You could apply this discounting function to the list of projected GDPs for options A and B, and then choose the policy that has the higher present value. Alternatively, you could simply look at the two lists of GDP projections for policy A and policy B side-by-side, and choose the one that you think is better. In the example given, I would always employ the second method.

Why should I believe that there is any discounting function relevant to global warming that exists in closed form? I have a set of preferences for comparing current to future scenarios of projected costs vs. benefits. Much of the knowledge that informs this set of preferences is tacit and/or contingent on elements of the scenarios that I didn’t list comprehensively in advance, but react to as I am presented with specific scenarios. This is of particular practical importance in a case like global warming which operates across a scope — centuries of time across the globe — in which many of the embedded assumptions that I use when making discounting assumptions in day-to-day life will likely be violated. If an economist can’t find a function that encompasses these, I’m not necessarily irrational; the economist just can’t model my beliefs in a way that he finds convenient. The discounting function is merely a heuristic, not some straightjacket that I have agreed to be bound by just because I haven’t disputed its assumptions.

The primary practical use for these discounting functions in global-warming analysis is to have a function that allows integrated environment-economics models to search a wide space of possible policies automatically with a commonly applied set of explicit assumptions without requiring human intervention to consider each model run. When it comes time to look at the policies that the model says are best, I would always want to see the actual data by time period for a variety of “good” scenarios to determine what policy I think makes sense.

Think of these models as being like a Google search. With infinite time and patience, I could review every document on the Web, but I have only finite time. The Google PageRank algorithm imperfectly, but usefully, narrows down the number of documents I need to review; usually, however, I don’t use the “I Feel Lucky” button, and certainly would not in a case where my life depended on it.

Jim Manzi is CEO of Applied Predictive Technologies (APT), an applied artificial intelligence software company.


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