Mind blindness & Unknown unknowns

I'm now going to conclude an extensive series of posts on Nate Silver's The Signal and the Noise: Why So Many Predictions Fail-but Some Don't, starting here. The extra attention is justified by how well-written and fruitful the book is.

I've naturally come across (Nobel Economics Laureate)Thomas Schelling before, especially his ground-breaking The Strategy of Conflict. But Silver uncovers some writings of his I hadn't come across, in a preface to the book Pearl Harbor: Warning and Decisionby Roberta Wohlstetter.

Schelling writes of our propensity to mistake the unfamiliar for the improbable: There is a tendency in our planning to confuse the unfamiliar with the improbable. The contingency we have not considered seriously looks strange; what looks strange is thought improbable; what is improbable need not be considered seriously.

But at least this flawed type of thinking would have involved some thinking. If we had gone through the thought process, perhaps we could have recognized how loose our assumptions were. Schelling suggests that our problems instead run deeper. When a possibility is unfamiliar to us, we do not even think about it. Instead we develop a sort of mind-blindness to it. In medicine this is called anosognosia:part of the physiology of the condition prevents a patient from recognizing that they have the condition. Some Alzheimer’s patients present in this way.

We have an inherent tendency as human beings to develop kinds of mind-blindness. I love that term.

And perhaps the biggest flaw of all is to believe the future is more predicable or controllable than it really is.

There is reason to suspect that of the various cognitive biases that investors suffer from, overconfidence is the most pernicious. Perhaps the central finding of behavioral economics is that most of us are overconfident when we make predictions. The stock market is no exception; a Duke University survey of corporate CFOs,whom you might expect to be fairly sophisticated investors, found that they radically overestimated their ability to forecast the price of the S&P 500.

Heuristics

So what can we do? What we need is heuristics:

We can think of these simplifications as “models,” but heuristics is the preferred term in the study of computer programming and human decision making. It comes from the same Greek root word from which we derive eureka.A heuristic approach to problem solving consists of employing rules of thumb when a deterministic solution to a problem is beyond our practical capacities.

In a complex an confusing world, I think good heuristics may be the best we can hope for.

It really is a very thoughtful and insightful book covering a wide range of issues. Silver clearly has deep talent far beyond baseball statistics and election forecasting.

 

Acceleration and flexibility in the economy

This is a fascinating story in the NYT about Zara, the huge Spanish clothes retailer, which says a lot about business and consumption. Zara has vastly accelerated the cycle of fashion. They monitor what customers are buying, and also saying to sales clerks. So if one item is selling, they can have more of them and more similar designs manufactured, shipped and on the shelves within three weeks.

Merchandise moves incredibly quickly, even by fast-fashion standards. All those thousands of Inditex stores receive deliveries of new clothes twice a week.

In this way, says Masoud Golsorkhi, the editor of Tank, a London magazine about culture and fashion, Inditex has completely changed consumer behavior.

“When you went to Gucci or Chanel in October, you knew the chances were good that clothes would still be there in February,” he says. “With Zara, you know that if you don’t buy it, right then and there, within 11 days the entire stock will change. You buy it now or never. And because the prices are so low, you buy it now.”

And fashion trends are now worldwide and instantaneous.

I remarked that it must be interesting to see what is fashionable in Turkey but not in New York and vice versa. I imagined that different nationalities still had different tastes, at least in terms of fashion. But I was wrong.

“Actually, the customer is more or less the same in New York and Istanbul,” she said. “There are differences, like Brazilian girls like more brilliant colors, whereas in Paris they use more black. But in general when you find a fashion trend, it’s global.”

It is similar to the evolutionary paradigm I have talked about before, in fact, from the very beginning of the blog. They try lots of small experiments, shipping just three or four skirts or jackets to a store. When they find something working, they immediately replicate and build on it. And they adapt extremely fast.

I wonder how fast the economy can really spin, though. Fashion has always been a special case. Change for the sake of change, for display and identity, might apply to clothes or phones. It seems to apply less to things like cars than before, though. The economy is increaingly intangible altogether.

And some things get increasingly commodified and generic at the same time as other things become the object of relentless change. Commodification is just a heartbeat or a moment away.

 

Prediction and Bayesian testing

I still have a little more to say about Nate Silver's The Signal and the Noise: Why So Many Predictions Fail-but Some Don't, which I have been looking at starting here.

I argued in this post the other day that what we need for predictive success is not so much big data as self-awareness. Hypothesis testing ought to help us revise our point of view.

Silver rightly emphasizes prediction is largely a means to an end:

The philosophy of this book is that prediction is as much a means as an end. Prediction serves a very central role in hypothesis testing, for instance, and therefore in all of science.As the statistician George E. P. Box wrote, “All models are wrong, but some models are useful.” What he meant by that is that all models are simplifications of the universe, as they must necessarily be. As another mathematician said, “The best model of a cat is a cat.”Everything else is leaving out some sort of detail. How pertinent that detail might be will depend on exactly what problem we’re trying to solve and on how precise an answer we require.

I keep talking about the importance of purpose, for example here about the difference between maps and models.

The potential pitfalls mean we have to know ourselves, says Silver:

This is why it is so crucial to develop a better understanding of ourselves, and the way we distort and interpret the signals we receive, if we want to make better predictions.

Frequentism

However, much recent statistics has run well and truly off the rails by assuming that error arises from our measurements rather than our perception or judgement. Silver criticizes simple-minded statistical "frequentism", which he says mostly stems from nineteenth century English statistician Ronald Fisher.

The idea behind frequentism is that uncertainty in a statistical problem results exclusively from collecting data among just a sample of the population rather than the whole population.

The idea is you can act as if you can repeat an experiment innumerable times. The more random experiments you do, the more accurate the outcome.

Essentially, the frequentist approach toward statistics seeks to wash its hands of the reason that predictions most often go wrong: human error. It views uncertainty as something intrinsic to the experiment rather than something intrinsic to our ability to understand the real world. The frequentist method also implies that, as you collect more data, your error will eventually approach zero: this will be both necessary and sufficient to solve any problems. Many of the more problematic areas of prediction in this book come from fields in which useful data is sparse, and it is indeed usually valuable to collect more of it. However, it is hardly a golden road to statistical perfection if you are not using it in a sensible way. As Ioannidis noted, the era of Big Data only seems to be worsening the problems of false positive findings in the research literature.

 

Frequentism dominated statistics in the twentieth century. Fisher criticized Bayesian statistics (which we will come to in a moment) for beng insufficiently objective. But, says Silver,

Nor is the frequentist method particularly objective, either in theory or in practice. Instead, it relies on a whole host of assumptions. It usually presumes that the underlying uncertainty in a measurement follows a bell-curve or normal distribution. This is often a good assumption, but not in the case of something like the variation in the stock market. The frequentist approach requires defining a sample population, something that is straightforward in the case of a political poll but which is largely arbitrary in many other practical applications. What “sample population” was the September 11 attack drawn from? The bigger problem, however, is that the frequentist methods—in striving for immaculate statistical procedures that can’t be contaminated by the researcher’s bias—keep him hermetically sealed off from the real world. These methods discourage the researcher from considering the underlying context or plausibility of his hypothesis, something that the Bayesian method demands in the form of a prior probability. Thus, you will see apparently serious papers published on how toads can predict earthquakes, or how big-box stores like Target beget racial hate groups,which apply frequentist tests to produce “statistically significant” (but manifestly ridiculous) findings.

Plenty of investors have lost their shirts by having risk models which assume that market events follow a neat normal ( or similar ) distribution.

Bayesian Probability

Instead, Silver strongly advocates the older Bayesian statistics. In essence, one must specify a prior probability of an outcome , based on one's current beliefs. Bayes' formula then specifies how you should alter that probability in response to incoming data and events, which produces a posterior probability. It is about recognizing your current expectations and beliefs, amd learning from new evidence.

The Bayesian viewpoint, instead, regards rationality as a probabilistic matter. In essence, Bayes and Price are telling Hume, don’t blame nature because you are too daft to understand it: if you step out of your skeptical shell and make some predictions about its behavior, perhaps you will get a little closer to the truth.

We took a step backwards when frequentism arose.

As we will see, science may have stumbled later when a different statistical paradigm, which deemphasized the role of prediction and tried to recast uncertainty as resulting from the errors of our measurements rather than the imperfections in our judgments, came to dominate in the twentieth century.

For me, the point about Bayesian probability ( which I haven't ever used professionally) is not so much the math but a procedure which requires you to test and revise your beliefs in response to evidence. I think Silver overdoes Bayesian probability as THE answer, but his main target in his own intellectual world is likely very much the frequentists. He is a statistician. So we can understand his emphasis on an alternative statistical tradition.

Incidentally, I am no statistician, but I have been intrigued in the past by Keynes' arguments in his A Treatise on Probability (Classic Reprint), but I'll leave that for another time.

 

 

Mobs and Marvels

The other end of the scale from medieval mobs burning schools in Pakistan: Microsoft demonstrates something just like a Star Trek Voice Replicator.

It could be the next best thing to learning a new language. Microsoft researchers have demonstrated software that translates spoken English into spoken Chinese almost instantly, while preserving the unique cadence of the speaker's voice—a trick that could make conversation more effective and personal.

 

Pakistan slowly melts

Meanwhile, grim signs in Pakistan. A family spent thirty years building one of the best girls schools in Lahore. The son of the family,a teacher in his thirties, was particlarly passionate about talking to many groups about astronomy.

But on 31 October the school was burned to the ground by a crowd who had heard it was accused of blasphemy. Lab equipment and computers were looted. Hundreds of library books – obviously with little use to the mob – tossed into the fire. Some even tried to pull the marble tiles off the floor

A teacher had inadvertently missed a page while photocopying, linking a sentence about the Prophet Mohammed to a chapter on begging.

This blasphemy law is devouring Pakistani society from within. It is an all-purpose tool in the service of intolerance. It has often been used against religious minorities, but Muslims are paying the price as well. The repeal of the law, unfortunately, is unlikely. Some voices critical of the law have already been silenced by intimidation and violence, such as the assassination of the governor of Punjab, Salmaan Taseer in 2011.

What a terrible story. A country of 190 million is slowly sliding into a civilizational abyss. I was in Islamabad, Rawalpindi and some of the northern areas about fifteen years ago, and even then travel to Karachi or even main roads toward the south was not advised.

The Islamist methods used against the west, such as suicide bombings and oversensitivity, are now having their worst effects at home.

Demography and the election

I'm still working through some of the implications of the election, although cautious about drawing conclusions too fast. Perhaps the most widespread immediate explanation is that Republicans can't win now because of changing demographics, especially more Latinos.

It's not actually clear that this explains this election, at any rate. According to RealClearPolitics, the absolute numbers of minority votes is likely to have hardly risen at all once postal ballots are counted and final results are in. The big difference this time is five million fewer white voters turned up at the polls. Obama did not suffer as much disaffection from disappointed liberal constituencies like students, but many whites seemed disaffected with Romney but unwilling to vote for Obama. I think this is not yet proven yet , but the data bears watching. And in any case, I think immigration reform is exaggerated as a means to Latino votes. Poorer working-class Latinos are likely to vote Democratic for a generation or two, no matter what Republicans do.

Megan McCardle doubts talk of the emerging democratic majority is justified, partly because we've been hearing it since the 1990s and it has not stopped GOP blowouts like 2010, and partly because the Democratic coalition itself is likely to fracture. Most Latinos are white, and could behave like the many Irish and Italian Catholics who fled the party since Reagan. There could also be massive conflicts between unions and other parts of the Democratic coalition.

Don't take this for a "Hey, GOP, everything's fine! Don't you go changing!" I've been saying for years that the GOP has run tax cuts out as a campaign plank--indeed, they're now over the cliff and about to plunge while Roadrunner chortles. .. . And they've now nominated two candidates who have put forward almost nothing that couldn't be found in Reagan's 1980 platform. The party desperately needs some new ideas to sell to the American public.

But I am highly skeptical that last night means they've gone into some sort of permanent decline. It was a close election in which Obama lost states that he carried in 2012. The Democratic bench is very weak--the current leading candidates to succeed Obama, Hillary Clinton and Joe Biden, will be 69 and 74 in 2016. And Obama is going to have to preside over some very, very tough choices. We can't borrow a trillion dollars a year for another four years. Nor can we get all the money from Republican constituencies; they just don't have enough of the stuff. Whoever's ox Obama chooses to gore will probably be a considerably less enthusiastic coalition member come 2016.

This is probably true.

You could have argued the traditional Yankee Calvinists were supplanted in Massachusetts in the nineteenth century, and much of the American Protestant mainstream by Catholic and Jewish immigration in the early twentieth century. All are now lumped together as "white" by the liberal press. This has been going on a long time, and assimmilation tends to grind away the differences over time. The Democratic Party tends to cater to the less assimilated, but that is historically a moving target.

There is a much more worrying possibility, though, as well. John O'Sullivan writes in National Review:

But it would be false comfort (and the kind of irresponsible optimism I detest) not to mention a darker possibility. That possibility is that whites will develop a defensive minority consciousness in response both to their statistically weaker position.

That has happened before where majorities have become minorities, and it is a “rational” response (so to speak) to this change in their condition. When their collective power was numerically unassailable, they felt able to extend generous concessions to other groups. When they feel threatened, they defend every item of privilege and resent every loss. [..]

The late Sam Huntington warned in his fine book Who Are We? that a racial concept of American identity might gain ground in the circumstances of America’s whites losing their majority status. I didn’t buy this explanation at the time. I still think it is somewhere down in the low teens of possibility. And the spread of intermarriage is one hopeful defense against it. But it cannot be dismissed entirely and thus deserves a mention alongside the sunnier prospects.

America works as a multiethnic society precisely because in principle we believe that ethnicity is less important than what unites us. E pluribus unum. An alignment along largely ethnic lines would be potentially disastrous. Look at Yugoslavia or Nigeria or Northern Ireland or the collapse of the Austro-Hungarian empire or the current Middle East , such as Syria, for where that leads. Diversity can quickly turn into conflict in the wrong circumstances.

The melting pot made ethnicity less important in the past. A racial spoils system could enflame differences if the stakes - i.e. the whole future of the country - are as high as the liberal press claims.

I said before the election the left often prefers the romantic dream that turns to darkness. History offers many examples of instances where changing demographics produces resistance and conflict, not a liberal paradise. Liberals would be much better off emphasizing differences of ideas as the basis for political coalitions, not ethnicity or race. And everyone would be better off thinking about what is good for the country as a whole, not particular sectional interests.

 

 

Blaming a whale (and narrative) for defeat

From a David Brooks exchange with Gail Collins in the NYT:

David: This might be a good time for Republicans to redouble their commitment to the reality-based community. Did you see Byron York's reporting from inside the Romney campaign? They apparently had this giant computer model called Orca - named after a whale because it was bigger than anything the Democrats could imagine. It processed huge amounts of data and late in the day was still projecting a Romney victory until its head exploded. Garbage in. Garbage out.

Gail: The Republican Orca - stop me before I fall into a great pile of Moby Dick analogies.

I think that some people are a little transfixed by Nate Silver's accurate projection of the election, despite, as I've noted, his own much more self-awade mature grasp of the limits of models.

I think this is at root a particular problem with journalism.

Or, to put it more precisely, it is a problem with narrative. Human beings have a hardwired fascination with stories, probably dating from sitting around fires in the savannah fifty thousand years ago. It is often the prime way we transmit culture and values and pointers for behavior we should admire.

But stories can be too trite, and this is where journalism becomes very hedgehog-like sometimes. It is all to easy to link stories into an appealing broader narrative, to instinctively frame things in a way which makes for an entertaining read. Stories are much more interesting than data. Once you go looking for "stories", as opposed to drier mechanical reporting of facts, you introduce potential blindspots. Things can fit all too neatly into narrative buckets.

Journalism thrives on stories. Journalists are paid to look for them, rather than dry, ambiguous, complicated truth in isolation.

We've also seen that hedgehogs typically get more media attention, because big striking claims typically make better tv or better stories. Hedgehogs want an audience for their "one big thing." Journalists want a story, And "triumph of models" is itself a good story.

Of course, at the same time as the fivethirtyeight model did well, the Romney and other models performed badly. But why let that get in the way of a good story?

Self-awareness

This is the real explanation of Silver's greater success here. It is not so much a data-driven approach, as using the data to confront your presuppositions. That is why he does better than most journalists and opinion pundits.

It is not, as the story linked above puts it, simply a matter of primitive punditry against spanking new "data driven rationality".

The scientific method is at root about testing hypotheses, not mining data. You test a prediction or explanation against reality, and you revise your views if necessary based on the outcome. You can selectively use data to confirm all kinds of things if you are not careful. So you need to have a falsifiable hypothesis, a situation where at least in principle you may be forced to revise your view.

There's nothing in narrative that compels you to change your view, because you can always add a twist in the story. Indeed, most good stories will have the hero endure many setbacks and misunderstandings before being proved right in the end.

Even in science, as Thomas Kuhn famously pointed out in The Structure of Scientific Revolutions, conflicting data is often left aside as a "puzzle" or "anomaly" in periods of normal science until there is a sudden change of awareness: I.e. the "paradigm shift" which has become so overused a term it is almost a clichė. Even in the hard sciences, it can take a generation for data to settle arguments one way or another. Theories can sprout dozens of ad-hoc adjustments.

So data can be part of this process of self-awareness. But in a world where we have a blizzard of often conflicting and imperfect data, it may equally reinforce entrenched views.

Romney's big Moby Dick model was probably highly sophisticated, but likely being used to mine data more efficiently, not testing against reality.

Innumerate journalists may regard the statistical models as some sort of new semi-deity, partly because they don't understand it, or the many ways in which statistics can be used to obscure. (There is a famous book called How to Lie with Statistics).

But the real message is not statistics as some kind of new rational technique which guarantees prophetic success. Instead, it is self-awareness.


You need devices to help you retain self-awareness, to see things as they really are instead of ever-more elaborate stories or models. And humans typically find that very hard to do. I've got a few more things to say about Silver's book in that light.

 

Too much data, no ideas

RealClearPolitics has a very good roundup of 21 reasons why Obama prevailed in the end. According to the concluding one:

 

To liberal writer Ezra Klein, Romney’s problem -- in terms of how he’s perceived -- is that what he most values is empirical data, which he thinks complement his natural management skills. “A lifetime of data has proven to him that he’s extraordinarily, even uniquely, good at managing and leading organizations, projects and people,” Klein writes. “It’s those skills, rather than specific policy ideas, that he sees as his unique contribution. That has been the case everywhere else he has worked, and he assumes it will be the case in the White House, too.”

But he won’t get the chance to prove that theory now. The American people, albeit by the narrowest of margins, didn’t choose a manager. For better or worse, they chose a leader, and it’s a measure of Romney’s core that when he said he’d be praying for him to succeed, the people who know him best believe him.

This parallels my concerns months ago. The GOP isn't going to win until it fights and persuades on ideas, not just taxes.

 

Morality binds and blinds

I was talking about Jonathan Haidt's analysis on Monday. What now, he asks in the NYT this morning. Shared fear and common threats can help overcome partisan blindness, he argues.

A basic principle of moral psychology is that “morality binds and blinds.” In many pre-agricultural societies, groups achieved trust and unity by circling around sacred objects. In modern societies, much larger groups bind themselves together by treating certain books, flags, leaders or ideals as sacred and by symbolically circling around them. But if your team circles too fast, you lose the ability to see clearly or think for yourself. You go blind to evidence that contradicts your group’s moral consensus, and you become enraged at teammates who suggest that the other side is not entirely bad (as New Jersey’s governor, Chris Christie, is now finding out).

Unlike a foreign attack, a problem that threatens only one side’s sacred values can therefore divide us, rather than unite us.

Each side finds it difficult to see the other's sacred values, he says. So conservatives tend to deny climate change, for example. Liberals tend to deny potential problems with entitlement spending.

But there are so many "asteroids" about to hit us in coming years that necessity may force each side to recognize some merit in other views. That may mean more attention to economic inequality and the fact that the family has eroded - 40% of births are to unwed mothers, for example.

I think that is a civilized comment for the morning after the election. At least we can be thankful for the country's sake that there has been no hanging chad dramas and Obama won the popular vote as well as the electoral college.

More reaction from me when I'm a bit less tired and have time to absorb it.

 

 

Looks like a very narrow Obama victory

Or so the networks are calling it. It doesn't feel like hope and change. It may be a very bad-tempered, divisive outcome if it is so close.

I was never that enthusiastic about Romney, but the left will take this as a huge victory. And that is sad.

Negative Feedback loops

 

We're looking at Nate Silver's The Signal and the Noise: Why So Many Predictions Fail-but Some Don't, starting here.

Here's another stray reference in the book which reminded me of something else. Silver talks about Larry Summers and feedback loops in the economy.

Usually, in Summers’s view, negative feedbacks predominate in the American economy, behaving as a sort of thermostat that prevents it from going into recession or becoming overheated. Summers thinks one of the most important feedbacks is between what he calls fear and greed.

Summers is a brilliant if famously obnoxious economist, of course, with a lot of practical experience of economic crises in several administrations. Isn't it surprising how little attention there has been to the homeostatic mechanisms in the American economy apart from the price system? A capitalist economy does have some inherent resilience and stability, for all its volatility. But we don't have much real knowledge about confidence and expectations ( for all the attention in economics to rational expectations.)

Another feedback loop is displacement of demand. Recessions produce pent-up demand for houses or cars or consumer durables. Less buying now typically means, other things equal, more buying tomorrow. Choice across time matters. (One of my larger themes, however, is preferences and tastes may not be stable as before, as abundance means many basic needs are satisfied.)

The best account I've seen from this dynamic perspective is Jane Jacob's dialogue about dynamic stability, which we looked at here. Surely someone within the economics profession is doing some systematic work in this vein. Or perhaps not, as it is so heterodox. If someone knows of any such good work, please comment.

In college, one sure way to throw something at an economics problem was to contrast comparative statics with a dynamic view of the economy. But much of the bedrock of economics is still single-country comparative statics, perhaps, for the daring, with some hysteresis - path-dependence - thrown in.

When people try to do dynamics , it necessarily relies much more on assumptions about behavior and psychology. Take, for example, the controversies over CBO dynamic forecasts of tax revenue. Republicans tend to want to see more behavioral changes and higher revenue for a given tax cut because do dynamic effects. Democrats do not.

The upshot is we would do much better thinking about the economy in dynamic terms, with close attention to feedback loops. But we mostly don't.

Maps, not models, by Mapper

We're looking at Nate Silver's The Signal and the Noise: Why So Many Predictions Fail-but Some Don't, starting here.

There's one stray reference in the book, which caught my eye, because I've been interested for over a decade in the difference between maps and models as ways to understand the world. I call myself Mapper on this blog for a reason.

Silver says:

The Finnish scientist Hanna Kokko likens building a statistical or predictive model to drawing a map. It needs to contain enough detail to be helpful and do an honest job of representing the underlying landscape—you don’t want to leave out large cities, prominent rivers and mountain ranges, or major highways. Too much detail, however, can be overwhelming to the traveler, causing him to lose his way. As we saw in chapter 5 these problems are not purely aesthetic. Needlessly complicated models may fit the noise in a problem rather than the signal, doing a poor job of replicating its underlying structure and causing predictions to be worse. But how much detail is too much—or too little? Cartography takes a lifetime to master and combines elements of both art and science. It probably goes too far to describe model building as an art form, but it does require a lot of judgment. Ideally, however, questions like Kokko’s can be answered empirically. Is the model working? If not, it might be time for a different level of resolution.

I hadn't head of Kokko before, but I thought this was interesting. The reason I find the topic so fascinating is that I have sat many times in meetings with senior economic officials - or even more often, their more academic staff - who argue that to think clearly about a situation you need a model in your head, if only to ensure consistency. For most economists, this tends to be linked to some version of Milton Friedman's as-if methodology, which says that the test of a model is not the realism of its assumptions, but the accuracy of its predictions. Simplification and abstraction from reality is what constitutes explanation, and anything else is mere muddle.

Of course, this automatically counts out the value of studying history, which has been largely excluded from the mainstream of education in the discipline for fifty years (despite some good work). Mathematical models have reigned supreme. Optimization subject to constraints is the center of thinking. It is far narrower than even "theory", as it imposes essentially aesthetic constraints on what a theory should be, I.e. mathematically elegant.

It also is dangerously overreliant on consistency. You need to be consistent to be fully rational in your choices, of course, in a narrow sense. But consistency is no guarantee of truth. You can be consistently wrong. And many actual problems are about reconciling different objectives or opposing views. Compromise in this sense is bound to be somewhat inconsistent with someone's basic principles. It is also real life.

However, the most important point is the economic modeling perspective is also a very naive way to think about useful abstraction. A map is an abstraction too, but it tends to be much more useful for most purposes than a mathematical model. Many policy problems are in fact much more like "how to get from A to B" than " maximize X subject to Y".

In particular, maps are much more focused on specific purposes. If I want to get from New York to Albany, I look at a standard road atlas or Google maps. If I want to see where shale gas deposits may lie, I want a geological map of New York State. If I want to sail up the Hudson to Albany, I want a chart which shows sandbanks and shoals and traffic lanes in the river.

In other words, purpose is much more intrinsically present in a map than a mathematical model using aggregate economic statistics or optimization assumptions.

Add to that, as Kokko says, the scale and resolution which are inherent to maps. You abstract away what you don't need. He is right here, but both Silver and Kokko are wrong in a larger sense. The point is not that there is an art to building models so they are , by analogy, a little more like maps with a correct level of detail. It is that maps and models are wholly different ways to understand reality.

Maps are much more suited to seeing risks. The kind of generalized abstraction in a model will not help you avoid the specific rock which is just below the surface as you enter harbor, but the right chart will. A map will show you the massive mountain chain or desert or unfordable river in your way. It will show the paths and cliffs and hazards. A model won't.

So thinking in terms of surveying a specific landscape for specific important features for a specific purpose is just as disciplined an intellectual exercise as developing an abstract model to predict, and much more useful. It can also be a much better predictor of some kinds of problem (eg how long it will take to get to Albany.)


Maps help you see what is actually there, rather than "explain" it in some ultimate universal way. We most often don't need a general theory of roads. We just want to find our way home. Models will predict, but maps will show you the right way. If you want to climb a mountain you are better off bringing a topo map than an abstract mathematical model of paths.

Silver's book is all about prediction, but the more general human problem in decision-making is which way do I go? What is the right direction? Where are the hazards? We need maps more than universalized explanations in most situations. We need a survey of the actual landscape with a particular purpose in mind.

Ways of seeing what is really there are the main way we will develop and make progress. Too much modelling often prevents us from doing that, by distracting people towards the mathematically elegant and tractable, the thin universal rather than the thick description of specifics, and the quantifiable and obvious rather than the danger that lurks in the details.

 

International Legitimacy and the American election

There's one other thing I should say, particularly to foreign friends who may read this blog. I noted just how astonishingly lopsided the view of the election is in many other parts of the world, especially Europe. 97% of Germans would vote for Obama. In any other context you'd think this was a made-up party number from the old East Germany. How can we explain why a country with a conservative government which is getting tough with Greece can be so one-sided in its view of the GOP in the US? The same applies in most other European countries.

Europeans detested George W. Bush too, far more than can be explained by his general political stance. Bush, after all, was actually more in the Nixon mode in domestic policy terms, rather than particularly right-wing. He ran massive deficits and spending, drove through new entitlements like prescription drugs, argued for loose immigration policy with amnesty, and nation-building efforts abroad. He cut taxes, but so do many EU governments from time to time. W claimed to be a "compassionate" conservative. He was relatively left-wing in many senses.

But millions turned out to march against Bush in Europe. "US foreign policy" post 9-11 is the reason, perhaps, but Obama has surged in Afghanistan, killed Bin Laden and stepped up drone attacks.

So what is going on here? I think there is a much deeper split about the nature of international legitimacy, a rift that started with the Yugoslavian war and has only grown since with Iraq. Europe has been committed to multilateralism and pooling of sovereignty for over fifty years now. European elites believe that legitimacy flows down from international law, including human rights law and the United Nations.

So as an example, the response to the euro crisis has been an unending series of multilateral, indecisive and testy summits. The whole motivating force of the European project was to suppress individual national tendencies which could produce war.

This view is (mostly) alien to the United States, where the US constitution is almost four times older than the UN and infinitely more venerated. Legitimacy flows up from the people. The UN and international institutions have little legitimacy, and there is little domestic support for tying national freedom of action. America ( to generalize) sees no advantage in subjecting its actions to the multilateral approval of Europeans, who refuse to bear the burdens or responsibility of actions or defense spending in any case.

So on the one side, Europeans see the GOP as a horrifying cowboy-like entity which violates the most basic elements of international legitimacy. And on the other, America, especially the GOP, sees Europe as fractious, irresponsible and naive, playing internal parlor games while America has underwritten the defense needs and spending which allowed the Europeans the scope to play their children's games in the first place. America deals with China and contains Iran while the Europeans squabble amongat themselves and shout abuse offstage.

It isn't a dispute about left versus right that produces 96% or 97% support for Obama in Europe, so much as a perception he will be more sympathetic to and bound by multilateral norms which give Europe a significant veto over US actions. Romney and the GOP seem like a crazed bull which will stomp all over their most deeply cherished notions.

In fact, if there is one thing that can lead to massive wars, it is profound differences about legitimacy ( see Philip Bobitt's , The Shield of Achilles: War, Peace, and the Course of History). Of course, that is hardly likely to happen here. But it does help explain some of the depth of feeling.

I don't have much sympathy with the European view, which naturally is self-interested in seeking restraints over US actions. We do need international institutions and flexible international law. But it cannot be wholly divorced from power or reality either.

Europeans over-read the lessons of 1945 in their own terms, as delegitimizing national institutions. America does not, and reads the Second World War as much about crazy European political excess and varieties of murderous Utopianism. And we have multilateral utopian excess today.

The differences may narrow again in the future, as the multilateral European project itself is under such strain. Euroskepticism is rampant in the UK, with talk of a referendum on EU membership. Dormant nationalisms like that of Catalonia are reigniting. The EU institutions have if anything negative legitimacy in Greece.

And even Obama, who is venerated so much in Europe, has initiated a "pivot to Asia."

Multilateralism is a wonderful dream, but Europe is prone to utopian overreach. And multilateral commitments are fragile, and sometimes utterly counterproductive. (Take the Kellogg-Briand Pact, which outlawed war, as an example. Just eleven years later the worst war in history began when Hitler invaded Poland).

Those poll numbers against Romney are more a symptom of a European fever about legitimacy than an accurate read on choices in America.

 

 

 

 

So there is an election tomorrow?

I just haven't been able to summon up any interest in the election in the last week, and changed the channel when the national media talked about it. But the day is now at hand.

The RCP average has Obama at 47.8%, Romney at 47.4%, so far within the margin of error. Pennsylvania might even be in play.

Michael Barone is calling a big Romney win in the electoral college, 315 to 223. I have a lot of regard for his knowledge and feel, as the Editor of the Almanac of American Politics. On the other hand, Intrade is pricing a 67.6% chance for Obama, and prediction markets as a rule tend to be slightly more accurate than polls.

It comes down to the vagaries of likely voter adjustments in the polls and turnout. It is going to be a very exciting Tuesday night.

One vote against the left

I'm going to - reluctantly - vote Romney, even though I'm not particularly enthused by him. The main reason is I detest and distrust the hard left, and the preachy zealotry and bigotry they display. Every time they indulge in shrill name-calling, or trying to trying to reduce everything to vague anti-intellectual emotions - "hate", "fear", "greed", they just turn me off.

I think many on the left simply don't see potential problems with their polices, and this makes progressive change far more difficult. I said in a previous post about Jonathan Haidt's studies of what makes people conservative or liberal:

So I think the main reason I'm a registered independent with a rightward tilt, rather than a liberal, is because I think moral capital is important. I think you can better achieve liberal goals by controlling downside risks, rather than ignoring them. I want change, but pragmatic change that works in practice and delivers better lives to people, not a romantic dream which turns into darkness.

Moral capital, in Haidt's definition, is the resources that sustain a community - that restrain selfishness and free-riding and help people pursue common goals. It is the things that make cooperative behavior possible, which liberals often fail to see or just assume will be in place. Intentions are not enough. Outcomes matter. And that makes me more socially conservative.

On economic issues, I could accept a Clinton-style economic policy. I have no sympathy for the libertarian aspects of the GOP. I think tax is a matter of technical advantage rather than a zero-sum distributional struggle, however. It is more complicated than many believe. It is a matter of dynamics , rather than comparative statics (i'll discuss that in more detail in a coming post) so that sometimes tax cuts can increase the capital stock, productivity and hence growth and income. Tax is more than simply distribution of an existing pie, but the size of the pie in the future.

I'm also fairly fiscally conservative, and think current entitlements are largely a way to transfer income from poor working families to richer older Americans. Medical costs are unsustainable. We need massive entitlement reform.

Ultimately I will vote against the left, rather than positively for Romney, because I think the left is the opposite of the things it likes to claim -I.e diverse, tolerant, open-minded. I see it as shrill, hateful towards those who have a different view, and deeply tribal and sectarian and racist in outlook. It has a deeply inadequate conception of "fairness" and "justice" and "equality" as all meaning essentially the same thing.

Leftwingers generally don't talk to conservatives about ideas, but demonize them in adolescent ways. I see the left as much more zealously dangerous than the tea party ever would be. Because the left controls so much of the universities and mainstream media and major corporations, it is a much more present danger.

Obama himself seems intelligent and capable. But he enables the liberal left. When they talk about "forward", I think of the Great Leap Forward. And you should really look before you leap. More spending won't really help the poor so much as the teachers' unions and rich retirees.

Progressive intentions and narrow notions of "fairness" can produce catastrophe, if everything is just a moralized drama of helping the "vulnerable" or hastening the irresistable march of history.

Shrill screaming romanticism and closed-mindedness is the last thing we need, and that's how I see the partisan left. Obama may be cool-minded and pragmatic, but he turned domestic policy over to Nancy Pelosi and Barney Frank and Charlie Rangel in practice. And he will be beholden to the gay lobby and La Raza and union pension plans and the whole outdated "blue" model.

In any case, G is going to vote for Obama, so we will cancel each other out. And it won't make one iota of difference to New York going for Obama regardless of what we do.

 

Overheard in a New York Coffee Shop

Loud declamation in a thick Brooklyn accent:

"Maybe those MAYANS were RIGHT!!"

 

The joy of a hot shower

We keep being amazed by the smallest, most simple things since the storm. We are back in our Manhattan apartment. We cooed with awe at the elevators working, instead of climbing fifteen minutes in the dark with flashlights. The corridors are lit, which suddenly seems like an astonishing thing. We could clean the kitchen and wash out the fridge with....wait for it ... Water!

And I could turn on a stream of hot water in the shower this morning, at the flick of a tap. I stood there thinking that was something Louis XIV could not do in Versailles.

I briefly mentioned before being struck at how much more difficult daily life was in the past. Plumbing was very rudimentary at Versailles. Heat in the winter was mostly absent, as the small fireplaces were highly inefficient ways to heat large rooms.

Fernand Braudel relates in The Structure of Everyday Life that on 3 February 1695, the Princess Palatinate wrote "At the King's table the wine and water froze in the glasses." (p299). Builders later learned to use the draught in a fireplace more efficiently from around 1720 on, he says, but it is a surprisingly late development.

The great monarchs of European history spent the winter wrapped in fur or under layers of blankets.

One of our big problems as human beings is the hedonic treadmill. We very quickly get used to and take for granted good things, so they become routine and not a source of particular pleasure.

But every now and then we are reminded what a daily miracle ordinary life in a modern city is. Central heating. Hot water. Refrigeration. Light. TV and stereo for entertainment, and Internet for connectivity and all the world's knowledge. The outward form of dwellings may be much less grand than the houses of the rich in the past. But apart from hurricanes and other catastrophes, wine doesn't freeze over in our glasses at home.

 

Economic forecasts

We're looking at Nate Silver's The Signal and the Noise: Why So Many Predictions Fail-but Some Don't, starting here.

One other area which has experienced consistent failure is economic forecasting. I cite what Silver says here with a certain amount of glee. Of course, I know this as background information, but to see the hard facts marshalled together is striking. Take a survey economic forecasts in 2008, for instance.

As I mentioned, the economists in this survey thought that GDP would end up at about 2.4 percent in 2008, slightly below its long-term trend. This was a very bad forecast: GDP actually shrank by 3.3 percent once the financial crisis hit. What may be worse is that the economists were extremely confident in their bad prediction. They assigned only a 3 percent chance to the economy’s shrinking by any margin over the whole of 2008.15 And they gave it only about a 1-in-500 chance of shrinking by at least 2 percent, as it did.

Nor was this a once-off occurrence because of a freak once-in-a-lifetime crisis.

In fact, the actual value for GDP fell outside the economists’ prediction interval six times in eighteen years, or fully one-third of the time. Another study,18 which ran these numbers back to the beginnings of the Survey of Professional Forecasters in 1968, found even worse results: the actual figure for GDP fell outside the prediction interval almost half the time. There is almost no chance that the economists have simply been unlucky; they fundamentally overstate the reliability of their predictions.

Aggregate forecasts tend to be more reliable than individual forecasts, however. This has been bad news for in-house corporate economists, who were mostly eliminated in the 1990s. Bluechip or Consensus Forecasts are better.

My research into the Survey of Professional Forecasters suggests that these aggregate forecasts are about 20 percent more accurate than the typical individual’s forecast at predicting GDP, 10 percent better at predicting unemployment, and 30 percent better at predicting inflation. This property—group forecasts beat individual ones—has been found to be true in almost every field in which it has been studied.

Perhaps the new availability of computers made forecasters particularly overconfident in the 1960s and 1970s, he says - the age of the massive economic forecasting model. But ultimately you have to have some theoretical understanding or you will sink into mere data mining, he says.

The idea that a statistical model would be able to “solve” the problem of economic forecasting was somewhat in vogue during the 1970s and 1980s when computers came into wider use. But as was the case in other fields, like earthquake forecasting during that time period, improved technology did not cover for the lack of theoretical understanding about the economy; it only gave economists faster and more elaborate ways to mistake noise for a signal. Promising-seeming models failed badly at some point or another and were consigned to the dustbin.

Economics has inherent limitations on theory, however. One of the decisive intellectual impacts on me in college was learning about the Lucas Critique, which says people's behavior may change when policy changes, so you cannot rely on large-scale econometric relationships. I lost interest in econometrics and forecasting.

The economics profession has mostly responded to this problem by searching for policy-invariant microfoundations. It tries to model individial choice, far below the level of economic aggregates. In practice, this mostly entrenches naive rational-choice mathematical optimization even further.

A better answer to this is deeper knowledge of history. At least some people in the central banks note that we are fortunate that Ben Bernanke was an acknowledged expert in the history of the Great Depression, rather than, say, real business cycle models.

 

Overfitting models

We're looking at Nate Silver's The Signal and the Noise: Why So Many Predictions Fail-but Some Don't, starting here. Silver discusses some of the inherent problems and mistakes people make with statistical models.

One of the most important is overfitting.

The name overfitting comes from the way that statistical models are “fit” to match past observations. The fit can be too loose—this is called underfitting—in which case you will not be capturing as much of the signal as you could. Or it can be too tight—an overfit model—which means that you’re fitting the noise in the data rather than discovering its underlying structure. The latter error is much more common in practice.

It can lead to serious problems.

 

As obvious as this might seem when explained in this way, many forecasters completely ignore this problem. The wide array of statistical methods available to researchers enables them to be no less fanciful—and no more scientific—than a child finding animal patterns in clouds.* “With four parameters I can fit an elephant,” the mathematician John von Neumann once said of this problem. “And with five I can make him wiggle his trunk.” Overfitting represents a double whammy: it makes our model look better on paper but perform worse in the real world. Because of the latter trait, an overfit model eventually will get its comeuppance if and when it is used to make real predictions.

This is one of the great stories of financial markets. People are forever trying to come up with the equivalent of quantitative alchemy to transform historical data into gold. It is quite easy to tune a model so it performs very well on past data, and marches undulations with surprising precision. And it is amazingly easy to lose your shirt when the model goes awry when used to predict where the market will go next.

 

Foxes and Hedgehogs

I'd started talking about Nate Silver's The Signal and the Noise: Why So Many Predictions Fail-but Some Don't, starting here. He is very controversial right now, immediately before the election, because of his prediction of a much higher chance of an Obama victory than the headline polls indicate. So he will be seen by many in partisan terms.

The book is not partisan, however, so it deserves a fair look by people who might fight over his NY Times articles. After all, Silver argues for skepticism about expert predictions as a general rule.

I've mentioned Philip Tetlock's work on political prediction before. Tetlock found that most prediction by political or international relations experts is terrible. As Silver puts it,

Tetlock’s conclusion was damning. The experts in his survey—regardless of their occupation, experience, or subfield—had done barely any better than random chance, and they had done worse than even rudimentary statistical methods at predicting future political events. They were grossly overconfident and terrible at calculating probabilities: about 15 percent of events that they claimed had no chance of occurring in fact happened, while about 25 percent of those that they said were absolutely sure things in fact failed to occur. It didn’t matter whether the experts were making predictions about economics, domestic politics, or international affairs; their judgment was equally bad across the board.

But Tetlock also found one distinction that pointed to more successful forecasts. Isaiah Berlin had revived the ancient distinction between hedgehogs, who know one big thing, and foxes, who know many small things. I've mentioned it many times. Silver puts it nicely:

Hedgehogs are type A personalities who believe in Big Ideas—in governing principles about the world that behave as though they were physical laws and undergird virtually every interaction in society. Think Karl Marx and class struggle, or Sigmund Freud and the unconscious. Or Malcolm Gladwell and the “tipping point.” Foxes, on the other hand, are scrappy creatures who believe in a plethora of little ideas and in taking a multitude of approaches toward a problem. They tend to be more tolerant of nuance, uncertainty, complexity, and dissenting opinion. If hedgehogs are hunters, always looking out for the big kill, then foxes are gatherers. Foxes, Tetlock found, are considerably better at forecasting than hedgehogs.

Hedgehogs have more trouble seeing what is there without predispositions.

Foxes may have emphatic convictions about the way the world ought to be. But they can usually separate that from their analysis of the way that the world actually is and how it is likely to be in the near future. Hedgehogs, by contrast, have more trouble distinguishing their rooting interest from their analysis. Instead, in Tetlock’s words, they create “a blurry fusion between facts and values all lumped together.” They take a prejudicial view toward the evidence, seeing what they want to see and not what is really there.

Hedgehogs are vey good at coming up with stories and narratives that validate their positions - and turn out to be wrong.

The foxy forecaster recognizes the limitations that human judgment imposes in predicting the world’s course. Knowing those limits can help her to get a few more predictions right.

I am, as you might imagine from reading this wide-ranging blog, a fox to the core. I like to take ideas from different disciplines, and recombine and synthesize. So of course I like this argument. Hedgehogs often suffer from serious blind spots, and are prone to fanaticism.

One issue I haven't seen addressed anywhere, though, is what makes people foxes or hedgehogs. Part of it must be personality, although it's difficult to see a direct link to the big five theories of personality. There is some evidence that liberals tend to have higher openness to experience, but in my experience liberals are if anything more prone to ideological narrowness than conservatives. Conservatives often have a skepticism about systems and experts and theory which might make at least some less prone to hedgehog temptation. But one can find ideologues and zealots across the political spectrum. It is a sensibility rather than a particular political conviction.

Some of it must be a matter of education and styles of learning. And some must be a reflection of the incentives we set up. As Tetlock says, you are likely to be a more successful TV pundit by making overconfident big pronouncements than being nuanced.

In any case, Silver's point is that hedgehogs tend to be worse at prediction, not necessarily worse in general. Some of the most gifted people are hedgehogs by nature. As I've said before, Plato was a hedgehog, Aristotle a fox ( which may explain why I've become so interested in Aristotle). Dante was a hedgehog, Shakespeare a fox. Systems have their place. But we do need a feel as a society for the boundaries and limits and clashes of systems.

People like to believe in more certainty than there really is.