Have a nice summer!

Had some posts I needed to get out on the Norwegian-language blog of a colleague regarding housing prices in Norway. As a result I never got around to finishing up my thoughts on Gigerenzer´s criticism of behavioral economics (I really thought the paper was a good, enjoyable and interesting read, but my comments on this blog have so far been on things I didn´t like so much... use the search bar underneath the twitter box on the right to find the posts), nor some things I kind of want to think through regarding popularized economics, nor some ideas I would like to explore regarding the strong demand for assurance and its implications in politics and debate and academia.

Hope some of you readers will still come by once in a while next season.


Have a nice summer!

Should parenting and drugs affect economic theory?

Would economic theory be different if economists were parents when initially taught it? Freakonomics-blogger Justin Wolfer says yes, because that´s what the experience of becoming a father has told him. Overcoming Bias-blogger Robin Hanson says no, and says becoming a father is like having a mystical experience on drugs: It doesn´t inform us about the real structure of the world.

I´m wondering if the difference between these two may reduce to one thing: How “religiously” they´ve believed in the “ultimate truth” of the economic model of rational decision making. If we take Wolfer at his own word, he always saw it as

the basic idea informing economics—that people are purposeful, analytic decision makers. And this idea just seemed entirely natural to me. I had always believed in the analytic self; I was rational, calculating, and tried to make smart decisions. Of course real people don’t use math, but I figured that we’re still weighing costs and benefits just as our models say. Or at least that was my understanding of the world.

In other words, he sounds like the kind of guy who believed all behavior could be explained by economic theory as optimal, even if some of it would require complex choice models that assume people take subtle feedback effects, strategic “he knows that I know that he knows that I know X” issues and complicated delayed consequences of present actions into account in an optimal “rational” manner. After having a kid, this no longer seems to describe his own experience of himself:

My feelings toward my daughter Matilda aren’t easily expressed in analytic terms. I struggle to express it, just as I struggle to understand it.

…

There’s something new and strange about all this. Today, I feel the powerful force of biology. It’s visceral; it’s real; it’s hormonal, and it’s not in our economic models. I’m helpless in the face of feelings that overwhelm me. Yes, I know that a twenty-something reader will cleverly point out that I just need to count kids as a good which yields utility, or perhaps we need to add a state variable to the utility function as in rational addiction models. But that’s not the point. I’m surprised by how little of this I’ve consciously chosen. While the economic framework accurately describes how I choose an apple over an orange, it has had surprisingly little to say about what has been the most important choice in my life.

Hanson, in contrast, seems to see economic models as attempts to capture some of the regularities in human behavior:

First, econ makes sense of a complex social world by leaving important things out, on purpose – that is the point of models, to be simple enough to understand. More important, econ models almost never say anything about consciousness or emotional mood – they don’t at all assume people choose via a cold calculating mindset, or even that they choose consciously. As long as choices (approximately) fit certain consistency axioms, then some utility function captures them. So how could discovering emotional and unconscious choices possibly challenge such models.

Given Hanson´s view of economic theory, there is no need to redefine everything after having a kid. People will still tend to buy less as the price rises, avoid risk, and so on. It surprises me somewhat, though, that Hanson doesn´t see that there are a number of economists with a more fundamentalist belief in the neoclassical model. I´ve met several, and I bet I´ve met fewer economists in general than Hanson. I´ll admit this is pure speculation, but I´ve wondered if some economists feel threatened by behavior that deviates from the “rational choice” model they hold. They don´t say "Well, this is a simplified model, sure there´ll be deviations, but we`re capturing some regularities and that´s what we´re aiming for. Explaining something is better than not explaining anything and we´ll never be able to explain everything.” Instead, they try to twist their brains into coming up with ad-hoc assumptions that would reveal these deviations to be full, sophisticated optimization. At times, this means that increasingly stupid and shortsighted behavior is explained as increasingly subtle and complex optimization. Maybe it´s a fear of letting non-rational explanations get a foot in the door, maybe it´s because the "welfare effects" often tacked on at the end of choice models would no longer be "valid" (not that they are valid today, but if you truly believe all choices always maximize the ultimate good of importance to the acting agents, then I guess they might seem valid to you).

Hanson concludes that

Having an emotional parenting experience is as irrelevant to the value of neoclassical econ as having a mystical drug experience is to the validity of basic physics. Your subconscious might claim otherwise, but really, you don’t have to believe it.

 I´m not sure. If a person sees economic theory as Hanson describes it, then I agree with him. But if a person thinks his way of seeing the world is the only one that is valid and possible (in the sense of consistent with past experiences), then having a child or a high dose of psilocybin in a controlled setting may both be ways of learning otherwise?

Tim Harford´s "Adapt" - a book review

Tim Harford´s new book “Adapt” is a wonderful read but difficult to pigeonhole. There´s interesting stuff about the Iraq war, the finance crisis, development aid, randomized experiments, skunk works, the design of safety systems, whistleblowers, overconfidence and groupthink phenomena, not to mention a truly wonderful explanation of how a carbon tax would work and why environmentalists should embrace it. Even when he covers topics that have been ably covered by others elsewhere, he does so in a light and enjoyable way and manages to dig up new, cool anecdotes. It´s partly a popularization of science, partly a business book, at times it almost moves into self-help territory, and at times it seems to present new and interesting perspectives on big topics (such as financial regulation). Still - though it may sound sprawling, I didn´t really find it so when reading it. At one level it reads like a series of interesting pieces of journalism on different topics, but on another, there´s an underlying thread of ideas that gradually emerges.

The way I read it, the main point of the book is that the problems we face are too complex for us to understand and figure out the solutions to from behind a desk. Evidence from the failed predictions of experts to the extinction records of firms and the failure of high-level military strategies support this. There´s a number of reasons why this is so, ranging from the difficulty of capturing and aggregating information at a sufficiently finely grained level to psychological tendencies to trust in our (frequently false) beliefs and suppress possible evidence that they´re wrong. Still - we do solve problems - but this happens through an evolutionary process: We make lots of bets - each one of which is small enough that failure is acceptable - and the winning bets identify “good enough for now” solutions that we replicate and grow. The best examples of this (as a method for human problem solving) are market economies and science. Lots of entrepreneurs who hope to strike it big, some of whom combine the factors of production in a way that better creates value than others - thus making a profit (to put the point in an Austrian way). Lots of scientists stating hypotheses, some of whom are able to better predict the outcomes of experimental and quasi-experimental data than others - thus having their hypotheses strengthened (on a related note - I recently made the argument together with a colleague that this process is broken in economics - see more on that here).

Harford also discusses a host of implications that follow from this - the need to “decouple” systems so that failure in a single component (such as a bank in the financial system) doesn´t bring down the entire system, the need to finance both “highly certain” research ideas as well as “long shot” ideas, avoiding groupthink by including people likely to disagree (thus creating room for disagreement in the group) and demanding disagreement, and using prizes to elicit experiments. He also discusses how such evolutionary processes can be exploited better in policy- which is where he gets to his beautiful explanation of how a carbon tax works by tilting the playing field (there are two chapters here that should be reworked into a pamphlet and handed out in schools and parliaments).

That´s my brief take on the underlying “storyline” - but it doesn´t do justice to the book, which reads like a string of intellectual firecrackers. The wide-ranging topics, however, also means that they are necessarily touched on lightly - it´s an appetizer for a lot of ideas more than a fully satisfying meal. For instance, if success in the market (and elsewhere) consists of being the “lucky” winner who made a bet that - ahead of time - had no stronger claim to being right than others, how does this factor into our views on entitlements and redistributive taxation? If prizes (such as the prize for a space-going flight) actually elicit large-scale, expensive experiments that we only need to pay for when they succeed - does this mean that they exploit some irrational overconfidence in the competitors? If people were sensible and unbiased in their estimate of success, would they spend more than their expected reward? And if not - wouldn´t that mean the prize money would have to be sufficient to finance all the experiments - in which case it doesn`t save us any money? To what extent does the desire for control play into the desire for top down planning and control? (Imagine you were the prime minister - would you feel comfortable if loads of schools were allowed to try out whatever they felt like, risking the chance that some of them would beat kids or indoctrinate them in some way that blew up in the media?) In an online interview by Cory Doctorow, Harford states that

I also looked at the banking crisis and big industrial accidents such as Deepwater Horizon, and found that there were almost always people who could have blown the whistle — and sometimes did — but the message didn’t get through. So those communication lines need to be opened up and kept open.

Yes - but no…. After all, if there´s a host of signals coming up, most of them wrong, it might well be rational to have some filtering mechanism in place that also weeds out many of the correct signals in order to avoid being swamped and misguided by wrong ones.

While we´re on the topic of whistleblowers - I also wish he´d said a word or two about some of the biggest transparency cases of recent years. On the one hand, the whistleblower-friendly candidate Obama who changed his tune once he got in office. This could have served as a way of discussing how hard it is to actually have people looking over your shoulder and criticizing you, even when you think (or at least see the arguments for) allowing them to do so. Also, I would have been interested in Tim Harford´s views on Wikileaks, which in some ways is the biggest attempt to increase transparency in modern times - as well as his views on the conflicts it generated (a book championing the cause of whistle-blowers should also at least mention the awful treatment of claimed whistleblower Bradley Manning). Given the many stories from the Iraq war and the US military about the dangers of a strictly enforced official partyline/strategy/story, the potential value in Wikileaks shining a light on what is actually going on seems pretty clear. Or at least worthy of discussion.

Given the number of topics covered in the book there are obviously quibbles you may have with certain facts that are wrong or the way some of them are treated, but that´s to be expected. More importantly, there were parts of the argument that I felt were missing - especially concerning how difficult it is to learn from experience. As documented in for instance Robyn Dawes´ excellent “House of cards” (in the context of psychology and the misguided beliefs of treatment professionals), there are clear cases where statistical decision rules consistently outperform human judgments, without this being enough to convince the experts who could gain from them. Or consider this post on the backfire effect from the you are not so smart blog, which discusses experiments suggesting that people can react to evidence that they were wrong by being even more convinced in their wrongness. The way politicians respond to arguments about the surprisingly weak effect of drug decriminalization on usage levels is another example. In terms of Harford´s argument - adaption and evolution not only requires us to test things and find out what works - it also requires us to accept what works and implement it more broadly. Taking into account the number of things covered, he probably covered this too - but if he wants his ideas to be taken up in policy circles I think (that is, my gut-feeling is) that this would be perhaps the hardest part.

Finally, the book could also have been tempered by applying its thesis to the thesis itself: Has “planned evolution” been attempted, and did it actually work? As the book argues, the devil is often in the details and seemingly good ideas based on solid case stories may turn out to work quite differently in practice from what we expected.

Economics, math and science - Krugman 1996 vs. Krugman 2008

Recently came across a slate essay by Krugman from 1996 where he basically asserts that economics is a hard-core sciencey discipline because it uses mathematical models, and that those who dislike modern economics do so because they would prefer literary criticism style blah-blah. He starts his piece by discussing criticism of him (which I haven´t read) from Bob Kuttner. Krugman states that the disagreement has nothing to do with politics:

We are both, after all, liberals.  (...) What we are really fighting about is a matter of epistemology, of how one perceives and understands the world.
(...)
A strong desire to make economics less like a science and more like literary criticism is a surprisingly common attribute of anti-academic writers on the subject.
(...)
More than 40 years ago, the scientist-turned-novelist C.P. Snow wrote his famous essay about the war between the "two cultures," between the essentially literary sensibility that we expect of a card-carrying intellectual and the scientific/mathematical outlook that is arguably the true glory of our civilization. That war goes on; and economics is on the front line. Or to be more precise, it is territory that the literati definitively lost to the nerds only about 30 years ago--and
they want it back. That is what explains the lit-crit style so oddly favored by the leftist
critics of mainstream economics. Kuttner and Galbraith know that the quantitative, algebraic reasoning that lies behind modern economics is very difficult to challenge on its own ground. To oppose it they must invoke alternative standards of intellectual authority and legitimacy.
In effect, they are saying, "You have Paul Samuelson on your team? Well, we've got Jacques Derrida on ours."
(...)
The literati truly cannot be satisfied unless they get economics back from the nerds. But they can't have it, because we nerds have the better claim.

I find this interesting for two reasons. For one thing, economics is about the real world, yet Krugman doesn`t mention empirical evidence with a single word. Based on this piece, the discussion seems to be a theological debate between Pythagorean mystics who believe in the revelatory power of math and the medieval scholastics who want to focus on conceptual distinctions and dialectical reasoning. With both of them seeing themselves as the more scientific.
The second thing is that this belief in divine revelation through algebra is exactly what Krugman later attacked when he had had enough of the absurdities of highly regarded, peer-reviewed work in top journals spouting poorly justified empirical claims. After the financial crisis, he wrote,

the fault lines in the economics profession have yawned wider than ever. Lucas says the Obama administration’s stimulus plans are “schlock economics,” and his Chicago colleague John Cochrane says they’re based on discredited “fairy tales.” In response, Brad DeLong of the University of California, Berkeley, writes of the “intellectual collapse” of the Chicago School, and I myself have written that comments from Chicago economists are the product of a Dark Age of macroeconomics in which hard-won knowledge has been forgotten. What happened to the economics profession? And where does it go from here?

As I see it, the economics profession went astray because economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth.
(...)
the central cause of the profession’s failure was the desire for an all-encompassing, intellectually elegant approach that also gave economists a chance to show off their mathematical prowess.
(...)
what’s almost certain is that economists will have to learn to live with messiness. That is, they will have to acknowledge the importance of irrational and often unpredictable behavior, face up to the often idiosyncratic imperfections of markets and accept that an elegant economic “theory of everything” is a long way off. In practical terms, this will translate into more cautious policy advice — and a reduced willingness to dismantle economic safeguards in the faith that markets
will solve all problems.

My point with this is not that it`s wrong to use mathematics (Krugman made sure to clarify this as well). My point is that it`s wrong to think that you can reason your way to empirical truth without getting involved with the messy reality around us. Claims about reality need evidence from reality. The claims about reality that you start out with could derive from a formal model or a verbal argument or even a diagram - but to analyze empirical evidence requires quantification of phenomena and statistics, so this is not an argument against either numbers, mathematical methods or hard-to-understand algebra. It`s an argument against theology in science and the belief that you can dispense with empirical evidence provided you`ve thought "logically" enough from a priori "truths" using some method or other - whether based on mathematics, literary criticism-style discussion, or symbology.

Bitcoin - the newest e-money, internet threat and speculative bubble - all in one?

The idea of untraceable, privately controlled electronic currency is not new. Remember Kevin Kelley made a big deal about it in "Out of control" (chapter 12), his breathlessly enthusiastic book on the accelerating digital age published back in 1995:

The nature of e-money -- invisible, lightning quick, cheap, globally
penetrating -- is likely to produce indelible underground economies, a
worry way beyond mere laundering of drug money. In the net-world, where
a global economy is rooted in distributed knowledge and decentralized
control, e-money is not an option but a necessity. Para-currencies will
flourish as the network culture flourishes. An electronic matrix is
destined to be an outback of hardy underwire economies. The Net is so
amicable to electronic cash that once established interstitially in the
Net's links, e-money is probably ineradicable.

Kelley didn`t discuss Bitcoin, as even a futurist would be hard pressed to discuss by name something that would be developed 13 years later. But it`s the same thing: Untraceable, outside government control, loved by libertarians and kind of geeky. A good description is here, a recent "oh-my-god-they-sell-drugs-with-this" article from wired here, and a very bullish "this-is-where-i´m-gonna-place-all-my-savings" post on the appreciation trend of the bitcoin here.
Off the cuff, my guess would be that a simple, safe on-line currency that was as easy to use as cash would be quite useful. If you`re buying some one-off good or service online, buying a piece of software directly from the vendor, want to leave something in a blogger´s tip jar, etc. - rather than using a number of different services (visa, paypal, google checkout, tipjar etc) a simple e-cash would be nice. Based on my very cursory look, bitcoin is not quite there. Most importantly, it seems like a chore to get money into and out of bitcoins (partly because paypal, mastercard and visa don`t want to help). If they fix this problem, there would also seem to be a user interface issue: They need to make this integrated into browsers or some ubiquitous tool (facebook? google account?) so that it truly became as easy as pulling a bill out of your pocket. As it stands, my guess would be that it might keep appreciating for a while as gold-standard devotees and Ayn Rand fans discover it, there may be a slight influx of blackmarket funds, and the resulting appreciation may attract people who see it as an investment vehicle. Unless "currency exchange" becomes easier (so you can get the money into and out of the real world) and usability improves, I don´t quite see why this would become big. And if you can´t get your money out without a lot of bother (it might even get worse if governments see the money laundering issue as a problem) - then the investment aspect of it is going to suffer as well.

The “flaw” in modern economics – and how to fix it?

Why do economists produce such sophisticated, intelligent work and yet end up supporting claims about the real world that seem – at times – insane, absurd and clearly unsupported by evidence? (We realize you might disagree that this is ever a problem, but (as the quotes below will show) we are not alone in making this observation.)

A colleague and I have tried to understand why this happens in a recently published paper. An essay presenting the same ideas in a shorter, simpler, and more readable form is here, and for those who prefer to get “the gist of it” through a video, you can do so here. An even shorter version follows in this blogpost… ;-)

The puzzle that we try to explain is this frequent disconnect between high-quality, sophisticated work in some dimensions, and almost incompetently argued claims about the real world on the other. DeLong recently blogged about this as the “Walrasian” mindset (as opposed to the more pragmatic and empirically oriented Marshallian) he feels characterizes some macroeconomists:

The microfoundation-based theoretical framework is not to be tested, but simply applied. It is not an "engine for the discovery of concrete truth" but rather a body of truth itself. Once a Walrasian has pointed out some not-wholly-implausible microfoundation-based mechanisms, his work here is done.

The implied claim is that some economists are seduced-by-theoretical-beauty and talk about the real world even though their gaze is fixed almost exclusively on the Platonic ideal of their equations and models. This is similar to Olivier Blanchard`s recent statement that

Before the crisis, we had converged on a beautiful construction" to explain how markets could protect themselves from harm […] But beauty is not synonymous with truth.

This, again, was similar to Krugman’s claim in the 2009  essay “How did economists get it so wrong?”:

As I see it, the economics profession went astray because economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth.

I`d also note the recent reflections of blogger noahpinion on his graduate economics courses, where

the course [… in macroeconomics] didn't discuss how we knew if these theories were right or wrong. We did learn Bob Hall's test of the PIH. That was good. But when it came to all the other theories, empirics were only briefly mentioned, if at all, and never explained in detail. When we learned RBC, we were told that the measure of its success in explaining the data was - get this - that if you tweaked the parameters just right, you could get the theory to produce economic fluctuations of about the same size as the ones we see in real life. When I heard this, I thought "You have got to be kidding me!" Actually, what I thought was a bit more...um...colorful.

and (in part 2)

all of the mathematical formalism and kludgy numerical solutions of DSGE give you basically zero forecasting ability (and, in almost all cases, no better than an SVAR). All you get from using DSGE, it seems, is the opportunity to puff up your chest and say "Well, MY model is fully microfounded, and contains only 'deep structural' parameters like tastes and technology!"...Well, that, and a shot at publication in a top journal.

Though these observations seem related, they still don`t explain how this happens and why – and that makes it hard to find a good way to fix things.

Our explanation can be put in terms of the research process as an “evolutionary” process: Hunches and ideas are turned into models and arguments and papers, and these are “attacked” by colleagues who read drafts, attend seminars, perform anonymous peer-reviews or respond to published articles. Those claims that survive this process are seen as “solid” and “backed by research.” If the “challenges” facing some types of claims are systematically weaker than those facing other types of claims, the consequence would be exactly what we see: Some types of “accepted” claims would be of high standard (e.g., formal, theoretical models and certain types of statistical fitting) while other types of “accepted claims” would be of systematically lower quality (e.g., claims about how the real world actually works or what policies people would actually be better off under).

In our paper, we pursue this line of thought by identifying four types of claims that are commonly made – but that require very different types of evidence (just as the Pythagorean theorem and a claim about the permeability of shale rock would be supported in very different ways). We then apply this to the literature on rational addiction and argue that this literature has extended theory and that, to some extent, it is “as if” the market data was generated by these models. However, we also argue that there is (as good as) no evidence that these models capture the actual mechanism underlying an addiction or that they are credible, valid tools for predicting consumer welfare under addictions.  All the same – these claims have been made too – and we argue that such claims are allowed to piggy-back on the former claims provided these have been validly supported. We then discuss a survey mailed to all published rational addiction researchers which provides indicative support – or at least is consistent with – the claim that the “culture” of economics knows the relevant criteria for evaluating claims of pure theory and statistical fit better than it knows the relevant criteria for evaluating claims of causal or welfare “insight”. To see this, just compare the Bradford-Hill criteria for establishing causality in medicine/epidemiology with the evidence presented in modern macro or rational addiction theory or a game-theoretic model of the climate treaty negotiation process.

If this explanation holds up after further challenges and research and refinement, it would also provide a way of changing things – simply by demanding that researchers state claims more explicitly and with greater precision, and that we start discussing different claims separately and using the evidence relevant to each specific one. Unsupported claims about the real world should not be something you`re allowed to tag on at the end of a work as a treat for competently having done something quite unrelated.

Anyway, this is also an experiment in spreading research – and in addition to this blogpost you can pick from three different levels of interest: The full paper, the essay or the video.

Comments welcome :-)

A blind spot in economics? Unjustified claims about reality

Bob Lucas – believe the vision, belie the evidence

Noahpinion has a nice “Marshallian” take on the recent talk by Robert “Rational-Expectations” Lucas, the Godfather of modern macro. He shows easily available empirical evidence that strikingly goes against each of the three main assertions Lucas made about the US macroeconomic woes.

In this recent lecture at the University of Washington, Lucas makes the following assertions:

1. The persistent gap in income levels among rich economies is due to the costs of European welfare states.

2. The length of the Great Depression was due in part to the emergence of strong unions.

3. The reason for our current ongoing weakness in employment and business investment is the recent expansion of the U.S. welfare/regulatory state.

All three of these assertions are baldly contradicted by history.

Head over to Noahpinion to read the smack-down (well worth reading). What I`d like to do here is just to add a relevant and telling anecdote from Lucas`s professional memoir that I came across in one of the comments on DeLong:

"'Crossing over' was a term introduced to us to describe a discrepancy between Mendelian theory and certain observations. No doubt there is some underlying biology behind it, but for us it was presented as just a fudge-factor, a label for our ignorance. I was entranced with Mendel’s clean logic, and did not want to see it cluttered up with seemingly arbitrary fudge-factors. “Crossing over is b—s—,” I told Mike.

In fact, though, there was a big discrepancy between the Mendelian prediction without crossing over and the proportions we observed in our classroom data, too big to pass over without comment.

My report included a long section on experimental error.... Mike...replaced my experimental error section with a discussion of crossing over. His report came back with an A. Mine got a C-, with the instructor’s comment: “This is a good report, but you forgot about crossing-over.”

I don’t think there is anyone who knows me or my work as a mature scientist who would not recognize me in this story. The construction of theoretical models is our way to bring order to the way we think about the world, but the process necessarily involves ignoring some evidence or alternative theories—setting them aside. That can be hard to do—facts are facts—and sometimes my unconscious mind carries out the abstraction for me: I simply fail to see some of the data or some alternative theory. This failing can be costly and embarrassing to me, but I don’t think it has any effect on the advance of knowledge. Others will see the blind spot, as Mike did with crossing-over, keep what is good and correct what is not."

From Robert Lucas, Professional Memoir, pp. 4-5

This may also be an appropriate time to call attention to the classic old Solow quote about Lucas that you can find here.

Friedman`s schizophrenic legacy in economic methodology

Brad DeLong had an unexpected take on Friedman`s methodological legacy in economics, highlighting his desire to stay close to data when theorizing rather than his defense of "as-if" theorizing. In DeLong`s words, Friedman was a (pragmatic) Marshallian rather than a (purist) Walrasian:

are the theoretical mechanisms we are studying things that we can see? Are their predictions consistent with the gross features of reality? Supply curves slope up: if we say that demand has changed and pushed us along a supply curve, is it in fact the case that both quantities and prices have risen (or fallen)? Demand curves slope down: if we say that supply has changed and pushed us along a demand curve, is it in fact the case that quantities have risen and prices have fallen (or fallen and risen)?

If the first-order predictions of our theories are not visible in the first-order movements of the data--quantities, prices, asset values, and expectations--then, Friedman (and Marshall) would say, our theory is broken and we need to fix it.

I`ve often been puzzled by examples Friedman`s  pragmatic, close-to-the-data, uncover-the-actual-mechanisms approach and its mismatch with the message economists took away from his essay on methodology. In a footnote in Hausman's book on "the inexact and separate science of economics" he mentions that Lee Hansen

recalls economists in the 1950s reacting to Friedman`s essay with a sense of liberation. They could now get on with the job of exploring and applying their models without bothering with objections to the realism of their assumptions.

More recently, Nathan Berg and Gerd Gigerenzer wrote a paper where they set up the "as if" methodology associated with Friedman as the great big flaw of behavioral as well as neoclassical economics:

For a research program that counts improved empirical realism among its primary goals, it is startling that behavioral economics appears, in many cases, indistinguishable
from neoclassical economics in its reliance on as-if arguments to justify ―psychological models that make no pretense of even attempting to describe the psychological processes that underlie human decision making.

This image of Friedman as the staunchest defend of absurdly speculative rational choice fiction always seemed at odds with other stories about the man`s research. As I understand it, he pored through meeting minutes from the Fed together with Anna Schwartz to understand why the Fed did what it did during the Great Depression, and he was sceptical of data-fitting and overly complex theoretical models. Also, when the Economic Journal had a 100 year anniversary issue (January 1991, vol 101 no 404) and asked a number of famous economists for their predictions about the "next 100 years" of our discipline, Friedman went back to the early issues to actually see what (if anything) had changed. As far as I remember, the other contributions I read were mainly
economists saying that in the future the discipline would finally move
towards what they themselves had been doing for a long time. Friedman concluded that the core subjects of the late 1800s would still be present, some new topics (e.g., property rights, crime, public choice) would probably be present, along with some new topics. The methods would be an updated but recognizable mix of pure theory, descriptive statistics and econometrics. And to conclude he quoted a conclusion Ashley had made after a similar exercise in 1907:

When one looks back on a century of economic teaching and writing, the chief lesson should, I feel, be one of caution and modesty, and especially when we approach the burning issues of our own day. We economists...have been so often in the wrong!

What graduate school economics did and did not teach some random dude

I`ve got no idea who this guy is – found links to these posts from Tyler Cowen’s blog – but I found his reflection on his graduate economics education (see also part 2)  insightful and interesting.

Some highlights (that is to say – things that remind me of my own opinions ;-)

coming as I did from a physics background, I found several things that annoyed me about the course (besides the fact that I got a B). One was that, in spite of all the mathematical precision of these theories, very few of them offered any way to calculateany economic quantity. In physics, theories are tools for turning quantitative observations into quantitative predictions. In macroeconomics, there was plenty of math, but it seemed to be used primarily as a descriptive tool for explicating ideas about how the world might work. At the end of the course, I realized that if someone asked me to tell them what unemployment would be next month, I would have no idea how to answer them.

As Richard Feynman once said about a theory he didn't like: "I don’t like that they’re not calculating anything. I don’t like that they don’t check their ideas. I don’t like that for anything that disagrees with an experiment, they cook up an explanation - a fix-up to say, 'Well, it might be true.'"

That was the second problem I had with the course: it didn't discuss how we knew if these theories were right or wrong. We did learn Bob Hall's test of the PIH. That was good. But when it came to all the other theories, empirics were only briefly mentioned, if at all, and never explained in detail. When we learned RBC, we were told that the measure of its success in explaining the data was - get this - that if you tweaked the parameters just right, you could get the theory to produce economic fluctuations of about the same size as the ones we see in real life. When I heard this, I thought "You have got to be kidding me!" Actually, what I thought was a bit more...um...colorful.

(This absurdly un-scientific approach, which goes by the euphemistic name of "moment matching," gave me my bitter and enduring hatred of Real Business Cycle theory, about which Niklas Blanchard and others have teased me. I keep waiting for the ghost ofFrancis Bacon or Isaac Newton to appear and smite Ed Prescott for putting theory ahead of measurement. It hasn't happened.)

[…]

DeLong and Summers are right to point the finger at the economics field itself. Senior professors at economics departments around the country are the ones who give the nod to job candidates steeped in neoclassical models and DSGE math. The editors of Econometrica, the American Economic Review, the Quarterly Journal of Economics, and the other top journals are the ones who publish paper after paper on these subjects, who accept "moment matching" as a standard of empirical verification, who approve of pages upon pages of math that tells "stories" instead of making quantitative predictions, etc. And the Nobel Prize committee is responsible for giving a (pseudo-)Nobel Prize to Ed Prescott for the RBC model, another to Robert Lucas for the Rational Expectations Hypothesis, and another to Friedrich Hayek for being a cranky econ blogger before it was popular.

And from the follow-up blog-post which discusses the field-courses he chose (which, AFAIK are the courses he voluntarily chose):

The field course addressed some, but not all, of the complaints I had had about my first-year course. There was more focus on calculating observable quantities, and on making predictions about phenomena other than the ones that inspired a model's creation. That was very good.

But it was telling that even when the models made wrong predictions, this was not presented as a reason to reject the models (as it would be in, say, biology). This was how I realized that macroeconomics is a science in its extreme infancy. Basically, we don't have any macro models that really work, in the sense that models "work" in biology or meteorology. Often, therefore the measure of a good theory is whether itseems to point us in the direction of models that might work someday.

[…]

all of the mathematical formalism and kludgy numerical solutions of DSGE give you basically zero forecasting ability (and, in almost all cases, no better than an SVAR). All you get from using DSGE, it seems, is the opportunity to puff up your chest and say "Well, MY model is fully microfounded, and contains only 'deep structural' parameters like tastes and technology!"...Well, that, and a shot at publication in a top journal.

Finally, my field course taught me what a bad deal the whole neoclassical paradigm was. When people like Jordi Gali found that RBC models didn't square with the evidence, it did not give any discernible pause to the multitudes of researchers who assume that technology shocks cause recessions. The aforementioned paper by Basu, Fernald and Kimball uses RBC's own framework to show its internal contradictions - it jumps through all the hoops set up by Lucas and Prescott - but I don't exactly expect it to derail the neoclassical program any more than did Gali.

The source of our policy views – an honest opinion from Steven Levitt

Freakonomics-author Levitt recently posted on why he strongly opposed the US ban on internet poker, while weakly preferring drug prohibition (despite the good arguments against it) and legalized abortion.

I’ve never really understood why I personally come down on one side or the other with respect to a particular gray-area activity.  […]

It wasn’t until the U.S. government’s crackdown on internet poker last week that I came to realize that the primary determinant of where I stand with respect to government interference in activities comes down to the answer to a simple question: How would I feel if my daughter were engaged in that activity?

If the answer is that I wouldn’t want my daughter to do it, then I don’t mind the government passing a law against it. I wouldn’t want my daughter to be a cocaine addict or a prostitute, so in spite of the fact that it would probably be more economically efficient to legalize drugs and prostitution subject to heavy regulation/taxation, I don’t mind those activities being illegal.

Some express disappointment in Levitt for this comment:

What's missing in Levitt? The whole idea of tolerance. It's easy to tolerate people doing what you would do and approve of. It's harder to tolerate what you don't approve of. It's even harder to tolerate activities and behaviors that you find disgusting. Levitt has just confessed that he's intolerant or, at least, that he won't object to a government that's intolerant. That's disappointing. I had expected better of him.

Personally, I find this a misreading of his point. I don`t think he`s saying that he believes this is how it should be – just that this seems to be the way it is. If anything, the fact that he has tried to reflect on the source of his opinions and their possible basis in emotions makes me trust the guy more.

Seems to me that we often have  a strong feeling or “intuition” that something is good or bad, and that the smarter we are the better we`re able to convince ourselves that this is due to logical arguments. There`s a host of good stuff on the psychological mechanisms driving our attitudes towards sources of risk in Dan Gardner`s book “The science of fear.” There`s a host of good stuff on how easily we trick ourselves in Kurzban`s “Why everyone (else) is a hypocrite”. Who hasn`t been in a discussion with intelligent, informed people who dig themselves deeper and deeper into a hole while trying to defend some ridiculous opinion. (And who hasn`t at times been that very same person themselves?)

Note: I`m not making the argument that we can`t learn and modify our views when confronted by evidence. But I am making the claim that this is frequently difficult to do, and that someone able to reflect on their feelings and biases (as Levitt does here) seems more open to changing his views than somebody who ignorantly imagines him- or herself to be a rational, evidence-based and principled logic machine.

“As-if behavioral economics” – puzzle: How can an as-if theory be normative?

Although I enjoyed it, I’ve spent the last few days on this blog noting some issues where I disagree with the paper ”As-if behavioral economics”. Today I want to reflect on something they touch upon without fully resolving.

Some economists argue that their assumptions can`t be questioned because their models are “as-if” - they are merely tools that allow you to successfully predict market data, and the realism of the assumptions is irrelevant. If that is so - why are there so many norms and criteria apart from prediction that a “good” model should fulfill? And why - if they are mere “as-if” prediction-generating machines - are the neoclassical models held up as a normative ideal we should strive to aim for in our own decision making?

Berg and Gigerenzer touch on this puzzle in a couple of places. For one thing, two of the points they emphasize is that

• behavioral economics suffers from subscribing to the as-if method, which ignores the realism of the assumptions (similarity of model to the real-world mechanism/process), and that
• behavioral economics has grown to see behavioral “heuristics” as “biases” that violate the normatively correct neoclassical rules

Later, they also note that the

the normative interpretation of deviations as mistakes does not follow from an empirical investigation linking deviations to negative outcomes. The empirical investigation is limited to testing whether behavior conforms to a neoclassical normative ideal.

Consider - if the model is nothing but a black box that spits out impressive predictions:

• Why is it important that agents inside the model are optimizing and rational?
• Why is it important that the agents are well informed?
• Why is it important that preferences are “standard” (thus generating well behaved utility functions and nice indifference curves)?
• Why does it matter whether or not your prediction is based on an “equilibrium” inside the model?
• How can the utility and welfare effects of a model imply anything about real people`s welfare?

This is particularly odd since, as far as I can tell, rational optimizers can behave in all sorts of ways depending on their preferences and the choice problem they face. When assumptionsdon’t need to be supported by empirical evidence, this means that any observable behavior pattern can be modelled as rational behavior given some hypothetical choice problem. If you don't believe me, ask yourself whether you can describe any specific behavior pattern that could not be the result of rational choice. Note that this has to be a pattern, that is to say that it has to be stated in terms of observables without reference to “underlying” but non-observable preferences. You can refer to prices, consumption goods, patterns across time and between goods, etc., and using such categories I don`t think it is possible to find any “non-rationalizable consumption pattern” that would be accepted as that by most economists.

So what?

Well - if anything can be rationalized by such a theory, and assumptions can be as unrealistic as you want - then any stable pattern can be “explained” by such a “theory.” In actuality, though, you would just be describing the pattern using a different format (the “rational choice model” format). Which raises the question of why it is so important to use that format.

After all - if all you want to do is to predict, then it shouldn`t matter whether you assumed people to behave “as if” they were maximizers or not. Any model would be just as good if it predicted equally well.

Also - if the rational choice model is just a format - a way of describing behavior by identifying some “story” that would generate it - then why should it have normative power?

This is extra puzzling if you consider the old-school style Chicago-economics that sees all behavior as rational. If this is so, then there is no normative power beyond “do whatever you do cause that’s what’s optimal.” Taken at face value, this view of the world would also lead to apathy: There’s

no point in criticizing politicians or engaging with the world, because everyone knows what they’re doing and are doing what’s best for themselves. Politicians – that’s public choice. Regulators - they`ve been captured by special interests. Economists? Well - I guess their doings could be made endogenous as well.

I don’t have an answer to this puzzle - but I wonder if it may have something to do with politics. By both claiming that everyone is rational  and that this rationality represents the normative ideal for action, then a world of unfettered markets seems like a good idea: It would be a world of informed, self-interested people generating huge benefits to each other through their selfish doings. If so - then behavioral economics becomes the “interventionist” response: Yes - a neoclassical paradise would be great – however, unfortunately, we’re just evolved apes with lots of biases and flaws. With a little carefully designed policy, though, we can regulate and nudge people in the direction of the truly rational agent.

Does anyone know of a survey that would make it possible to correlate policy views and politics with economists`attitudes towards behavioral and old-school rational choice theory?

Strauss-Kahn and rational assault

Tyler Cowen generated a bit of discussion recently with his blog-comment on Dominique Strauss-Kahn

Dominique Strauss-Kahn has been arrested, taken off a plane to Paris, and accused of a shocking crime.  When I hear of this kind of story, I always wonder how the “true economist” should react.  After all, DSK had a very strong incentive not to commit the crime, including his desire to run for further office in France, not to mention his high IMF salary and strong network of international connections.  So much to lose.
Should the “real economist” conclude that DSK is less likely to be guilty than others will think? 

Let`s try to answer the question:
A  bad economist would think : Strauss-Kahn clearly has more to lose and thus less of an incentive to sexually assault – which makes it unlikely that he did. So he is probably innocent.
A better economist would go one step further: Strauss-Kahn realizes that we would think this way, which makes crime relatively risk-free for him. This makes it likely that he did perform the crime. So he is probably guilty.
The even better economist would go even further: Since we realize that Strauss-Kahn would realize this, and that he would want to exploit this mechanism, we can conclude that he is probably guilty.
The “real economist,” finally, would realize that this infinite loop would lead Strauss-Kahn to play his part in implementing a randomized, mixed-strategy equilibrium by throwing a dice to decide whether or not to run naked down hallways assaulting hotel staff. The economist would then write up the model, derive suitably generalized solutions for various assumptions of payoffs and attitudes towards risk, and publish it in a high ranking journal, using the Kahn-Strauss story as a motivating example in the introduction.

“As if behavioral economics” - flaw 3: Adding a parameter is not all behavioral economists have done

I´m writing through some issues raised by the paper As-if behavioral economics. I have one more annoyance I want to raise with the paper before I move on to some of its strong points.

The annoyance I want to note today is one that disappoints me. Berg and Gigerenzer write:

Behavioral models frequently add new parameters to a neoclassical model, which necessarily increases R-squared. Then this increased R-squared is used as empirical support for the behavioral models without subjecting them to out-of-sample prediction tests.

This is silly. Yes, adding a parameter does increase R-squared (the share of the variation in the data that your statistical model captures), but this way of phrasing it makes it sound as though any variable added to a statistical model would increase R-squared by the same amount. That´s not the case: A randomly picked variable that is irrelevant would (if we ignore time trends and that sort of data) on average have zero explanatory power. The standard test is to check the significance level of the variable. This answers the following question: If the variable actually has no explanatory power for the data - how likely is it that it would “by chance” seem to explain whatever it seems to explain in the current dataset? The normal significance level to test at is 5%, and if you use that significance level the “irrelevant” variable will seem relevant in your data only 5% of the time. I´m pretty sure Berg and Gigerenzer know this.

A related flaw shows up in their discussion of Fehr and Schmidt´s model of inequality aversion (which assumes that some people dislike inequality, especially inequality in their own disfavor). Berg and Gigerenzer write:

In addition, the content of the mathematical model is barely more than a circular explanation: When participants in the ultimatum game share equally or reject positive offers, this implies non-zero weights on the “social preferences” terms in the utility function, and the behavior is then attributed to “social preferences.”

This, too, is weak. What Fehr and Schmidt´s model assumes is that there is a specific structure to the inequity aversion: That your dislike of how much better (or worse) off someone else is than you is a linear function of how much better off than you they are. And, second, that it´s worse being behind someone than in front of someone, even if you´d prefer most of all that you were equal. It may be this model is "wrong," but it is more than circular and there is a variety of competing models that others have promoted as better ways of capturing typical patterns in experimental data on various economic games (off the top of my head, Charness and Rabin (2002), Bolton and Ockenfels (2000) and Engelmann and Strobel (2004)).

Having said that, it might well be that Fehr and Schmidt is a crude model that fails to capture and process the relevant data in the best way. However, it does so well enough to be useful and interesting. If you found a model that did better and that could also predict well for new experiments, as well as in different settings - using less information that could more credibly be related to actual pscyhological processes - then I´m pretty sure you would be published quickly in a good journal. That´s not to say that “you shouldn´t criticize unless you can do better,” but it is to say that the current model captures something interesting in a simple way - even if it is clearly imperfect. Clarifying its weaknesses is fair game - but Berg and Gigerenzer should do better than brushing it off as though its fit with data was no better than any random model thrown up.

"As if behavioral economics" - flaw 2: "Neglecting the process is always wrong"

I´m writing through some issues raised by the paper As-if behavioral economics. It´s a sprawling paper with some very good arguments and some… not so good ones. I´m hoping to get through both good and bad this week.

The paper opens with a reasonable goal - evaluating whether behavioral economics has achieved its (sometimes) stated goal of improved empirical realism:

Insofar as the goal of replacing these idealized assumptions with more realistic ones accurately summarizes the behavioral economics program, we can attempt to evaluate its success by assessing the extent to which empirical realism has been achieved.

This is an OK idea for a paper: Some tradition has aimed to achieve X, and we want to see how successful they´ve been in this. However, Berg and Gigerenzer also imply in much of the paper that this aim (empirical realism in the assumed decision making process) is always an important aim, and that any economic theory that fails in this regard is wrong. They call behavioral economics a “repair program” for the flaws of neoclassical “rational choice” economics, and have a long section on how “empirical realism” was sold, bought and re-sold (i.e. they had it in mainstream economics, lost it due to Pareto and his friends, started getting it back with behavioral economics, but then lost it as these strayed from the path):

perhaps after discovering that the easiest path toward broader acceptance into the mainstream was to put forward slightly modified neoclassical models based on constrained optimization, the behavioral economics program shed its ambition to empirically describe psychological process, adopting Friedman‘s as-if doctrine.

So why is this empirically accurate process description so important in Berg and Gigerenzer´s view? The reason seems to be that they give different implications for how we can aid and improve human choice. After an (interesting) explanation of how ball-players catch balls through a simple heuristic (“run so that the ball up in the air is at a constant angle to you”) rather than through “intuitive” application of Newtonian mechanics, they write:

Thus, process and as-if models make distinct predictions (e.g., running in a pattern that keeps the angle between the player and ball fixed versus running directly toward the ball and waiting for it under the spot where it will land; and being able to point to the landing spot) and lead to distinct policy implications about interventions, or designing new institutions, to aid and improve human performance.

This is a good and valid argument in its relevant context but it surely fails to apply to all types of economics. It seems particularly relevant as a criticism of welfare economics, which often involves nothing more substantial than the argument that “all choices are always welfare-maximizing, so any new choice option that is chosen improved welfare.” However, not all of economics is (or should be) dealing with this.

To my mind, at least part of what economics is about is the study of interactions: What happens when many people interact in a given institutional context (market, negotiation or whatever) and there are mechanisms (prices, norms, whatever) that introduce various positive and negative feedback effects? To study this you need a method, and one such method is to create a “toy world” where “toy people” act in a way that captures relevant behavioral regularities in real people. If people tend to buy less of a good when the prices rises, then you need a toy person who responds like this. If you think it may be important that people in some market want to buy the same thing as some other person or group (e.g. fashion), then you need a toy person who exhibits this response. However, you don´t need a psychologically realistic model of a person because all you want (in this context) is to see what the outcome of various interaction effects would be.

Sometimes (usually, I would guess), economists will do this in a closed, simple mathematical model with utility maximizing agents and profit maximizing firms. However, since utility maximizing agents can behave in almost any conceivable way (just change their preferences and introduce state variables as in Becker´s extended utility approach), this “rationality postulate” doesn´t really constrain the kinds of behavior you can study that much. You are likely more constrained by the expectations of other economists that the toy people and firms in the model should have “model consistent expectations” (i.e., they should expect the consequences of their actions that actually occur), and that it is important and interesting to study the subtle mechanisms that are created when these toy agents consequently marginally adjust their behavior for all sorts of reasons (Hotelling´s rule, the green paradox, smokers responding to expectations of future tax hikes by smoking less today, etc.).

Another way of doing this is agent based modelling, where you create small “ant people” in a computer program and let them interact based on simple rules. You do this again and again and see what “typically happens” and so on. This is related to evolutionary game theory where the shares of “agents” living by some simple strategy grows or shrinks depending on the average payoff it produces given the current mix of strategies in the population.

Anyway - though none of these ways of studying interaction are sufficient to credibly examine the social world around us, they don´t seem completely valueless. Granted - some (many?) economists do take the welfare of the toy people a bit too seriously as a proxy for real world consumer welfare, and some seem to think that tweaking a toy person to act like a real person means that the real person “is similar” pscyhologically to the toy person. But these are errors in interpretation and use, not in the method as such.

In short: If you want to show how simple behavioral patterns at the individual level could combine to create various higher-level patterns in groups and markets and other contexts, then what you want is the simplest, most tractable representation of those behavior patterns. Psychological realism is irrelevant - because your argument is “several people interacting in this specific way, each of whom exhibit this simple behavior patterns, would generate these and these aggregate patterns and would - in aggregate - respond in this and this way to various external shocks in the environment”.

"As if behavioral economics" - flaw 1: The “true tradition” argument

I recently read As-if behavioral economics, a paper critical of behavioral economics written by Nathan Berg and Gerd Gigerenzer. Last week I presented the underlying narrative that they seem to imply. This week I hope to have time to discuss some of the more substantial good and bad points of their paper.

However, before we move on to substance I have one annoyance that I want to get off my chest: What I call the “true tradition” argument. I´ve touched on this before - regarding the “Holy Scripture” view that some people seem to have of Smith´s Wealth of Nations, but this paper does it again and I find it silly and annoying.

The “structure” of the argument (if you can even call it an argument) is one of two:
* “Somebody I disagree with has fallen from the true and pure tradition”
* “I may seem to be an outsider, but I´m actually the true carrier of the true and pure tradition”

You see this in religion and alternative movements such as meditation or NLP- where people trace their guru or Kung-Fu teacher or whatever back to some original figure. “My teacher studied under X, who studied under Y, who studied under Z in a pure unbroken line back to (idolized figure or text)” or the long "X begat Y who begat Z who begat.." sections of the old Testament.

You also see this in quasi-scientific practices such as Freudian psychoanalysis. It´s probably even more pronounced in some parts of Austrian economics, where the discussion of what Hayek or Mises or Böhm-Bawerk or Menger “truly” meant seems to be a huge thing. Followers of Ayn Rand are the same or worse. You see it in people who make a big ado about how their claims are foreshadowed in Aristoteles or some ancient philosopher´s speculative musings as if that should somehow count as relevant evidence for an empirical claim.

Amongst people opposed to “standard economics” there seems to be a similar thing going on - to me, the family tree of the “other canon” project seems a clear example.

And in Berg and Gigerenzer´s paper, the “wrong turn” of economics is identified as the

fundamental shift in economics which took place from the beginning of the twentieth century: the ̳Paretian turn‘. This shift, initiated by Vilfredo Pareto and completed in the 1930s and 1940s by John Hicks, Roy Allen and Paul Samuelson, eliminated psychological concepts from economics by basing economic theory on principles of rational choice.

You could choose to ignore this kind of stuff - see it as narratives that help provide groups of people with a feeling of connection to a larger tradition and that places their work and struggles into a larger storyline of good and bad. But seriously… it´s just stupid.

More than stupid, I see this as a real problem in that it raises as a significant and important issue something which is irrelevant to the evaluation of scientific claims. Nobody has a hotline to truth! I don´t care how smart you are or how often you´ve been right before - even the smartest people in the world can be misguided and confused and incorrect. Their claims must be evaluated and confronted with evidence, and if they´re wrong they´re wrong and we move on.

Is behavioral economics a flawed band-aid on the neoclassical enterprise?

I finally got around to reading the paper “As-if behavioral economics: Neoclassical economics in disguise?” by Nathan Berg and Gerd Gigerenzer this past Easter holiday. I found it enjoyable, often insightful, and somewhat confused. It contained a lot of stuff, so I´ll split this into several parts.
Today I´ll merely go through the overall “story” they seem to be operating from. This isn´t the “storyline” of the paper, but more the story such as I can piece it back together from the pieces and clues they scatter throughout the paper.
Their story is that economics was a sensible science informed by psychological science until an italian economist called Pareto turned it into the current, neoclassical “monster” we have today.

a fundamental shift in economics which took place from the beginning of the twentieth century: the  ̳Paretian turn‘. This shift, initiated by Vilfredo Pareto and completed in the 1930s and 1940s by John Hicks, Roy Allen and Paul Samuelson, eliminated psychological concepts from economics by basing economic theory on principles of rational choice.

This new framework assumed that people´s stable preferences can be described by a mathematical utility function such that any good (provided in sufficient quantities) can fully compensate for a reduction in any other good.

If, for example, x represents a positive quantity of ice cream and y represents time spent with one‘s grandmother, then as soon as we write down the utility function U(x, y) and endow it with the standard assumptions that imply commensurability, the unavoidable implication is that there exists a quantity of ice cream that can compensate for the loss of nearly all time with one‘s grandmother.

In addition, this framework built up an axiomatic, logical theory of normative rationality centered around internal consistency. That is to say, they argued that people should have transitive preferences, conform to expected utility axioms and have Bayesian beliefs.
This was actually just an unsupported (and in Berg and Gigerenzer´s view, false) assumption, in that they never even attempted to establish that such rules would lead to better outcomes in the real world.

Expected utility violators and time-inconsistent decision makers earn more money in experiments (Berg, Johnson, Eckel, 2009).

Because this theory completely misspecified how people make choices and process beliefs, it became necessary to ignore the realism of the assumptions. For this reason, they turned to the “as-if” methodology that they saw Friedman as having preached: All models are only to be evaluated in terms of how well they predict - and the realism of the assumptions is irrelevant. They describe this as

the Friedman as-if doctrine in neoclassical economics focusing solely on outcomes.

This did not fully solve the underlying problem: Since people do not choose in this way, predictive ability was poor. Behavioral economists initially wanted to tackle the root of the problem by reintroducing realism (psychology) into the description of consumer behavior. After a while, though, they were instead reduced to adding bells and whistles of various kinds to patch up the existing formal framework so that it would better predict in an as-if sense.

Instead of asking how real people—both successful and unsuccessful—choose among gambles, the repair program focused on transformations of payoffs (which produced expected utility theory) and, later, transformations of probabilities (which produced prospect theory) to fit, rather than predict, data. The goal of the repair program appeared, in some ways, to be more statistical than intellectual: adding parameters and transformations to ensure that a weighting- and-adding objective function, used incorrectly as a model of mind, could fit observed choice data.

Their work, by introducing further complications into the choice models, actually made things worse - in that they made the resulting “theory” of human choice even less plausible.

Leading models in the rise of behavioral economics rely on Friedman‘s as-if doctrine by putting forward more unrealistic processes—that is, describing behavior as the process of solving a constrained optimization problem that is more complex—than the simpler neoclassical model they were meant to improve upon.

On the normative side, most behavioral “epicycles” that were introduced came to be seen as biases and flaws that needed nudging and paternalistic regulation.

To these writers (and many if not most others in behavioral economics), the neoclassical normative model is unquestioned, and empirical investigation consists primarily of documenting deviations from that normative model, which are automatically interpreted as pathological. In other words, the normative interpretation of deviations as mistakes does not follow from an empirical investigation linking deviations to negative outcomes. The empirical investigation is limited to testing whether behavior conforms to a neoclassical normative ideal.

Finally, perhaps in an effort to avoid revealing how poor both the neoclassical and behavioral models actually are, the bar for predictive success was lowered even further by turning it into an exercise in fitting models to existing data rather than an exercise in making successful out-of-sample predictions.

Behavioral models frequently add new parameters to a neoclassical model, which necessarily increases R-squared. Then this increased R-squared is used as empirical support for the behavioral models without subjecting them to out-of-sample prediction tests.

That´s the story as I read it, and the authors continue to describe their view of what they think should be done. But that will have to wait for another time.

How convinced should we be of an economic theory that is “consistent with empirical data”?

What follows is not rocket science, and probably not 100% correct, but: When we say that “empirical tests” support an economic theory, does this mean the theory is probably right? More specifically, what I want to explore is whether there is a simple way of stating the issue so that we don’t ignore the base-rate .

An example of how important the way we state this issue is, comes from medical decision making: There’s a number of screening programs in place to identify people with medical conditions that can be harmful, and research on medical decision-making shows that doctors seriously misinterpret positive results from such tests. Simply put, test results are “misleading” when a test with even a low error rate is used to search for a rare condition in the general population: The small error rate multiplied by the huge number of healthy people gives you the bear share of those flagged as “positive” by the test.

An example from a nice write-up of this issue shows how difficult the issue is to understand when stated in probabilities:

In one study, Gigerenzer and his colleagues asked doctors in Germany and the United States to estimate the probability that a woman with a positive mammogram actually has breast cancer, even though she’s in a low-risk group […]:

The probability that one of these women has breast cancer is 0.8 percent.  If a woman has breast cancer, the probability is 90 percent that she will have a positive mammogram.  If a woman does not have breast cancer, the probability is 7 percent that she will still have a positive mammogram.  Imagine a woman who has a positive mammogram.  What is the probability that she actually has breast cancer?

Gigerenzer describes the reaction of the first doctor he tested, a department chief at a university teaching hospital with more than 30 years of professional experience:

“[He] was visibly nervous while trying to figure out what he would tell the woman.  After mulling the numbers over, he finally estimated the woman’s probability of having breast cancer, given that she has a positive mammogram, to be 90 percent.  Nervously, he added, ‘Oh, what nonsense.  I can’t do this.  You should test my daughter; she is studying medicine.’  He knew that his estimate was wrong, but he did not know how to reason better.  Despite the fact that he had spent 10 minutes wringing his mind for an answer, he could not figure out how to draw a sound inference from the probabilities.”

When Gigerenzer asked 24 other German doctors the same question, their estimates whipsawed from 1 percent to 90 percent.   Eight of them thought the chances were 10 percent or less, 8 more said 90 percent, and the remaining 8 guessed somewhere between 50 and 80 percent.  Imagine how upsetting it would be as a patient to hear such divergent opinions.

As for the American doctors, 95 out of 100 estimated the woman’s probability of having breast cancer to be somewhere around 75 percent.

The right answer is 9 percent.

The twist in the story comes from how easy this is to get right if you phrase the exact same question in a “natural frequencies” format:

Eight out of every 1,000 women have breast cancer.  Of these 8 women with breast cancer, 7 will have a positive mammogram.  Of the remaining 992 women who don’t have breast cancer, some 70 will still have a positive mammogram.  Imagine a sample of women who have positive mammograms in screening.  How many of these women actually have breast cancer?

My question is whether this format can be adapted to the case of empirical testing of a theory. We have three main terms that need to be “adapted”:

• Risk of false negatives – How likely is it that the theory will be rejected if it is actually true? Let us say this is quite unlikely (2%)
• Risk of false positives – How likely is it that the theory will be supported if it is actually false? This depends on how “observationally equivalent” it is to the true theory. Take rational addiction theory as an example: One article argues that consumption with a trend often will test positive for rational addiction even though there is no rational, forward-looking planned change in tastes going on. I find trended consumption far more plausible, so let us put the likelihood of “trended consumpti0n or some other non-rational addiction mechanism is actually present and testing positive by mistake” at 40%
• “Base-rate” – In medicine, this is the known prevalence of the disease in the population being tested. In our case it is not easily interpretable – but ask yourself , for instance, “how likely do I think it is that real junkies and cigarette smokers are gradually implementing a forward looking plan for changing their own tastes, and that this is the reason their use of cigarettes, heroin or whatever is gradually increasing?” Let us say we put this at 5%. This does sound both speculative and “science-fiction”ish, but could we interpret this as saying “of all the possible universes that would have unfolded consistently with our current history and experiences – in how many of these do we think real junkies and cigarette smokers [….]”?

If we think this sounds OK, we could try something along the lines of:

My feeling/guess is that only 20 out of 1000 universes we might be living in would have rational addicts. In all 20 of these universes rational addiction theory would do well in testing. Of the remaining 980 universes that do not contain rational addicts, some 392 will test positive. Imagine that our current test-results indicate that we live in one of the 412 universes that test positive for rational addiction. How likely is it that there really are rational addicts?

This is (I think) quite basic Bayesian updating, so the whole “new” thing here is the attempt to rephrase it in a way that makes the base-rate point obvious: After positive test-results, the likelihood that we are living in the rational addiction world would be 4,8% – higher than 2% (our starting estimate) – but still very low.

(Of course – you may quibble with the numbers I put on it – in fact, so would I – but they’re just there to have something to put into the format I was testing)

An escape from uncertainty? On the support for peer review and hierarchical journals

Some time ago, after discussing peer-review and mailing the first quote from yesterday’s post to a colleague, he responded “OK, so design a better system, then.”
The challenge has been bouncing around in the back of my head for a while, when one day it hit me that (maybe) it is an impossible task – because the perceived benefits of the current system are illusory, while an important benefit of an alternative system would be that it was more transparent and thus would not provide the illusion of authoritativeness, finality and certainty.
Imagine a place where all articles could be published – an online repository of some sort. There’s A LOT of researchers out there, and there would be a flood of papers in any (even narrowly defined) field. You might see which ones other readers have read, you might even have tools in the repository for giving “starred reviews” (as on Amazon) but with scholarly comments, for giving evaluations of reviewers, and thus maybe even average “ratings weighted by how “useful/valid/perceptive” the reviewers have been judged,” and so on. There could be long comment and discussion threads, the different articles could be cross-referenced by researchers and readers, the whole thing could be in a “facebook-ish” system that made it harder to be an anonymous troll.
Even so – I think this would prove unsatisfactory to many (most?) researchers: I think there’s a human desire for someone to have the final say and state that “Yes – this is good, important and probably true!” It is a desire to have some external authority that can make the final judgment call that “your work is good!” or that “This result can be cited with confidence!” An open, transparent system makes it hard not to see the apes behind the machine. The “institution” of peer-reviewed, prestigious journals, in comparison, has a somewhat magical aura of authoritativeness and gravitas.
Put differently – the present journal system makes it easy to identify which “giants” we should stand on the shoulders of to see further, and it offers the hope that we can be published in a high-ranking journal and thus be future giants ourselves. A truly open system shows us that we are trying to build on the shoulders of  a large, shifting mass of more or less confused fellow ants all scrambling around trying to do the same thing.
I’m not sure how I can test this hunch – but if it’s correct then it will be difficult to move towards a more open access approach to science based primarily on post-review. Maybe it will change as new generations become more and more comfortable with on-line tools and evaluation methods, I don’t know. But my guess would be that you can marshal all the evidence you want against peer-review and tiered journals and it wouldn’t help. You could show that peer-review fails to catch errors, that referees are biased in favor of conclusions they like, that referees agree as often as two tossed coins, that it is a newfangled thing that was quite unusual in even top journals until the second half of the 20th century (think about it – they didn’t even have photocopiers in the “old days”), that a system of tiered journals creates publication bias in favor of spurious results, provides disincentives to replication studies, and so on and so forth.
Yes – a “top journal” may be just some guy acting as editor who gives two or three researchers access to an enormously impactful “Like-button,” but it doesn’t feel that way.

Peer review and transparency

There is some evidence that the status of your name or institution affects the conclusions of peer review:

There have been many studies of bias - with conflicting results - but the most famous was published in Behavioural and Brain Sciences [14]. The authors took 12 studies that came from prestigious institutions that had already been published in psychology journals. They retyped the papers, made minor changes to the titles, abstracts, and introductions but changed the authors’ names and institutions. They invented institutions with names like the Tri-Valley Center for Human Potential. The papers were then resubmitted to the journals that had first published them. In only three cases did the journals realise that they had already published the paper, and eight of the remaining nine were rejected - not because of lack of originality but because of poor quality. The authors concluded that this was evidence of bias against authors from less prestigious institutions.

The solution sometimes proposed is double-blind peer-review – where the referee does not know whose article he/she is reviewing – which is seen as a way of ensuring that famous names and well-known colleagues do not have an easier time getting published than others.  Daniel Lemire discusses a paper that found double-blind peer-review to actually hurts “outsiders” more than it leveled the playing field. Criticism also became harsher, and the quality increase was marginal at best.

Lemire concludes that transparency is better – interestingly, he makes the transparency argument against both the blinds in the double-blind: He seems to argue both that the author should be known to the referee, and that the referee and the review report should be known to the author:

But the best way to limit the biases is transparency, not more secrecy. Let the world know who rejected which paper and for what reasons.

The slippery slope of the market

An interesting post on the economist deals with a phenomenon the blogger labels “Economism,” the view (expressed by another Economist blogger) that

it is normally the economist's lot to explain to the superstitious public the humanitarian benefits of bringing human life ever more within the cash nexus.

The Economism-post then goes on to discuss whether shifting our perspective of all relationships into the form of consumer-and-provider-commercial relationships is always right, or whether there are some types of relationships we want to see as different from a purely commercial, self-interested transaction.
I would agree that this is a relevant point, that there are some relationships or even some areas of life that are cheapened or altered in a bad way by transactionalizing them or seeing them too much as a quid-pro-quo transaction (even when they “at some level” have that aspect as well). My point today, however, is more whether the market as such lets loose forces that tend to move us in that direction anyway.
The idea is just that if there is some non-commercialized value generated in some arena of our lives, then even if commercializing it would reduce the “total value generated” in that arena, it would still make it possible for someone to monetize and get hold of a larger share of it.
This would be a kind of entrepenurship – establishing a new market – finding a way to frame and promote a new product or service so that people suddenly accept and engage with it in an arena that was previously non-commercial.
For instance (a non-realistic (hopefully), but clear illustration): If you found a way to make it “fun” and socially less distasteful to trade for sexual services (by bumping your phones and agreeing on a price that was then transferred between accounts), then at least part of the “value” of sex would be monetized and the service-owner could capture this through a 1%-off-the-top fee. If 20% of sex in society shifted into this domain – then even if the value of each of these sexual encounters over time became lower than before because of it, the entrepeneur would still earn a load of money.
For this mechanism to work, individuals must be myopic or tempted or in some way not foresee the effect this will have on the long term quality of the activity or good or service in question. Some unforeseen lagged effect or ignored externality must be present. But given that, any non-monetized activity or value will be an alluring “potential market” for any entrepeneur who is able to package this into a commericalized market-activity.