In his latest book, Keynes’s biographer Lord Robert Skidelsky argues that you just can’t insure against some risks. In fact, some expectations shouldn’t be called risks at all. One of Keynes’s least appreciated contributions, also voiced by his contemporary Frank Knight, was the importance of uncertainty, events in the future that we can’t measure or predict because we don’t have enough information or computational capacity.
Markets depend on prices, and prices depend on information, rational behavior, and predictable distributions of random shocks. When those foundations break down, governments are the only institutions that have the ability to restore order, from central banks injecting liquidity during credit crunches to regulators preventing or monitoring new innovations (be they financial derivatives or oil rigs) with uncertain social costs.
One important example that I haven’t spent enough time talking about is…
Earthquakes and oil spills are rare, but at least we can estimate how many to expect. Fair enough, but what about climate change?
With record-breaking ocean temperatures this year, climate scientists expect an active hurricane season. [S]houldn’t the government do its part to prevent greenhouse gases from clouding insurers’ ability to predict weather disasters?
Now economists Simon Dietz, Geoffrey Heal, and Antony Miller have joined the club with a new paper showing how standard probability theory doesn’t account for uncertainty properly and how many economists therefore underestimate the need for “immediate, rapid cuts in greenhouse gas emissions.” They list 3 important reasons for uncertainty (a.k.a. 3 assumptions that standard economic theory ignores):
The first is futurity, in particular the uncertain future socio-economic trends that determine the path of emissions, as well as how numerous and well off we will be when the impacts of today’s emissions occur.
Second, there is the considerable complexity of the climate system, not to mention its linkages with ecosystems and the economy, which means that it is hard to know whether our models are a reasonable simplification.
Third, there is the fact that the system is non-linear. This greatly increases the significance of model misspecification.
Other economists’ predictions, when compared as a group, confirm these weaknesses:
First, notice that, irrespective of what model is applied, the distribution is wide, and skewed to have what we might loosely call a “fat tail” of low-probability, high-temperature outcomes. This means that any cost-benefit analysis of emissions cuts that abstracts from uncertainty by working solely with a best guess of the climate sensitivity is likely to be misleading.
Second, notice the obvious fact that the various models disagree on what the distribution looks like precisely and that the spread between some sample pairs of models is wide.
They go on to explain that seemingly “irrational” behavior is actually the correct response to uncertainty. But that’s a conversation for another day…