An interview with Tom Sargent

5 Jun, 2019 at 17:38 | Posted in Economics | 3 Comments

I especially love the way this Nobel laureate “explains” rational expectations:

There are so many people out there, and it is so difficult to know how each one of them thinks about the future. So let’s just assume they think the same. Problem solved.

And that absolute nonsense reasoning rendered this guy a ‘Nobel prize’ in economics!

The tiny little problem that there is no hard empirical evidence that verifies rational expectations models obviously doesn’t bother rational expectations überpriest Thomas Sargent. He goes on defending the epistemological status of the rational expectations hypothesis arguing that since it “focuses on outcomes and does not pretend to have behavioral content,” it has proved to be “a powerful tool for making precise statements.”

Yeah, “precise” and “rigorous” — but what good does that do when they are all wrong?

As Sargent says in the interview, the concept of rational expectations was first developed by John Muth in an Econometrica article in 1961 — Rational expectations and the theory of price movements  — and later — from the 1970s and onward — applied to macroeconomics. Muth framed his rational expectations hypothesis (REH) in terms of probability distributions:

Expectations of firms (or, more generally, the subjective probability distribution of outcomes) tend to be distributed, for the same information set, about the prediction of the theory (or the “objective” probability distributions of outcomes).

But Muth was also very open with the non-descriptive character of his concept:

The hypothesis of rational expectations] does not assert that the scratch work of entrepreneurs resembles the system of equations in any way; nor does it state that predictions of entrepreneurs are perfect or that their expectations are all the same.

To Muth, its main usefulness was its generality and ability to be applicable to all sorts of situations irrespective of the concrete and contingent circumstances at hand.

Muth’s concept was later picked up by New Classical Macroeconomics, where it soon became the dominant model-assumption and has continued to be a standard assumption made in many neoclassical (macro)economic models – most notably in the fields of (real) business cycles and finance (being a cornerstone of the “efficient market hypothesis”).

REH basically says that people on the average hold expectations that will be fulfilled. This makes the economist’s analysis enormously simplistic since it means that the model used by the economist is the same as the one people use to make decisions and forecasts of the future. paste

But in the real world, it is not possible to just assume — as does Sargent — that we know the exact probability distribution of future events. On the contrary. We can’t even assume that probability distributions are the right way to characterize, understand or explain acts and decisions made under uncertainty. When we simply do not know, when we have not got a clue, when genuine uncertainty prevails, REH simply is not  “reasonable.” In those circumstances, it is not a useful assumption, since, under those circumstances, the future is not like the past, and henceforth, we cannot use the same probability distribution – if it at all exists – to describe both the past and future.

If we want to have anything of interest to say on real economies, financial crisis, and the decisions and choices real people make, it is high time to replace the rational expectations hypothesis with more relevant and realistic assumptions concerning economic agents and their expectations. ‘Rational expectations’ — paraphrasing Nichols Kaldor — is a barren and irrelevant apparatus of thought to deal with the way real-world people make decisions and act. It sure is “thoroughly misleading and pretty useless” and also one of the major obstacles to developing economics as a science.

Any model assumption — such as ‘rational expectations’ — that doesn’t pass the real world Smell Test is just silly nonsense on stilts.

4703325Suppose someone sits down where you are sitting right now and announces to me that he is Napoleon Bonaparte. The last thing I want to do with him is to get involved in a technical discussion of cavalry tactics at the battle of Austerlitz. If I do that, I’m getting tacitly drawn into the game that he is Napoleon. Now, Bob Lucas and Tom Sargent like nothing better than to get drawn into technical discussions, because then you have tacitly gone along with their fundamental assumptions; your attention is attracted away from the basic weakness of the whole story. Since I find that fundamental framework ludicrous, I respond by treating it as ludicrous – that is, by laughing at it – so as not to fall into the trap of taking it seriously and passing on to matters of technique.

Robert Solow


  1. It does seem to me that genuine uncertainty prevails always and everywhere — it is the rule and constant, not an exception that sometimes applies to particular circumstances. The difficulty is that uncertainty forms a fuzzy boundary around that which we do at least suppose that we know. When we do know something with practical application, when we know enough to control the outcomes of some process where we apply what we know as a kind of technology, we still do not know everything, but what we do not know may form a residual of variation of well-behaved probability distribution.
    For economists, a naïve observer might think that the resources allocated variously 1.) to creating the conditions in which knowledge can be applied practically to controlling processes and 2.) coping with the inevitability of unanticipated events and outcomes would be of supreme interest. The institutions that allow people to coordinate around knowledge and information that may be widely and unevenly distributed would be especially interesting. In this context, one interpretation of “rational expectations” as a modeling assumption would be that it is equivalent to assuming that these putative institutions for combining and coordinating distributed knowledge — the details of their mechanisms unexamined — work perfectly, with the implication that every actor is induced to discard their idiosyncratic knowledge or opinion in favor of the “objective truth” generated by the coordinating institutions. So, for example, informationally efficient financial markets produce prices that all actors treat as “true” and objective facts, superior for decision-making to any idiosyncratic subjective perceptions.
    As an opening gambit for inquiry, it is not a completely crazy way to begin thinking, but it is crazy as a place to stop thinking, which appears to be what Sargent wants to do.
    All the deeply interesting questions in a social science of economics would seem to revolve around how people manage and coordinate around this fuzzy boundary between what they think they know (including what they imagine others know) and what they, in fact, do not know. Economist’s traditional pre-occupation with allocation of resources would seem subordinate to this problem of social management of knowledge and uncertainty, if not altogether trivial by way of comparison. Recognizing that what is commonly called “capital” are efforts to apply knowledge and contain uncertainty calls into question how meaningful are the ways economists typically try to imagine “capital” as a factor to be allocated, while abstracting away from “capital” as a means to apply knowledge to the practical control of processes and containment of or coping with irreducible uncertainty.

  2. Rational Expectations is really normative: if you are irrational the system is meant to crush you because that is the just and moral thing to do.
    Uncertainty can be hedged. You may say that a stock’s future price is uncertain. But you know it will either go up or down. You can take both a long a short position in the stock, using financial instruments to leverage your positions such that you win whether the stock goes up or down. There is uncertainty about the future stock price, but finance allows you to hedge away the uncertainty so you win in all cases. Big banks use this technique to generate risk-free returns. They may only make a few basis points guaranteed profit but when you are investing billions or trillions the returns are substantial in volume.
    “the fields of (real) business cycles and finance (being a cornerstone of the “efficient market hypothesis”).”
    Black in “Noise” says markets are efficient to within a factor of two. Oil could be $100/barrel or $25/barrel, due to noise. That is 300% inflation due to noise, in an “efficient” market. What implications does that have for public inflation policies? Black was a finance guy whose innovations still appear in applied finance publications today. (I recently saw citations of Black-Litterman portfolio balancing in a big bank research document; you don’t really see references to IS-LM curves in those publications by applied finance practitioners.)

  3. The awarding of the Nobel Prize shortly after the GFC to Sargent, then shortly followed by Fama, was the biggest FU the economics profession made of all.

Sorry, the comment form is closed at this time.

Blog at
Entries and comments feeds.