Thursday, April 28, 2011

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.

Tuesday, April 12, 2011

On Summer’s silly defence of silly economics

Yesterday I wrote about Larry Summers rules for knowing what economics research to dismiss when you are looking for valid and useful insights. However, he didn’t want to criticize the nonsense too hard:

On the other hand, he pointed out that while there was clearly a need to be prudent while applying research to the real world, it would also be unwise to attack it wholesale. He surmised that it might be possible that some things that seem useless or of limited applicability now would turn out to be useful in years to come (microfoundations for macroeconomics, perhaps?).

This last caveat is one I’ve frequently encountered in two contexts: From people who want to defend basic (natural) science, and from people who want to defend some discipline in economics that is just plain wacky. The argument is the same: It might turn out to be useful in the future.

Though true in the strict sense (I can’t rule out possible value coming from this research), the argument is frequently a “cheat”: I suspect that the person supporting basic science (or abstract economic theorizing) believes that this is nice and valuable intrinsically no matter what the usefulness of the results may turn out to be. But since this is a tough pitch to sell to the general public (especially for the economist), they try to say that “well, this could actually turn out to be valued highly by you even if you don’t care about the intrinsic value.” And yes, there are clear cases of (truly) useful things that came out of (seemingly) pointless and abstract theorizing. Here’s an example from the US Department of Energy:

The discovery that all matter comes in discrete bundles was at the core of forefront research on quantum mechanics in the 1920s. This knowledge did not originally appear to have much connection to the way things were built or used in daily life. In time, however, the understanding of quantum mechanics allowed us to build devices such as the transistor and the laser. Our present-day electronic world, with computers, communications networks, medical technology, and space-age materials would be utterly impossible without the quantum revolution in the understanding of matter that occurred seven decades ago. But the payoff took time, and no one envisioned the enormous economic and social outcome at the time of the original research.

However, it seems wrong (especially of an economist) to just transfer this argument from basic science (whether mathematics or theoretical physics or whatever) to economics. The reason is simple: Take two types of research. One (“applied research”?)is practical and will with high probability lead to valuable insights (in  terms of practical usefulness, economic value, material benefits to humanity or whatever). The other one (“basic research”?) is highly abstract and divorced from empirical applications and will with high probability fail to lead to such valuable insights. However, with both of them there is uncertainty, and we can imagine some probability distribution over “insight-value” that these will generate. It seems to me that unless we have reason to believe that the tail of the “basic science” distribution is fatter – i.e., unless the probability of making truly mind-blowing important progress  is higher for basic than for applied science – then we should always go for the applied in so far as the pragmatic value of the insights is what we want. The expected value would be higher, and the probability of an insight of any given value would be higher with the applied research. In other words, we need a “fat-tail” argument – an argument that the distributions will differ for observations lying far away from the mean (explaining the possibility of such differences in distributions in another context was part of what made Summers resign as President of Harvard , so I would think he sees this).

My point is just that I can see the possibility of this fat-tail argument in terms of certain types of basic science, but that does not mean it is present in economics. In physics there could be some argument such as “the higher the granularity and precision with which we can understand and manipulate the world around us, the more opportunities are open to us for manipulating it to our benefit,” and this can be supported by examples from experience. In mathematics there could be an argument that “the more analytical tools for a broader array of problems, the more mathematics will be able to power up other disciplines and improve their reach and value”. However, I am at a loss to see what more sophisticated representative agent-modelling in DSGE models will give us. To me, it seems more like Tolkienesque fantasy about alternate worlds. And if such fantasy about alternate probably-not-even-conceivably-realistic worlds can be useful – then the question is: Which ones are most likely to be useful, and how do we tell? Why representative agents deciding with optimal control theory? Why is the (seeming) bias towards non-regulation and free markets?

Also – if such modeling divorced from evidence “could potentially” turn out to be useful – surely it could also “potentially” turn out to be harmful? For instance, if it misled (at times influential) economists into thinking that the world is simpler than it is and that it is imperative that our world implements the policies derived from their rational choice fan-fiction. A possible example: Brooksley Born pushed hard for the regulation of a booming, wild-west-frontier derivatives market, and was stopped by President Clinton’s Working Group on Financial Markets. Alan Greenspan argued that regulation could lead to financial turmoil, and at one point she was called by Larry Summers and told that

"You're going to cause the worst financial crisis since the end of World War II."... [Summers then said he had] 13 bankers in his office who informed him of this.

Monday, April 11, 2011

Summers on the policy irrelevance of modern economics

When it comes to some parts of modern economics I’ve often wondered whether anyone (economists or not) actually sees this work as potentially relevant, applicable, empirical knowledge. Larry Summers, who (based on unsystematic and non-random sample of second-hand impressions such as these) is both highly intelligent, overly enamored of unregulated financial markets, and a bit of an arrogant a**hole, offered this heuristic for separating the nonsense from the useful:

[…] read virtually all the ones that used the words leverage, liquidity, and deflation, he said, and virtually none that used the words optimising, choice-theoretic or neoclassical (presumably in the titles or abstracts).

This comes from the Free Exchange blog on The Economist, which also provides further descriptions of modern economics from Lawrence “Ex-President-of-Harvard-ex-treasury-secretary-under-Clinton-ex-chief-economist-at-the-world-bank-and-ex-chief-economic-advisor-to-Obama” Summers:

[H]e talked about all the research papers that he got sent while he was in Washington. He had a fairly clear categorisation for which ones were likely to be useful: read virtually all the ones that used the words leverage, liquidity, and deflation, he said, and virtually none that used the words optimising, choice-theoretic or neoclassical (presumably in the titles or abstracts). His broader point—reinforced by his mentions of the knowledge contained in the writings of Bagehot, Minsky, Kindleberger, and Eichengreen—was, I think, that while it would be wrong to say economics or economists had nothing useful to say about the crisis, much of what was the most useful was not necessarily the most recent, or even the most mainstream. Economists knew a great deal, he said, but they had also forgotten a great deal and been distracted by a lot.

Even more scathing, perhaps, was his comment that as a policymaker he had found essentially no use for the vast literature devoted to providing sound micro-foundations to macroeconomics. (So that would be most macroeconomics since the original Keynesian revolution?) On the other hand, he pointed out that while there was clearly a need to be prudent while applying research to the real world, it would also be unwise to attack it wholesale. He surmised that it might be possible that some things that seem useless or of limited applicability now would turn out to be useful in years to come (microfoundations for macroeconomics, perhaps?).

Wednesday, April 6, 2011

An impossible observation #143

Jeff Dunn was a top-executive at Coca Cola (at one point angling for the CEO-spot) who moved into the carrot business. In a fun Fast Company article on “marketing baby carrots as snack food” we get this little tidbit:

Bolthouse had never marketed its baby carrots. It just sent truckloads to supermarkets, where they got piled up in the produce aisle. Dunn assembled a small team and studied advertising campaigns for other agricultural commodities, such as almonds, avocados, eggs, and milk. They were shocked at what they found. “Every campaign paid back,” Dunn says. “Every single one. Between 2 and 10 times.”

My guess is that if you’d presented this to economists in a seminar they would have shot you down and disbelieved it. After all, this would be tantamount to money lying around on the ground, so if it was true everyone would have acted on it. Since they haven’t, it isn’t. As it is, however, Dunn trusted the study and moved to advertise baby carrots.

[The ad-company] Crispin's campaign, "Eat 'Em Like Junk Food," debuted last September in two test markets: Syracuse, New York, and Cincinnati. (There are plans to expand the campaign to other markets by this fall.) […]

By November, sales in Bolthouse's test markets were up 10% to 12% over the year before, compared to minimal improvement or slight decline in a control group. The vending machines were selling 80 to 90 snack packs per week; a number of schools have approached the company about installing their own machines, and Bolthouse is investigating what it would take to scale vending into a real business.

Though it’s irrelevant to the point, the ads are kinda fun in their surreal, self-consciously meta, “creative” and “off-the-wall” way:


Tuesday, April 5, 2011

In support of computer-assisted trading

Computerized trading using algorithms to sift through massive amounts of data and pick stocks to buy and short has its good and its bad sides. A recent profile of quant trader Cliff Asness, who built a successful such model, made me see a couple of the good points more clearly.

Asness and his partners were among the first to build a stock portfolio--and now a very successful business--by using computer models to combine two simple concepts: buying undervalued stocks (a strategy known as value investing) and betting against overvalued ones (which are called "momentum" stocks, referring to the tendency of securities that are rising in price to keep going up for a time, even when they're overvalued). Using a variety of metrics, the AQR models spit out the names of hundreds and hundreds of stocks that are undervalued (which the firm buys and holds) and hundreds more stocks that are over-valued (which they short, or bet will fall).

Asness explained the differences between quants and quals this way: "A qual digs very deeply into potential investments, but he can only do that with so many stocks, so he needs to have a relatively high level of conviction that he is right, since he's going to hold a pretty concentrated portfolio, say 10 or 20 stocks ... A qual needs to be careful about not making mistakes--one bad mistake in a 10-stock portfolio can get ugly!" He continued: "A quant, on the other hand, has the ability to study thousands of stocks at once, and thus can hold much more broadly diversified portfolios. Because quants hold so many stocks, ones that are even slightly misvalued may still make sense ... If you can find 500 stocks to bet on where each has a 51 percent chance of beating the market, then through diversification, the odds of your overall portfolio start to look pretty good."

This could actually be quite efficient. There’s a host of studies showing that human judgment is poor at synthesizing and weighting a large number of different types of evidence, and that simple, statistical models can outperform humans on tasks such as predicting recidivism, making clinical judgments (psychiatry and medicine), predicting divorce, predicting future academic success, etc. (for an entrypoint to this literature, see here for a blogpost I found that has some good quotes from J.D. Trout and Michael Bishop).

I guess the point is that algorithmic trading can be good or bad depending on the algorithm – and that the danger it brings is more if the ecology of trading algorithms active in a market is of a kind that could create cascading ripples destabilizing the market: One set of algorithms lowering the price of a set of stocks, triggering another set of algorithms to sell these stocks to avoid loss, triggering another set of… and so on. The lightning-fast feedback cycles set up by a changing ecology of (proprietary and secret) algorithms, increasing and decreasing in weight and influence depending on past results in the market, is difficult to predict. Which the article briefly touches on:

Sometimes, though, the quants get too clever for their own good, with potentially devastating effects. Such a moment occurred in the second week of August 2007, when a wave of selling by a group of quant funds using the same trading strategies led to terrible losses, as the firms all tried to sell the same stocks at the same time. As Andrew Lo, a professor at MIT's Sloan School of Management, observed in a September 2007 paper on the event, an "apparent demand for liquidity" that week "caused a fire sale liquidation." Patterson estimated that AQR lost $500 million in a single day, and close to $1 billion in the four-day rout before the markets steadied and started to recover on August 10.


Asness […] told the New York Post that he blamed the sudden losses not on AQR's computer models but on "a strategy getting too crowded ... and then suffering when too many try to get out the same door" at the same time. He told me he finds the argument that quants are "black boxes" of dangerously opaque trading strategies annoying and wrong. "We don't think of ourselves as 'black box,' " he said. "It is a great irony to us that even though a quant can, if willing, fully describe his investment process, it's often called 'black box,' even as the fundamental investor, who can never accurately describe his process, is not tagged with that label. A friend of ours, who is both a quant and fundamental investor, thinks quant is more accurately called 'glass box.' We think that's pretty accurate."

Seems like an interesting thing to study – maybe along the lines suggested in this paper by Brian Arthur, and it also seems related to evolutionary game theory (where strategies increase and decrease depending on their relative payoff in the current environment).

Monday, April 4, 2011

Scientific training is no cure for irrationality

Some scientists seem to think that a PhD and peer-reviewed publications is proof that they are logical, clear-thinking and rational people not prone to the systematically biased recall and interpretation of evidence that ordinary people are prone to.

I have noticed this smug, almost condescending arrogance several times – and I’m probably guilty of it myself as well (as several experiments show, everyone thinks everyone else is biased but that their own (biased) decision was actually rational). However, I have rarely seen a more beautifully clear instance of this attitude then in the following quote from a recent editorial in “Water, air and soil pollution.” I doubt a parody could have made the point clearer:

Now, some people and special interests continue to propagate misleading information about climate change. They are using all of their newly gained knowledge (on how to fool the public) to enhance their greedy benefits. Once the method of scientific inquiry is understood, and the knowledge of how to evaluate scientific claims is at hand, people are not likely to be swayed or confused by misinformation. Some poorly educated people, on the other hand, will be at the whim of the profiteers, not being able to distinguish a lie from a statement based on scientific data. In fact, the more complex an explanation, the more distasteful it might appear to them. These people do not want to be burdened with factual information that their backgrounds do not prepare them to conceptualize; they want to believe in ideas that require minimal intellectual effort. They are likely to prefer a fairy tale to reality; it's so much nicer (for a while) to think that no serious problems exist. Such people just continue to live in a fantasy world that will dissolve when reality becomes oppressive, just as does a dream fades away after one wakes. Then it will unfortunately be too late to correct the problems that were propagated by ignorance.

There’s a nice discussion of some problems with this attitude amongst climate scientists at DeSmogBlog (where I came across the quote), but to my mind this attitude is also a problem within science: If you believe learning the scientific method is like gaining a superpower, then you can relax and trust almost everything you’ve been taught and everything that is claimed by your peers – as well as everything you believe and all the results you get. Paradoxically, then, it makes you less questioning and cautious, and more consensus-oriented and over-confident, and thus less “rational” (by most meanings of the word). At times, it may be useful to recall that we’re all just domesticated apes.