Hewitt on Futarchy
Chris Masse taunted, “If he had balls, Robin Hanson would debate Paul Hewitt, instead” of Mencius Moldbug. Monday Paul posted a 7000+ word critique of futarchy. I commented, “Care to indicate the top three claims you’d most prefer I respond to?” Paul listed three, and Eric Crampton responded much as I would. But since Paul probably wants to hear it from me, here are those claims, and related (long) quotes from Paul’s post:
1. Whether it is possible for (very) long-term prediction markets to be accurate, at the time the decision is made (not 19 or 20 years hence, just before the outcome is determined).
Attempting to forecast total national welfare measure (GDP+) assuming the policy is enacted, which is based on 20 or more years’ of future statistics. In those intervening 20 years or so, many new policies will be enacted, and every one of them will be expected to improve the national welfare. What are the odds of such a prediction market being able to accurately (and consistently) predict the actual national welfare that will be determined over a 20 year period? I’m sure Robin will counter … both decision markets were subject to the same uncertainty about the national welfare measure. Of course they were. This only means that both markets must have been equally accurate prior to the policy decision being triggered. What are the odds? How could we prove their accuracy? We don’t have very many long-term prediction markets that can be tested. … David Pennock’s analysis looked at the calibration of long-term markets 30 days prior to settlement.
2. How you expect traders to be able to understand and analyse GDP+ forecasts and the effects of particular policies on this metric, to the extent necessary to be able to improve the accuracy of the market forecast. – Governments (and teams of economists) have a difficult enough time measuring GDP for the current period, let alone one 20 years from now.
Forecasting national welfare under futarchy is an incredibly complex problem. I don’t think it is even possible for speculators to make reasonably accurate forecasts of national welfare. They simply do not possess the knowledge or understanding, let alone a decision model, that would allow them to make accurate predictions. … Each trader’s prediction is an “accurate” estimate combined with an “error” factor. The assumption is that the errors cancel out, leaving only the accurate information reflected in the market price. I think this is likely to be true, but not in every case. Where the individual errors are large, relative to the known, accurate information, the predicting algorithm is likely to break down. … For example, if we were to run a decision market for a policy designed to combat global warming, the forecast would be wildly inaccurate. The participants simply do not have enough information to make a reasonable forecast.
3A. Why budget constraints and policy adoption ranking was not considered?
Under futarchy, as long as it is clearly shown that a proposed policy would improve national welfare compared with the status quo, the policy is to be adopted. You don’t have to be much smarter (if at all) than a chimp to understand that no nation would be financially able to implement every policy that met this standard for adoption. Simply put, there are budget constraints, now and in the future. All but the simplest of policies involve financial commitments in the future. Accordingly, policies adopted in the current period will have budget implications in future years, which will limit the ability to adopt future policies that may be proposed (and that should be adopted). Futarchy makes no mention of budget constraints.
OK, issues #1 and #2 seem to me to be based on two fallacies:
As estimation problems get harder, accuracy quickly falls to zero, after which we literally know nothing about them.
There is some absolute accuracy standard which a governance estimation mechanism must reach to “work.”
Yes, our estimates usually get less accurate further into the future; this happens with pretty much all forecasting mechanisms. And the academic field of fiance has shown in great detail that this also holds for speculative financial markets; accuracy never falls to zero, but does get worse further out.
There is, however, no absolute “good enough” accuracy standard required to make a forecasting mechanism useful for governance. A “good” mechanism need only be better than our status quo institutions, and a new mechanism worth investigating need only have a chance of reaching that standard.
Yes, of course it is damn hard to estimate the distant future consequences of policies. But this is what current institutions must nevertheless try to do. They do it badly, in part because participants in such institutions have disappointingly weak incentives to look that far ahead. But they do something, to our advantage. Our challenge is only to see if we can do better than this situation, via mechanisms that give stronger incentives, even if those are still disappointingly weak.
Yes, our direct comparisons between speculative market estimates and other mechanisms have so far been over relatively short time periods, but the fact that markets have done well there at least suggests we might try them for further futures. I just don’t understand how you can be so confident of failure as to think we shouldn’t even try.
Issue #3 seems to just be a confusion. Adopting policies that threaten financial solvency would threaten national welfare, and speculators have incentives to take that into account.
I really think Paul and other critics would do better to focus first on the simpler example of a fire-the-CEO market for publicly traded firms, where many of these issues can be more easily addressed.