Can we ever read articles of the opposite political persuasion? An alternative model

Sean A. Munson, & Paul Resnick (2010). Presenting diverse political opinions: how and how much Proceedings of the 28th international conference on Human factors in computing systems : http://doi.acm.org/10.1145/1753326.1753543



Can we ever be convinced by someone we usually disagree with completely? Can we even manage to read regularly people whose views are antithetical to our own? These are fascinating questions, I think. First, because they are political questions; conversations and debates matter very much for any kind of open, democratic society. But I find them fascinating because they also bring up questions about the nature of knowledge: is knowledge just a matter of true and false propositions? Or is it something different, a mangle of practices, propositions and institutions, and in some way inherently inarticulable?

I bring all this up because of a talk I attended at CHI 2010 -- a presentation of a paper by Sean Munson and Paul Resnick at the University of Michigan -- that explored their very preliminary results of getting people to read articles with opposite political persuasions. Here's the abstract:

Is a polarized society inevitable, where people choose to be exposed to only political news and commentary that reinforces their existing viewpoints? We examine the relationship between the numbers of supporting and challenging items in a collection of political opinion items and readers' satisfaction, and then evaluate whether simple presentation techniques such as highlighting agreeable items or showing them first can increase satisfaction when fewer agreeable items are present. We find individual differences: some people are diversity-seeking while others are challenge-averse. For challenge-averse readers, highlighting appears to make satisfaction with sets of mostly agreeable items more extreme, but does not increase satisfaction overall, and sorting agreeable content first appears to decrease satisfaction rather than increasing it. These findings have important implications for builders of websites that aggregate content reflecting different positions. [pdf]

Remember this was a CHI paper so there was a lot of emphasis on how to "present" diverse views so as to make people read them. The results were disappointing -- people don't really seem to want to read the opposite side -- but since not everything has been tried yet, and the web still has a lot to evolve, we shouldn't really lose hope.

I want to speculate on a different sort of model in this blog-post. I am going to take for granted that people of opposite political persuasions need to talk to each other in a liberal democracy*. But if it is important for Democratic-leaning and Republican-leaning (or as they are called in the US, liberal and conservative) voters to talk to each other, is it a good way to rank news articles and people on a sliding scale from liberal to conservative and then mix and match them up? Or do we need to classify people (and articles) on some other orthogonal parameter? I.e. a parameter that doesn't correlate with being Democratic or Republican-leaning.

In what follows, I am going to propose two such parameters. This is by no means a very systematic analysis, just some thoughts that I've been playing around with, based on my own personal experiences. Most important, I have absolutely no idea how I would go about implementing such a system computationally and frankly, it may be wrong and not even work in practice. With all those caveats in mind, here goes.

The first parameter maps the content of an article. The content of an article spans the spectrum from "uncertain" to "certain". An "uncertain" article sounds unsure of itself, has many caveats in it, has a respectful tone perhaps, even if it does have definite conclusions. A "certain" article is more sure of itself, perhaps more dogmatic, even snarky. One point though. If an article is uncertain, does that mean that its author is non-ideological? Not at all. All it means is that he chooses to express himself in a certain way that seems uncertain even though the article itself may end up endorsing a very specific ideological point.

The second parameter maps the disposition of the reader. The disposition of the reader spans from "prefers interesting" to "prefers true". This is not a straight-forward spectrum and its terms need some explanation.

The terms are based on the expression "I prefer saying something interesting to something true". E.g. most philosophers**, you see, would rather write something interesting and therefore be read by generations, than solve a problem definitively and thus not be discussed at all in a few decades. In the same vein, a reader who prefers something interesting likes intricate arguments even if sometimes they lead to conclusions he does not agree with. Note that this does NOT mean that this sort of reader does not have an ideology or that he is an "independent." Nothing of the sort is required, just that he likes to read clever things.

Since no ideology has a monopoly on clever things to say so this reader probably reads a lot of clever things that come out of his own camp. A reader who prefers "true" things is the opposite. He prefers "truth" to "play", has no use for "play" and would rather prefer to say it as it is. When I say truth, I don't really mean truth-as-it-exists. I mean things that the reader believes to be true. Note that most readers will fall somewhere in between these two positions.

If indeed, in an ideal world, these metrics i.e. the certainty of an article, and the disposition of the reader could be computed with some degree of accuracy, and if we also knew the ideology of the reader as well as the ideology of the article, this is what I would do in order to get people to read articles with opposite points of view:
The flowchart is clearly a little vague and is not meant to represent some definite algorithm. The heuristic that it depends on is that those readers with a taste for the interesting will find at least some of the uncertain articles that are however in the opposite ideological camp thought-provoking to read.

At this point, it also seems appropriate to explain the theory of knowledge that underwrites this model:

• I assume that a person's ideology gets fixed pretty early in life. A person has an ideology by the time he or she reaches the mid-twenties. Ideology however does not mean a voting preference. It means a way of looking at the world, a preference for certain types of people (who become your friends) and a certain set of issues that become important, and a certain stance towards them.
• Ideologies do not change because someone offers "rational" arguments for the opposite side. People only change their minds about certain things, and these things are small technical things. World-views do not change much. When they do, and this is rare, the comparable analogy for it is religious conversion.

This seems pretty bizarre at first sight. After all, if one believes that world-views don't change, why even bother recommending articles of the opposite ideology? The mistake here, I think, is that people assume that getting people to read more articles of the opposite ideology is just an instrument for something else, sometimes termed "bipartisianship." I think of it as an end on its own, irrespective of whether it makes people change their views (which I don't think it does anyhow).

Let me make one final point before I end this (pretty unsatisfactory) post. What makes me think that people don't change their minds all that much? Well, for one, it's my own interactions with people. Arguments about politics or policies are rarely closed, the way that arguments about the validity of a mathematical proof are, for instance. So there is no control on what counts as an argument. People usually have an infinite variety of arguments to choose from and many discussions are a circle of question-begging assertions.

In his book, Knowledge and Social Imagery, David Bloor provides a very good example of how world-views are woven together. In tribal Azande society, an oracle is usually asked to tell the people the witches residing in their midst. Being a witch is taken to be a hard physical fact and it is commonly believed that a male witch passes on this "substance" to his sons, a female witch to her daughters. One would therefore think that when the oracle says someone is a witch, the whole corresponding line of people will have been or will be witches. In practice, however, the Azande don't act this way, only the close paternal kinsmen of a known witch are considered witches.

At this point, one could simply say that the Azande are being illogical and irrational. But to counter this, Bloor gives another example, one that's closer to our own culture. We commonly believe that a murderer is one who deliberately kills people. But in that case, are pilots and soldiers also murderers? As citizens who rely on the armed forces to protect us, we will immediately resist this conclusion. But aren't we being illogical or irrational here? After all, if one takes the laws of logic seriously, they lead us inexorably to this conclusion: All murderers kill people deliberately. Soldiers kill people deliberately. So all soldiers are murderers.

But no, someone will say. It all depends on what you mean by "deliberately." Soldiers don't kill "deliberately," they kill to protect us, or they kill because one of us had been killed. Or it all depends on what you mean by "kill." And so on and on. The point here is that our ideologies are "informal knowledge", we believe them because they are common cultural practices. When we reason about them ("formal knowledge"), the laws of logic and reasoning are flexible enough that we can use them to justify what we only know informally (i.e. our ideologies). That's why rational argument never succeeded in converting someone to a different ideology. Arguments like that tend to go round and round in circles. But that doesn't mean that they are unimportant or that one shouldn't be having them. One should just remember what they can or can't accomplish.

*The discussion above is heavily US-centric. In India, for example, it is very hard to isolate "opposite" political persuasions, but that's a topic for another day.

** E.g. here is Daniel Dennett's variant of it:

In an informal survey, I have been asking philosophers a slightly different question recently, and will be pleased to field further answers in response to this review: Which would you choose, if Mephistopheles offered you the following options?

(1) simply solving an outstanding philosophical problem so definitively that after a few years, only historians ever mentioned it (or your work) again, or

(2) writing a book that was so tantalizingly equivocal and problematic that it would be required reading for philosophy students for centuries to come.

The history of science offers many instances of the first sort, and none, really, of the second, but I find that many of my philosophical colleagues admit to being at least torn by the choice. They would rather be read than right. Perhaps it is of the "essence" of philosophical problems to admit of no permanent solutions, though I doubt it, but in either case it is no wonder we make so little progress.

The strange subway signs of Madrid (Notes on Navigation 2)

In a previous post, I talked about my Madrid trip and the experience of navigating with a guidebook as well as the relationship between carrying a guidebook and remembering a trip.

In this post, I want to talk about navigation signs, in particular how the subway signs I encountered in Madrid were so different from those in New York City*.

In New York, a subway sign will say something like this:





This means two things. To get to subway X, go left. To get to subway Y, keep walking straight.










Here's an example. The meaning, for anyone who's lived or traveled in NYC seems fairly obvious. (Image source.)







Now, this sign, usually means that subway X is to your left but to get to subway Z, walk down this staircase that that you see going down.









For example, check out the sign on the left. It points to a staircase going down.








This sign, however, means something totally different in Madrid, as I found to my astonishment. The subway X part is pretty clear: "go to your left for subway X". The part about subway Z is however interpreted as: "to get to subway Z, keep walking straight." When I saw this sign in Madrid, I paused and looked around me for a staircase going down and was surprised to see none. After about 5 seconds of looking around and realizing that no staircase could be found anywhere remotely close, I understood what the sign meant: "keep walking and you will get to subway Z".





For an example, see this figure (to your left, source here, the image has been doctored to change the name of the station). The sign is saying "keep walking along in this direction to get to Salida". There is no staircase involved (although that's hard to see in the picture).



I am going to discuss some implications and possible reasons for this difference in what the same sign means.

First, I think this is a great example of what is sometimes called the indeterminacy of writing as a representation**. We typically understand writing -- and what are these signs if not a form of writing? -- to represent certain stable features of the world and to describe them. A prototypical instruction set, for example, has a certain sequence of steps to assemble furniture or on how to accomplish a certain task using a computer program. Sometimes the aim is to make these instructions as "complete" and as "independent" as possible. We sometimes want these instructions to "stand on their own" and at still other times, this "standing on their own" is taken as the measure of the "goodness" of an instruction set.

The example of the street-signs of Madrid shows that instructions or signs or any kind of representation can't really "stand on their own". They need the world to give them meaning (or to "interpret" them) while they give meaning to the world at the same time. Stable features of the world don't really exist but emerge from the interaction of the sign with what it represents.

So instructions for assembling a piece of IKEA furniture make sense only when you look at the components of that piece and the instructions together. The instructions for how to use a computer program to do a certain task make sense only when you're "in" in the program. Subway signs -- the arrows -- don't have any meaning by themselves unless they're paired with the world they are supposed to represent. And as the example above shows, the "down arrow" can mean two different things New York and in Madrid.

Here's Agre on this:

The work of relating a text [in our case, the subway signs -- SAK] to a concrete setting -- looking around, poking into things, trying out alternative interpretations, watching someone else, getting help -- will generally be both "mental" and "physical", though it is best not to distinguish. Relating a text to a concrete setting takes work because the text might be relevant to the situation in a great variety of ways. The text has a great deal of "play", so that much of one's interpretive effort must wait until the time comes. This is the opposite of extracting a "meaning" from a text as soon as it arrives. The point is not that interpretation is wholly unconstrained by the text; rather, interpretation is constrained jointly by the text and by the circumstances in which it is interpreted.

Second, why does the "down arrow" come to mean two different things in New York and Madrid? Could this be a quirk of the Spanish language? E.g. perhaps in Spanish it may be more common to say "Stick to this path" rather than saying "Go straight ahead"? I am not sure and in any case, it doesn't seem like a good explanation anyhow (for which see below).




If you take a look at this page which has directions signs from many airports around the world (some are shown above). You can see that the Berlin and Istanbul airports have signs like those of Madrid i.e. the "down arrow" representing "go straight ahead" while Zurich, Singapore and Warsaw have signs like NYC i.e. the "up arrow" representing "go straight ahead". I don't think anymore that this has to do with "quirks of language". I do think this would be a fascinating research question: how did these simple arrow signs come to mean what they mean in different places?

Finally, here's a thought. If a gaping staircase does open up in a straight path in Madrid (as it does, say, on 42nd street in NYC when one walks between the 1 and the A trains), how would they represent it in a sign? I can't remember anything from my trip there so I would assume such things just don't occur all that much. But my hunch is that they would use a "down arrow", the same thing they use to show "walk straight ahead". This only proves further the indeterminacy of signs by themselves. A sign only means something when paired with what it shows. A "down arrow" above a staircase points to a staircase, the same "down arrow" when above a straight path tells me to "walk straight ahead."

Related posts: Notes on navigation.

* This post would have been so much better if I'd actually taken pictures of those strange signs. Alas, but no. Instead I'll be using diagrams and photos that I found on the web. Yay, Flickr and the Creative Commons License!

**For a good introduction to what this is, see Phil Agre's great paper: Writing and Representation.)

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Math-phobia, pedagogy and the choice of occupations

The general question that I am interested in is this: how does the pedagogy of science (and mathematics) influence a student's choice of occupation i.e. whether she decides to become an engineer, a doctor or even a lawyer? In particular, I am interested in understanding why the best and the brightest high school students in the United States usually choose not to become engineers or scientists while those in India choose the technical professions in overwhelming numbers, even though the style of science pedagogy is roughly the same in both countries. My hunch is that while the style of science pedagogy remains the same, the style in which other subjects are taught is drastically different and this, in turn, is responsible for the difference in the number of students who opt for technical careers.

But I want to develop this answer a bit more. So here goes.

How do styles of pedagogy influence the choice of a career/major? In a sense, the answer is trivial. An "internalist" account would go as follows: students develop an interest in a subject depending on how it is taught to them. This, in turn, depends on the pedagogical style used. Their like (or dislike) for certain subjects will typically determine the occupation they end up in. Reasonable. So, for instance, a student who likes science and mathematics in high school, is more likely to be an engineer or choose a technical occupation. A student who dislikes mathematics intensely ("math-phobia") is more likely to want to be a journalist, writer or lawyer.

The reason I call this an "internalist" account is that it ignores all the "external" factors that go into choosing a career. Jobs have values associated with them: some pay more than others (law, medicine); others have cultural prestige (academia, research), yet others offer you the possibility of being influential (journalism, politics). These values (and a lot of chance: the job market, familial obligations) often determine an occupation a person ends up in.

For the purposes of this post, I am going to ignore the these external factors (or only bring them up when needed) and concentrate on the "internalist" account that assigns a student's like or dislike for a certain subject as the main reason for her choice of occupation. But this account itself is too vague and non-explanatory if the issue at stake is choosing an occupation out of all possible occupations; e.g. it gives almost no understanding of why someone goes into journalism rather than law. It does, however, have a certain power if the issue in question is the choice between a technical occupation (i.e. being an engineer, a doctor, a computer programmer, or a researcher in science and technology) and a non-technical occupation.

So far, so good. Let me now restate the internalist account with a focus on the choice between technical and non-technical occupations. Students who experience some kind of aversion to science and mathematics in high school (a.k.a. "math-phobia") will typically not choose to go into technical occupations. The ones who don't feel math-phobic are more likely to become, say, engineers.

What causes this math-phobia in high school? Mostly, it is attributed to the "authoritarian" way that mathematics and science (especially physics) are taught. (Note that from now on, when I use the word "science", it means "mathematics and the mathematical sciences".) The use of an adjective, often used to describe hated regimes, being used to describe science education often surprises people. The metaphor is largely correct, I think but it has some slightly different connotations while describing a style of pedagogy.

Consider how we learn Newton's laws of motion. The laws are first, of course, taught to us and explained. But explaining the law and expressing it in symbolic form (e.g. F=ma for the Second Law) is only the first step. The most important thing in learning the laws of motion is to be able to use them to solve problems. It turns out that most students can't solve problems on their own just by learning the laws because the symbolic forms of the law used in these equations (e.g. mg = d2s/dt2 for problems dealing with free fall and gravity) bear little resemblance to the canonical F=ma. Students need to learn to transform F=ma so that it applies to a variety of scenarios. And the way they learn this is by solving practice problems, scores of them. There is no other way of mastering Newton's laws of motions without solving practice problems.

You can see why students can often find the procedure authoritarian. They are being asked to do something (solve problems) without really "understanding" it. But of course, as more problems are solved, students do understand, which, in this context, means that they learn to inductively apply the laws of motion to a variety of scenarios (free fall, motion on an inclined plane, etc.) on their own. Understanding in science means to understand in practice.

But solving problems can often be a frustrating experience, especially when one is learning them because an active construction process is involved. The current problem has to be seen in terms of previously solved problems, that too in a certain way. Note that, in this scenario, the teacher can only do so much: she can demonstrate more solved problems in class, teach the students certain tricks of the trade, she can arrange the problems in the textbook in a very precise order of difficulty so that progressing from one problem to the next is more orderly, she can help a student when he gets stuck. But that's really about it. It is finally the student who has to grind through the unsolved problems in order to master a concept -- and one can see easily why students get turned off.

In order to see how limited the actions of the science teacher are, consider a teacher of history teaching, say, the Great Depression. She can show a movie about the Depression, read extracts from novels set in that time, look at photographs or she can ask her students to write short stories set in the Depression highlighting some aspect of it. The pedagogical possibilities are endless (and "creative"). But if you compare this to the possibilities existing for a science teacher, who has to get her students to solve problems and thereby master the concept, and you will see how rigidly structured the teaching of science is.

The interesting thing to note here is that science pedagogy is pretty much the same across countries and cultures (which makes sense, because science is a relatively autonomous culture). Of course, there are differences: the class size may be smaller or larger, the teacher may have more time or less to demonstrate solved problems, the textbooks may be better or worse or more easily available. But the end objective is the same: in some way or the other, the teacher has to make the students solve the practice problems, which in turn means that the students have mastered a concept.

And yet -- and this is where we come to the main point of this post -- if the style of pedagogy influences the choice of college majors and/or occupations, and if the style of science pedagogy is still the same i.e. authoritarian, then why do so few students opt for engineering/technical majors in the US while so many of them do in India? To make the point a little more narrow and precise, why do the "best and brightest" students in the US choose non-technical professions (law, business) while those in India opt in overwhelming numbers for engineering and medicine? (For now, assume that this category of the "best and brightest" just exists.)

Obviously this claim needs to be substantiated. Without going into too many details (and honestly, I don't really have the statistics), let's take the admissions figures of an elite US university.


Admissions statistics for Columbia University show that there were 21273 applicants for Columbia College (CC) but only 4154 applicants to the School of Engineering (SEAS). Assuming that this is a fair sampling of the best and the brightest, it's clear that the number of CC applicants is almost 4 times the SEAS applicants! Now it is possible that a lot of CC graduates major in Mathematics or Physics but if this (and this) data is any indication, I would say that most common major would be Psychology.

Contrast this with some figures from 2007 of the Joint Entrance Examination (JEE) to the elite Indian Institutes of Technology (IITs). "Over 240,000 candidates took the exam, and "7,209 (almost 3 percent) are eligible to seek admission to 5,537 seats" in the seven IITs, IT-BHU and ISMU-Dhanbad." Clearly the best and the brightest in India prefer the technical professions (and majors) to the non-technical ones.

Why the discrepancy?

I am not going to argue that the "external" factors don't matter here. Clearly, they do, perhaps overwhelmingly so. Getting into an elite engineering college in India is sometimes the only sure-shot way of guaranteeing oneself a pretty decent job straight out of campus. And engineers and doctors in India are well-regarded socially too.

But I am going to argue that the "internal" factors (a student's interest in science and technology, i.e. whether or not a student suffers from math-phobia, which in turn depends on the styles of pedagogy) matter too. Here's why. Clearly someone who is math-phobic and has an aversion to mathematics will not opt for an engineering degree in college. So at the very least, something must happen that makes the best and the brightest in India less prone to math-phobia. Clearly that something cannot be the style of science pedagogy, which, if anything, is even more authoritarian in India.

The difference, I will argue, lies in the way that other subjects -- the non-technical ones -- are taught in India. In these subjects, students are asked to learn a lot of things by heart (a.k.a. rote learning) and there is an emphasis on facts rather than method. When compared with this, the best and the brightest often find the problem-solving methods of mathematics and science strangely appealing.

Consider my own experience. I went to a fairly good private school for middle and lower-middle class students. Our school did not have too many resources; the library was practically non-existent, there was hardly any equipment in class beyond chalk and blackboard (no projectors, no computers, no DVD players) and we rarely used any other books besides the state-printed textbooks. Our teachers did the best they could under the circumstances. But the limited time per week devoted to a subject, the amount of material that needed to be covered in that time (and our teachers were pretty scrupulous and covered everything they would put on the exam), and the structure of the exam itself (regurgitating facts that one had memorized) meant that they couldn't do much.

Consequently, non-technical subjects like history, geography, civics, the languages, and part of the sciences (that did not involve problem-solving, like biology or parts of chemistry) become exercises in teaching "facts". Meaning that they never became "interesting" to any of the students, and certainly not to the best and the brightest. My point is this: non-technical subjects were taught as "facts", not "methods." Mathematics and the mathematical sciences, on the other hand, despite the authoritarian way they were taught, were still taught as "methods".

Consider, say, a subject like history. This was taught from a state-sanctioned textbook. E.g. our 10th grade textbook had chapters on World Wars I and II, and then multiple chapters on India's freedom movement. Each chapter would be 6/8 pages and therefore fact-packed. Exams would be filled with questions like: "What were the immediate causes of World War I?" "What were the provisions of the Morley-Minto reforms of 1909?" And so on. Consequently (to me) school history became a compendium of facts to be memorized but the idea that history is also a method, an analytic tool came to me much later, when I started reading other books on history.

What's my point? My point is that I could never conceive of what a historian did because history seemed to me to be a body of well-defined facts without any idea of what methods a historian uses. I had no idea that there was even a science called sociology (beyond school, my reading consisted of voraciously reading pulp novels. Since access to good books in India is limited, I didn't come into contact with it in my outside reading either). But on the other hand, I was well-aware of the "methods" of mathematics and the mathematical sciences. It was easy to imagine what scientists, mathematicians or engineers do: they solve problems! It wasn't so easy to imagine what historians or sociologists did. (I probably I thought they had to master a lot of facts in order to be historians -- and who wanted to do that??!)

Consider exams. History exams (to take one example) were dreadfully tense affairs, the day before the exam particularly so as I had to revise and remember all the facts. Mathematics and physics exams, on the other hand, were not so much work. The method to solve problems had already been learned (and once learned, couldn't be forgotten). I think that exams (and their difficulty/easiness) have a lot to do with how a discipline is perceived by students. The conservative and authoritarian nature of science education meant that if one had solved all the problems before-hand, the exam itself would be a breeze. On the other hand, attending all the history lectures did not mean that one had memorized all the facts, so preparing for the exam was a hot and excruciating affair, full of memorizing and swotting. Was it any wonder that most students (or at the very least, the best and the brightest) preferred science to the other disciplines?

To sum up, my point in this post was to posit an "internal" explanation, rooted in styles of pedagogy, for why the best and the brightest high school students in the US do not opt for technical majors/careers in college, while those in India do so overwhelmingly. My explanation for this was that, in the US, it is the authoritarian nature of science education, especially when compared with the "creative" way other disciplines (like history or literature) are taught, that is responsible for the math-phobia. On the other hand, in India, the cultural logic of the same authoritarian style of science education works out differently because students can discern a "method" in it, and they can discern no such method in the other disciplines, just a series of facts. Consequently, the best and the brightest high school students in India are less likely to suffer from math-phobia.

[Note: There is probably a deeper sociological explanation of this and I am hoping that the sociology of Pierre Bourdieu can help me in this.

If you have any comments, notes, suggestions, disagreements, please post them in the comments below. I'd love to hear what you think of this. Also if you know any prior research on this, please do post it below. Thanks!]

Economics and Sociology: What Scott Sumner's defense of EMH really proves

(Photo courtesy Flickr.)

Via John Quiggin, I came across (and really liked!) this piece by Scott Sumner defending Eugene Fama's Efficient Markets Hypothesis (EMH).

I interpret him as saying the following:

1. The claim that a "bubble" exists when asset prices violate economic "fundamentals" is full of s*^#. Because there's no (algorithmic, mathematical) way of knowing what the "fundamental" price of an asset is. Hence, there's no sure-shot (again, read algorithmic) way of predicting a bubble; people who make it sound really easy (take asset prices, compare them with the "fundamental" price, find the difference, and voila, you know if there's a bubble) don't know what they're saying.
2. Yes, people do predict bubbles and they have a 50/50 chance of being right. No one has been right 100% of the time, and certainly, no algorithm. It's only in retrospect, with 20/20 hindsight that one can know a bubble exists, because it's only in hindsight that one can know the factors that went into making a bubble.
3. The reason that people do keep predicting bubbles is because a "cognitive illusion" is at work here. Because they think they can predict bubbles but they really can't because they end up being wrong 50% of the time. If they build models that predict bubbles, the models will only work 50% of the time too.
   

I think this is all fairly persuasive but to me, it points to a very different conclusion, one that he almost certainly wouldn't agree with. Here it is.

Economists are famously open* about the fact that they consider their discipline to be superior to most other social sciences, like sociology and political science. More rigorous, more empirical, etc. etc. Of course, sociologists disagree.

What Sumner's piece reveals is that simply using a lot of mathematics in your models doesn't really guarantee that your discipline is any more rigorous than, say, sociology. (I don't mean that sociology is not rigorous, I am just saying economics isn't more rigorous than sociology, like, say, physics is. Or, in other words, the social sciences and the natural sciences are different sorts of beasts and economics is a social science, despite all its pretensions to be otherwise.) Sumner seems to be saying that is impossible to construct a general-purpose algorithm (or mathematical model, take your pick) that is able to predict a bubble correctly every single time. To perceive that a bubble exists (and more importantly, to do it before it bursts and takes us all down with it) requires careful interpretation, not a derivation.

It turns out people do predict bubbles, even if they are wrong 50% of the time. Sumner thinks this is because they have a "cognitive illusion". This is wrong-headed. I think what this says is that bubble-prediction seems to be some kind of lived-in, embodied, practical skill that people acquire by living in the world and being a part of it (so of course, they think they can predict bubbles). So I can feel that housing prices are too high, and will crash, and of course I can use mathematical models to justify it to others, but that feeling (or rather, intuition) is based on far more than what that any mathematical model (or algorithm) can capture. In other words, knowing that a bubble exists is a tacit skill, to use Michael Polanyi's phrase, and therefore is something that just can't be captured in a general-purpose model that works every single time. It needs to be done on a case-by-case basis.

So consider this scenario. Person A predicts that our current situation is a bubble and person B says it is not. Person A has a model and person B, perhaps, has one too. Then it is impossible to say who is right, A or B, and be right, every single time. And the decision can certainly not be made just by looking at each model. One needs to see the assumptions the model is based on, the factors a model may overlook, or something else altogether. In other words, it is not only impossible to construct a general-purpose algorithm to predict bubbles, it is also impossible to construct an algorithm that can tell us whether A or B is right. (This, of course, is Thomas Kuhn's point in Structure, only he talks about competing scientific paradigms.)

Sumner's post, it seems to me, leads inexorably to the conclusion that economists stop looking for "generalities, principles" of the economy as a whole (or at the very least, stop pretending that they possess some methodology that is superior to the other social sciences). That a "unified social science" is not really going to materialize and we (all of us social scientists) are all better off looking into issues (in this case, bubbles) on a case-by-case basis. Seems to me that this is something that sociologists and anthropologists have long acknowledged.

Would this be a completely wrong interpretation?

* Yes, even that anti-EMH Paul Krugman):

As for social sciences other than economics, I am interested in their subjects but cannot get excited about their methods -- the power of economic models to show how plausible assumptions yield surprising conclusions, to distill clear insights from seemingly murky issues, has no counterpart yet in political science or sociology. Someday there will exist a unified social science of the kind that Asimov imagined, but for the time being economics is as close to psychohistory as you can get.

Notes on Navigation 1

One of the problems that fascinates me is the problem of navigation. Not web navigation, although that is interesting in its own right, but navigation in the physical world: how we get from one place to another, whether its on a route we already know, or a new route, how we make use of instructions and artifacts like maps. (Edwin Hutchins has, in Cognition in the Wild, looked at how groups of people collectively navigate at sea.)

Like Lucy Suchman, I don't think that to go from one place to another, human beings simply make plans in their heads and execute them. I think navigation is a complex activity, where many things come together: the plan that a person makes, the artifacts he uses like maps, written instructions as well as other artifacts like road signs all of them shape the experience in complex ways.

I had an interesting experience on a recent trip to Spain that is related tangentially to navigation. Now previously, when I went on trips abroad, I read a page or two of Wikipedia to find out what the noteworthy sites were. A best, I would write down their names, at worst, I would just try and remember the names in my head. The trips didn't turn out badly and I've always enjoyed just walking randomly on the streets of a new city without any particular destination in mind. But I don't remember much of these trips either. One of the reasons for this could be that I did not annotate the photographs I took immediately after the trip while the memories were still fresh. Another reason could be that I did not keep a journal of the trip. But I think there may be a third reason as well.

When I visited Amsterdam with a friend of mine, I noticed that he did things differently. He used a guidebook, a good one and usually planned to visit a couple of spots a day. No explicit planning was done, at least not in much detail but we always had the guidebook in hand as we walked around. We were always trying to map what we saw to what the guidebook said (well, he did the hard work but he explained it to me once he'd gotten it anyway). Just having the guidebook in hand, consulting it, somehow made the excursions much more informative. And more importantly, I remember much more of the trip! Even though, like the previous ones, I did not take photographs or keep a journal.

My hypothesis is that it was carrying the guidebook around that made a difference. Because we were always cross-referencing the guidebook, because our daily outings consisted of visiting a couple of locations in the guidebook, because even when we were just randomly walking the streets, our activity was directed: all of it had the effect of etching the details of the trip more firmly in my mind.

I got a chance to test this hypothesis when I recently went to Madrid for the WWW 09 conference. This time, I made sure that I did things the way my friend did in Amsterdam. I bought a guidebook for Spain: the National Geographic. I didn't plan on visiting too many spots on the same day, just one or two.

And you know what -- it worked! My trip seemed more substantial and I seem to be able to recall it better! Here are some observations on why and how having a guidebook made a difference:

• Reading the guidebook before the trip in detail proved to be unnecessary. I had time to just skim it on the flight to Spain and that was sufficient.
• It was much more important to have the guidebook with me as I was sightseeing: having it in hand, where I could refer to it easily, rather than keeping it, say, in my knapsack.
• I used the maps in the guidebook, especially on the day I took the Plaza Mayor Walk that was recommended. But the guidebook also helped me navigate the museums! The following point talks about how it did this:
  
• For the museum each exhibit is equally important. So one tends to get lost in a museum, getting overwhelmed by the sheer amount of stuff on display. The guidebook, on the other hand, will tell you what the important pieces in the museum are and more importantly, how to get to them (mine did, and I think a good one should!).
  
  • This meant that my activity as I went around the museum was directed: I was no longer just walking around but walking around with a purpose. I would continually refer to my guidebook to see where I was, both physically, and in terms of paintings/exhibits that I had already seen and the ones I still had to see. It was the same when I was walking on the road: my activity was directed towards the next landmark I wanted to see. Even if I was walking around randomly, it was still around a certain landmark.
    
  • At some point (this was in the Prado) my guidebook proved insufficient for the spatial navigation i.e. the instructions it gave me for moving from one room to another were outdated since the paintings had been moved around (obviously). So I picked up a floor plan, which had a map of the rooms with the painter(s) each room was dedicated to. I used the guidebook to find the next painting I wanted to see, the plan to figure out where I was and where I wanted to go, all the while looking at the paintings I was surrounded by to figure out which room I was in. -- it seems complicated to explain it but moving back-and-forth between these things proved surprisingly easy. I think a similar process takes place when we are trying to find our way in an unfamiliar place in our day-to-day life.
    

So where is this leading? Nowhere, really. And are there any takeaway points?

• One comes to mind: guidebooks should be designed keeping in mind that the best way for a person to use them is in-situ, in the process of sightseeing. And since my guidebook served the purpose so well, I am guessing they already are designed that way.
  
• For me at least, sightseeing became much more productive when it was directed than when it was random. I remember much more from this trip than I do from all of my others combined. It was because I had the guidebook with me so I had a name to go for every street I walked on and perhaps names help you remember better? Or to turn the argument slightly around, when I look at the guidebook today, after the trip, the images in my mind combine with the words on the page and the memory gets solidified. Or something. Sorry for the mixed metaphors but I haven't found a technical way of saying what I want to say.

That's all! More points about navigation to come later.

Calling all scientists and mathematicians

[This post doesn't really fall into the topics this blog is supposed to be about, but it's reasonably close. And besides, my other blog is too frivolous for it anyway.]

Jerry Coyne has a new essay in the New Republic on the split between science and religion, about "the never-ending attempt to reconcile science and religion, and why it is doomed to fail". Blah blah. I am of the firm opinion that the matter cannot be settled either way and this essay, like most others, manages to settle nothing. (See this rather interesting, but rather tendentious, response by Jim Manzi and this one by Alan Jacobs).

The reason for this post though is that I found some of the reasoning that Coyne uses near the end of his essay to be grossly misleading, although again, I can't make a cut-and-dried case for anything. Here is the objectionable graf:

The NOMA [Stephen Jay Gould's idea of science and religion being Non-Overlapping MagisteriA] solution falls apart for other reasons. Despite Gould's claims to the contrary, supernatural phenomena are not completely beyond the realm of science. All scientists can think of certain observations that would convince them of the existence of God or supernatural forces. In a letter to the American biologist Asa Gray, Darwin noted:

Your question what would convince me of Design is a poser. If I saw an angel come down to teach us good, and I was convinced from others seeing him that I was not mad, I should believe in design. If I could be convinced thoroughly that life and mind was in an unknown way a function of other imponderable force, I should be convinced. If man was made of brass or iron and no way connected with any other organism which had ever lived, I should perhaps be convinced. But this is childish writing.

Similarly, if a nine-hundred-foot-tall Jesus appeared to the residents of New York City, as he supposedly did to the evangelist Oral Roberts in Oklahoma, and this apparition were convincingly documented, most scientists would fall on their knees with hosannas.

Scientists do indeed rely on materialistic explanations of nature, but it is important to understand that this is not an a priori philosophical commitment. It is, rather, the best research strategy that has evolved from our long-standing experience with nature. There was a time when God was a part of science. Newton thought that his research on physics helped clarify God's celestial plan. So did Linnaeus, the Swedish botanist who devised our current scheme for organizing species. But over centuries of research we have learned that the idea "God did it" has never advanced our understanding of nature an iota, and that is why we abandoned it. In the early 1800s, the French mathematician Laplace presented Napoleon with a copy of his great five-volume work on the solar system, the Mechanique Celeste. Aware that the books contained no mention of God, Napoleon taunted him, "Monsieur Laplace, they tell me you have written this large book on the system of the universe, and have never even mentioned its Creator." Laplace answered, famously and brusquely: "Je n'avais pas besoin de cette hypothese-la," "I have had no need of that hypothesis." And scientists have not needed it since.

In a common error, Giberson confuses the strategic materialism of science with an absolute commitment to a philosophy of materialism. He claims that "if the face of Jesus appeared on Mount Rushmore with God's name signed underneath, geologists would still have to explain this curious phenomenon as an improbable byproduct of erosion and tectonics." Nonsense. There are so many phenomena that would raise the specter of God or other supernatural forces: faith healers could restore lost vision, the cancers of only good people could go into remission, the dead could return to life, we could find meaningful DNA sequences that could have been placed in our genome only by an intelligent agent, angels could appear in the sky. The fact that no such things have ever been scientifically documented gives us added confidence that we are right to stick with natural explanations for nature. And it explains why so many scientists, who have learned to disregard God as an explanation, have also discarded him as a possibility.

There is an interesting sleight-of-hand at work here. "If a nine-hundred-foot-tall Jesus appeared to the residents of New York City", Coyne says and if this was "convincingly documented", most scientists would start believing in God. You think so? I don't. There are a number of reasons why I think so but they call center on the meaning of the term "convincing documentation". How does one decide whether the documentation is "convincing"? Is there a definition of the term that most scientists would agree on? Because you see, if the documentation is not convincing enough then I can reject the nine-hundred feet tall statue of Jesus as the work of a scam artist while still asserting that science is not a priori indisposed to the idea of God.

Coyne further goes on to say that science is not philosophically committed to materialism; that rather, this is a pragmatic commitment, that scientists use it simply because it works. Again, I am unconvinced. I agree with Giberson's claim that "if the face of Jesus appeared on Mount Rushmore with God's name signed underneath, geologists would still have to explain this curious phenomenon as an improbable byproduct of erosion and tectonics." Coyne says this is nonsense, that were this to happen, he and his fellow scientists would convert to Christianity and take the scriptures literally. He, in fact, goes on to list other phenomena that will make him believe in God: "faith healers could restore lost vision, the cancers of only good people could go into remission, the dead could return to life, we could find meaningful DNA sequences that could have been placed in our genome only by an intelligent agent, angels could appear in the sky".

Nice try. But I'm not buying. My contention is this: should any of these things happen, not one scientist will take them as evidence of the existence of a Christian God. Rather, several other things will crop up: the validity of the documentation will come up, fraud will be suspected -- and at the end of it all, most scientists will believe that there is some unknown causal explanation at work here, something that will surface once more work is done -- and get to work on it pronto. (What do you think?)

Why does Coyne bring this up? I suspect this is a rhetorical gambit intended to play upon the image of the man of science in our culture today. This image, which most of us have, says that the scientist is a man who changes his mind by looking at the facts, that he never lets his facts be dictated by theory, that instead he changes his theories once the facts change. As opposed to this, the religious believer is one who doesn't change his mind even when when the facts say otherwise. So Coyne is saying: "Look, we scientists are ready to change our minds if such and such happens, whereas religious believers aren't even prepared to say such a thing!"

I am not saying that there isn't some truth to this popular image of religion and science. And I am pretty clear about which side I am on. But this notion in the popular imagination that there is something called a "fact" which scientists have easy access to, and which they use to change their theories has got to be contested -- at least a little bit. One of the reasons why science is so successful at what it does is precisely because scientists do not change their minds easily, that they stick with a problem, despite many setbacks they encounter at solving it. Thomas Kuhn's point in The Structure of Scientific Revolutions (a book I will talk about in length perhaps in another blog post in the future) is that the development of science proceeds in two alternate steps: a conceptual revolution that is followed by a period of what Kuhn calls "normal science" which is again followed by a conceptual revolution. In "normal science" scientists settle down with problems, which they model as puzzles. In doing so, they build their concepts further. While doing "normal science", scientists will massage their concepts to fit the data but they refrain from changing their concepts drastically. In other words, they do not lose their faith in their concepts, do not abandon them until a "crisis" (Kuhn's term) is reached. Then, and only then, do the concepts get radically revised.

I am oversimplifying what Kuhn says. But my broader point is that scientists have to have some kind of "faith" in their concepts in order to do normal science -- no science would get done if concepts were radically revised at the drop of a hat! The terms "normal science" or "puzzle solving" aren't meant to be pejorative either. They may sound unexciting -- who, after all, would want to do "normal" science? But the point is that these puzzles themselves are extraordinarily challenging even for the best and the brightest -- and cracking them gives a scientist fame and recognition among his colleagues. The community of scientists, to quote a metahpor used by (I think) Michael Polanyi, is like the blind man with a cane. He uses his cane to see what comes ahead, he gets better and better at using it with practice but he also has faith that the ground will not drop away under his feet suddenly. Scientists grope their way to knowledge. My point here is that science, like liberalism, has a set of implicit metaphysical commitments. These are not unchanging, they have evolved over the last five hundred years but nevertheless they exist.

Here's a thought experiment to see what I mean. Suppose there is an omnipotent God. Suppose that there is a woman in the last stages of a deadly cancer. Finally suppose that there is a faith healer. The faith healer appeals to God to save the woman. God decides that he will grant the healer's wish and the woman's cancer starts going into remission. Will the scientist accept the healer's explanation that God did this? Well, no. To a scientist a result is only a result if it is replicable as widely as possible. So the said scientist takes the healer to another woman who has the same condition as the healed woman, more or less. Now however God decides not to honor the faith healer's wish; there is no remission. Ergo, the scientist's point, that the faith healer is a scam artist, and the woman's remission was due to some unexplained causes which further research will bring to light. Well, you say, what will it take for the scientist to accept the healer's point, that it was indeed God who healed the woman? Here's my guess. He would take the healer to N number of similarly ill women and ask him to heal them. Even if all of them did get better (God allowing), the scientist is still free to think that the reason for this is some yet to be explained metabolic similarity between these women. Indeed, what may change is that the scientist now believes the healer to be sincere yet self-deluded and not, as he believed earlier, a scam artist.

I am not really sure where I'm going with all this. And I am sure there are many loopholes in what I have said. But that's just my point: there is no water tight way of saying that science is incompatible with literal belief. And frankly, aside from the policy implications for a democracy, by itself, it is an uninteresting question by itself.