Arguments and Wonkism

The Atlantic has a thought provoking article by Eric Liu.

The heart of the argument is that, while we have been bickering a great deal during this election cycle, we have not been “arguing” much, at least not in the classical sense of arguing, of presenting the premises for respective sides and explaining how and why each side has arrived at their respective conclusions.  Not so much to reach a “conclusion” as to who’s right and who is not, but to understand.  As per the following passage:

Imagine forming citizen “talking circles” all across the country, where people of differing world views agree simply to listen to one another. The point would not be persuasion or conversion. The point would be presence. And the method would not be to discuss ideology explicitly. It would be to address a simple universal question—something like “Who influenced you, and how do you pass it on?”

I think this is a noble argument, something analogous to what I had argued about before as well.  It is also something whose prospects I am deeply skeptical of.

The premise behind this line of thinking is ultimately that, because the uncertainty about the universe is so great, being “right” or “wrong” does not really matter.  Everyone has a sliver of truth, and we are all better knowing all these slivers of the truth.  Wonkism, a cousin of “scientism” the unstated ideology that has come to dominate all discussions these days, explicitly rejects that premise.  The idea behind scietnism is that “science,” arrived at from a certain specific set of assumptions and premises is necessarily right and the conclusions to the contrary necessarily wrong.  The acceptance of these assumptions and premises indicate someone as a member of the “righteous” (or the “brights” or “rationalia” or whatever) while the refusal signals that they belong to the tribe of “uncivilized savages.”  Alternate interpretation of the assumptions and premises is not allowed.  Furthermore, challenge to the assumptions on data is not usually permitted:  the accepted assumptions and premises apply to the interpretation of the data as well.  In order to challenge the accepted assumptions and premises, the data analysis must be presented in a manner that does not overtly contravene accepted assumptions and premises and still show a degree of contradiction.  Not an easy chore for almost anyone.

One might say that , of course, in a sense, this is how science usually works.  Contrary to what people think, Galileo was a crank, whose ideas may have been, in retrospect, more right than his contemporaries’ but who had no means of backing up his claims.  What he ran up against was not an obscurantism determined to thwart science and “progress,” but demanded sufficiently credible evidence to justify tossing out hitherto widely accepted assumptions.  This made them obstructive and reactionary, but not necessarily because they wanted to be.  This becomes more dangerous when “science” is mixed with ideology:  Stephen Jay Gould had made this argument about scientific racism in The Mismeasure of Man.  Whereas Galileo’s adversaries respected his genius and made reasonable, even if ultimately wrong, suggestions as to how his evidence and accepted conventional wisdom could be reconciled and showed themselves willing to be convinced for the right evidence, advocates of scientific racism were committed to faith in their own righteous as well as maintaining their view as the dominant view that actively shaped policymaking.  What was already difficult chore of breaking through the inherently “conservative” bias of “science” was redoubled by the stonewalling of those who had a vested in interest in rejecting contrary views.

Compared to, say, modern physics, social sciences suffer from another problem:  fundamental imprecision of its phenomena.  Einstein’s theory about gravitation made a very precise prediction, which could be demonstrated by a single experiment involving solar eclipse in 1919.  How many propositions in social sciences are similarly elegant and simple?  Humans behave in too erratic a fashion to allow for any prediction to be obeyed with sufficient precision.  Theories involve too many moving parts to generate a clean enough prediction without too many ceteris paribi to hold.  The means with which conduct actual experiments and collect measurements lack sufficient precision.  All data we deal with will be approximate, incomplete, biased, or some combination of these and other problems.  Very few data analyses will be sufficient to knock any set of assumptions off as unjustified.  At best, they can provide support for or against some set of premises, but not necessarily prove them “wrong.”

The problems of “science” and “evidence” in social sciences make for two possible paths:  it could encourage the kind of arguments that Liu suggests, by opening up discussion to multitudes of possible scenarios, premises, and arguments that are compatible with the observed data, encourage alternate approaches to thinking about facts, and different logical implications thereof.  Or, it can allow for obdurate persistence of closed-mindedness, impervious to contrary evidence because no evidence is “good enough.”   If the acceptance of wonkish premises is part of the equilibrium in a signaling game where it indicates membership in a particular tribe, the latter is far more likely, to be complemented by the rejection of the same logic, the good and bad, as the signal for membership in the other tribe.  This is, of course, what seems to often take place in United States today, in course of the culture wars:  if evolutionism, including scientifically erroneous versions thereof, has become the signal for membership in one tribe, creationism indicates the signal in the other tribe.  There is little or no room between them.  Likewise, you buy into the wonkish premises, and the notion that that makes you always right, by faith.  To the degree that wonkism is based on real evidence, you probably are, on average.  But it seems absurd that you should be invariably right–unless it is a cult of cargoish variety that becomes its own self-licking ice cream cone that perpetuates itself through self-justification.

To break down the barriers of wonkism, it seems that two steps are necessary.  First, wonks need to secularize themselves:  theories are only right within limits.  They are words of God, inherently “right,” but are right only as long as the pertinent conditions are met–which is hardly always. If you are standing at the limits of the theory, you need to open yourself up to fresh ideas, even if that means listening to heretics and nonconformists–who might actually know something that you don’t.  At the same time, opponents of wonks need to accept that, for many things, perhaps even most things, wonks know what they are talking about.  Facts and logic are not controvertible, at least where they are applicable.  Opponents of wonks need to come up with something better than they disagree with wonks’ conclusions, but be aware of where their facts and logic come from, where they work, and where they fail, and why.  Paul Krugman, before he became too wonkish, had a nifty essay on mapmaking:  how the people of Middle Ages and Renaissance, before the age of modern cartography, often had a lot of good information about how the terra incognito looked like, but also had a lot of crappy ideas too, and how the modern cartography wound up throwing away the good with the bad and had to take centuries relearning them.  My reaction when I first read the essay was that much of that effort was ultimately wasted–if the cartographers and sailors, travelers, etc. could talk to each other in full recognition of limits to each others’ knowledge, the mapping expeditions and such could have been more precisely targeted, saving lives, time, and expenses while producing better maps and expanding knowledge faster.  Krugman, perhaps already being a wonk, does not seem to contemplate this possibility much, though, as he seems to relish how the cartographers had to tear down the knowledge in order to rebuild it in image they could understand even at expense of centuries’ time and lives of many explorers.

An open, honest, and knowledgeable argument, humble in recognition that our knowledge is limited by genuine uncertainty, in a manner suggested by Liu, could be a great thing.  Could we actually pull it off?  That I am a bit skeptical.  Wonkism, even if built on knowledge, perpetuates itself by convincing itself that wonks know more than they do.  No one to remind them “Respice post te. Hominem te memento.”  Vespasian was joking on his deathbed, as he said “Vae, puto deus fio.”  The same lines seem to be uttered internally by many wonks today, without any pretense of joke, I fear.

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Information, Uncertainty, Incentives, and Trust.

Sandeep Baliga at the Cheap Talk blog has an outstanding summary of the contributions by Bengt Holstrom and Oliver Hart, the latest winners of the Nobel Prize in Economics.

The Holstrom-Hart Nobel is a bit personal to me, albeit through an indirect route, via one of my former teachers Paul Milgrom.  Paul liked to talk about how he came to graduate school not for PhD, but for MBA because he wanted to be an actuary, and how he found ways to apply actuarial thinking to economic theory.  Given the contributions by Holstrom and Milgrom that I found most enlightening brought together statistics and epistemology to a theory of incentives, this is an apt starting point for my reflection on their work.

The example by Baliga is an excellent illustration of the basic problem:  a worker at a burger joint does two things, one easily observable (the number of burgers), the other not so observable (the work in the kitchen).  By tying the incentives only to the number of burgers sold, the principal winds up discouraging kitchen work, and in so doing, subverting his own interests.  The solution is to create a low-powered set of incentives that depend comparatively little on burger sales.

But this opens up a whole slew of other questions.  Two questions pop into my mind immediately because these concern my eventual work in political science, especially with regards the relationship between voters and elected officials.  First, does the principal really know where the unseen parts of the business is?  Second, how does the principal know if the kitchen is being genuinely looked after?

In the legislative arena, the functional equivalent of burger sales come from the public record of legislative accomplishments and actions:  the number of bills, the voting record, etc.   Yet, these constitute comparatively little (and often, easily “faked”) aspects of the legislative work. Fenno and Mayhew, back in 1960s and 1970s, had written about how valued the “gnomes” (to borrow Mayhew’s terminology) who slave away at the unseen aspects of legislative and policymaking work without public accolades are by the legislative insiders, who reward them with currency that are particularly valuable intralegislatively.  Yet, this understanding is not shared by the members of the voting public, nor, apparently, by political scientists lately.  Very few regular voters appreciate how complicated the inner workings of the legislative process is, the kind of hidden negotiations and compromises that are needed to put workable bills and coalitions together–especially bipartisan coalitions.  Still, there is an implicit understanding that, without legislative outcomes, something isn’t being done right, that their agents are shirking somewhat and somehow that prevents their production–perhaps they are right in their suspicion.

The more problematic might be the obsession of the political science in putting data in place of theory (notwithstading the immortal Charlie Chan quote, “Theory, like fog on eyeglass, obscures facts.”–because “data” is not same as “facts.”)  The visible part of the legislative accomplishments, often padded by “faked” votes designed only to put votes on records (for example, the increasingly innumerable but meaningless “procedural” votes in the Senate designed only to publicly show who’s on which side, more  or less), are used to generate various statistics that purport to measure things like “ideology,” which, in turn, are assumed to be homologous to Euclidean space, and are fitted into models.  Since the measures are derived from the observed facts, they describe what goes on fairly accurately–but with significant exceptions that change over time, which are usually dismissed with the claim that they are mere “errors” and “nuisance.”

Fenno and Mayhew thought things differently.  Granted, they didn’t have the kind of legislative data or the tools for analyzing them that their more modern counterparts do (this is literally true:  the changes in Congressional rules around 1975 immediately tripled the number of recorded votes in the House, for example–coinciding neatly with the changes in House organization that followed the ouster of Speaker McCormick, engineered by the liberal Democrats.)  They saw the paucity of data that prevented data intensive analysis on their part as a normal part of the political process, where the seen and the unseen coexist and the importance of the unseen aspects of politics is deemed as important, even by those who did not know the specifics–e.g. the voters.  That brings the question back to what prompted to Holstrom to wonder, why so few contracts are written based on the “sufficient statistic” criterion, and as such, echoes the argument by Weber 100 years into the past (to be fair, there’s a paper by Oliver Williamson on this very point–if I could find it.)  Weber’s argument was twofold.  First, the compensation for the “professional” (“bureaucrat” in his terminology) should be low-powered, set without much regard for the visible indicators of performance because how exactly the professional “performs” is too noisy and complicated to measure with precision.  In turn, the professional should develop a code of ethics and honor–“professional conduct,” literally–whereby their work is carried out dutifully and faithfully without regard for the incentives in the contracts.  If you will, the mail will be delivered with utmost effort, as a point of honor, through rain, snow, or sleet, because that’s what mailmen do, so to speak. Most important, both must be part of the common knowledge:  the professionals “know” that they will be paid no matter what, while the principals “know” that the professionals are doing their utmost, even though the results are not necessarily obvious.  In other words, I don’t know what exactly they are doing, but whatever it is, I know it’s important, dang it.

This is a difficult equilibrium to sustain, with a LOT depending on the players’ beliefs, and potentially open to a lot of abuse and suspicion.  Mike Chwe might say that these beliefs, in turn, would require a lot of cultural trapping to sustain, various rituals carried out to show that the “professionals” indeed are being “professional.”  The “home style” by the legislators whereby they return home and engage in various ritualistic interactions with their voters to show their tribal solidarity might be seen in the same regard.  One might say that a lot of seemingly irrational socio-cultural activities, such as belief in creationism, are exactly that as well.  Of course, this is the kind of equilibrium that IS being subverted by the tilt towards visible data:  as we can see below, the correlation between Democratic shares of House votes and the DW-Nominate scores of the incumbents (with signs adjusted):

correlation

What the graph is showing is that, if you know the voting records of a House member in the preceding session of Congress, you can predict his vote share with increasing accuracy as 20th century progressed.  It does mean that the voters were becoming more “policy-minded,” in the sense of measuring their evaluation of the politicians more on the basis of visible record, but does it mean that the voters were becoming more “rational”?  To claim that would presuppose that the performance of the burger joint depends only on the burger sales and that kitchen is irrelevant to its success. Holstrom (and Max Weber before him) would say in no uncertain terms that that’s stupid.  But what does this mean for the trends in politics today?  I’ve been making a series of argument (and was halfway through a book manuscript) on this very point, but shockingly few people seemed to care, even if, I strongly suspect, the mess of the 2016 elections is a sharp reminder of this problem.

This is an illustration of the potential danger that the data-intensive environment of today is posing us:  because we have so much data, we become contemptuous of the unquantifiable and unaware of the potential limitations of the data that we are using.  If the data is always right, so to speak, i.e. has zero error, there can be no statistics that can be done with it, so to speak.  Then we’d know THE answer.  We do statistics to be less wrong, not necessarily to be “right” (I’m paraphrasing my old stats prof.)  If we insist on mistaking statistics (or indeed “science”) for the “right answer,” woe be upon us.

PS.  One great irony is that, while, intellectually, Paul was one of major influences on my way of thinking, I had precious little interaction with him when I was actually at Stanford. By the time he was teaching his “module,” (Stanford econ reorganized its graduate courses  when I was there so that we had 4 “modules” instead of 3 quarters.  Go figure) I was fairly deep in my occasional depressive spirals and was unable to do practically anything, let alone prepare for prelims.  In a sense, studying for econ prelims is easy–you literally have to study the textbooks and know the formulas, so to speak–just the answers you are supposed to know, even though, admittedly, the questions will be hard.  But depressed people have the hardest time doing routine chores when locked up, figuratively speaking, without anyone talking to them.  It is easy, in a sense, for people who have no stakes to think that depressed people ought to be locked up by themselves until they are better.  In practice, what almost always happens is that, after being locked up for a few months, they will be far more damaged than when they began.  But talking to depressed people requires way too much commitment for people without stakes of their own, too much to be asked of “strangers.”

 

Baseball, Information, and the Brain

This fangraphs article is possibly the most fascinating thing that I had read about neuroscience behind (quick) decision-making, ever.

The problem with seeing the ball out of a pitcher’s hand, obtaining some information, and translating it into reaction is that the information is usually too complex, too wrapped in uncertainty, and the amount of time available is too small.  The article is probably being fair saying that how most batters cannot really describe or explain what it is that they see or how they process the information–it is not really a deliberate “analytical” process, but it is still a reaction that is both learned and “analytical” if in a slightly different sense–of having a fairly small set number of probable reactions, learned through both experience, analysis, and “intinct,” into which a batter can switch into rapidly–a set of mental shortcuts if you will.  A useful analogue might be parties in politics:  there are just two bins, or 4, depending on how one conceptualizes the universe:  there are liberals and conservatives, Democrats and Republicans.  Most politics fit into these categories (or the combination thereof).  If it’s not any of these, the brain will be confused in the short term, and without an obsessive interest in figuring things out–and this kind of interest is rare in politics, especially this requires leaving opinions behind–it is not worth delving into such things too deeply.  So most people operate through two step process:  does it fit the usual boxes of politics, and if it doesn’t, do I care, with the answer to the latter question usually being a big “no.”

The same is true with hitting a baseball, and presumably, with most other activities requiring a quick reaction:  nobody who is any good is probably so simple minded to have just one box, so to speak.  But most people will have just a few boxes, which, thankfully for them, would account for most of the universe.  (The same applies to sabermetrics:  most of these usual boxes will yield predictable results–i.e. high frequency of fly balls probably means the pitcher is not as good as his ERA indicates, for example–the idea behind FIPS)  But if the expectations can somehow be subverted, you can fool the hitters.  While a strange politicians who is not exactly liberal or conservative nor a Democrat or a Republican will confuse the voters and lose elections–becaused confused voters don’t vote–getting batters confused is a useful skill, if you are a pitcher, and all the better if you can confuse the sabermetricians along the way, because, that way, your methods might be so complex that the batters won’t be able to adjust to you easily either.

Theory, Like Fog on Eyeglass….

There are two literary quotes from fictional Chinese characters from early 20th century literature.  The first is from Lee, in Steinbeck’s East of Eden.

“What’s your name?” Samuel asked pleasantly.

“Lee. Got more name. Lee papa family name. Call Lee.”

“I’ve read quite a lot about China. You born in China?”

“No. Born here.”

Samuel was silent for quite a long time while the buggy lurched down the wheel track toward the dusty valley. “Lee,” he said at last, “I mean no disrespect, but I’ve never been able to figure why you people still talk pidgin when an illiterate baboon from the black bogs of Ireland, with a head full of Gaelic and a tongue like a potato, learns to talk a poor grade of English in ten years.”

Lee grinned. “Me talkee Chinese talk,” he said.

“Well, I guess you have your reasons. And it’s not my affair. I hoe you’ll forgive me if I don’t believe it, Lee.”

Lee looked at him and the brown eyes under their rounded upper lids seemed to open and deepen until they weren’t foreign any more, but man’s eyes, warm with understanding. Lee chuckled. “It’s more than a convenience,” he said. “It’s even more than self-protection. Mostly we have to use it to be understood at all.”

Samuel showed no sign of having observed any change. “I can understand the first two,” he said thoughtfully, “but the third escapes me.”

Lee said, “I know it’s hard to believe, but it has happened so often to me and to my friends that we take it for granted. If I should go up to a lady or a gentleman, for instance, and speak as I am doing now, I wouldn’t be understood.”

“Why not?”

“Pidgin they expect, and pidgin they’ll listen to. But English from me they don’t listen to, and so they don’t understand it.”

“Can that be possible? How do I understand you?”

“That’s why I’m talking to you. You are one of the rare people who can separate your observation from your preconception. You see what is, where most people see what they expect.”

“I hadn’t thought of it. And I’ve not been so tested as you, but what you say has a candle of truth. You know, I’m very glad to talk to you. I’ve wanted to ask so many questions.”

“Happy to oblige.”

“So many questions. For instance, you wear the queue. I’ve read that it is a badge of slavery imposed by conquest by the Manchus on the Southern Chinese.”

“That is true.”

“Then why in the name of God do you wear it here, where the Manchus can’t get at you?”

“Talkee Chinese talk. Queue Chinese fashion—you savvy?”

Samuel laughed loudly. “That does have the green touch of convenience,” he said. “I wish I had a hidey-hole like that.”

“I’m wondering whether I can explain,” said Lee. “Where there is no likeness of experience it’s very difficult. I understand you were not born in America.”

“No, in Ireland.”

“And in a few years you can almost disappear; while I, who was born in Grass Valley, went to school and several years to the University of California, have no chance of mixing.”

“If you cut your queue, dressed and talked like other people?”

“No. I tried it. To the so-called whites I was still a Chinese, but an untrustworthy one; and at the same time my Chinese friends steered clear of me. I had to give it up.”

Le pulled up under a tree, got out and unfastened the check rein. “Time for lunch,” he said. “I made a package. Would you like some?”

“Sure I would. Let me get down in the shade there. I forget to eat sometimes and that’s strange because I’m always hungry. I’m interested in what you say. It has a sweet sound of authority. Now it peeks into my mind that you should go back to China.”

Lee smiled satirically at him. “In a few minutes I don’t think you’ll find a loose bar I’ve missed in a lifetime of search. I did go back to China. My father was a fairly successful man. It didn’t work. They said I looked like a foreign devil; they said I spoke like a foreign devil. I made mistakes in manners, and I didn’t know delicacies that had grown up since my father left. They wouldn’t have me. You can believe it or not—I’m less foreign here than I was in China.”

“I’ll have to believe you because it’s reasonable. You’ve given me things to think about until at least February twenty-seventh. Do you mind my questions?”

“As a matter of fact, no. The trouble with pidgin is that you get to thinking in pidgin. I write a great deal to keep my English up. Hearing and reading aren’t the same as speaking and writing.”

“Don’t you ever make a mistake? I mean, break into English?”

“No, I don’t. I think it’s a matter of what is expected. You look at a man’s eyes, you see that he expects pidgin and a shuffle, so you speak pidgin and a shuffle.”

“I guess that’s right,” said Samuel. “In my own way I tell jokes because people come all the way to my place to laugh. I try to be funny for them even when the sadness is on me.”

“But the Irish are said to be a happy people, full of jokes.”

“There’s your pidgin and your queue. They’re not. They’re a dark people with a gift for suffering way past their deserving. It’s said that without whisky to soak and soften the world, they’d kill themselves. But they tell jokes because it’s expected of them.”

Lee unwrapped a little bottle. “Would you like some of this? Chinee drink ng-ka-py.”

“What is it?”

“Chinee blandy. Stlong dlink—as a matter of fact it’s a brandy with a dosage of wormwood. Very powerful. It softens the world.”

Samuel sipped from the bottle. “Tastes a little like rotten apples,” he said.

“Yes, but nice rotten apples. Taste it back along your tongue toward the roots.”

Samuel took a big swallow and tilted his head back. “I see what you mean. That is good.”

“Here are some sandwiches, pickles, cheese, a can of buttermilk.”

“You do well.”

“Yes, I see to it.”

Samuel bit into a sandwich. “I was shuffling over half a hundred questions. What you said brings the brightest one up. You don’t mind?”

“Not at all. The only thing I do want to ask of you is not to talk this way when other people are listening. It would only confuse them and they wouldn’t believe it anyway.”

“I’ll try,” said Samuel. “If I slip, just remember that I’m a comical genius. It’s hard to split a man down the middle and always to reach for the same half.”

“I think I can guess what your next question is.”

“What?”

“Why am I content to be a servant?”

“How in the world did you know?”

“It seemed to follow.”

“Do you resent the question?”

“Not from you. There are no ugly question except those clothed in condescension. I don’t know where being a servant came into disrepute. It is the refuge of a philosopher, the food of the lazy, and properly carried out, it is a position of power, even of love. I can’t understand why more intelligent people don’t take it as a career—learn to do it well and reap its benefits. A good servant has absolute security, not because of his master’s kindness, but because of habit and indolence. It’s a hard thing for a man to change spices or lay out his own socks. He’ll keep a bad servant rather than change. But a good servant, and I am an excellent one, can completely control his master, tell him what to think, how to act, whom to marry, when to divorce, reduce him to terror as a discipline, or distribute happiness to him, and finally be mentioned in his will. If I had wished I could have robbed, stripped, and beaten anyone I’ve worked for and come away with thanks. Finally, in my circumstances I am unprotected. My master will defend me, protect me. You have to work and worry. I work less and worry less. And I am a good servant. A bad one does no work and does no worrying, and he still is fed, clothed, and protected. I don’t know any profession where the field is so cluttered with incompetents and where excellence is so rare.”

Samuel leaned toward him, listening intently.

Lee went on, “It’s going to be a relief after that to go back to pidgin.”

“It’s a very short distance to (your) place. Why did we stop so near?” Samuel asked.

“Allee time talkee. Me Chinee number one boy. You leddy go now?”

“What? Oh, sure. But it must be a lonely life.”

“That’s the only fault with it,” said Lee. “I’ve been thinking of going to San Francisco and starting a little business.”

“Like a laundry? Or a grocery store?”

“No. Too many Chinese laundries and restaurants. I thought perhaps a bookstore. I’d like that, and the competition wouldn’t be too great. I probably won’t do it though. A servant loses his initiative.”

The other is from Charlie Chan, from a movie, actually (Charlie Chan in Egypt, I believe)

Dr. Anton Racine: You have a theory about this, of course?
Charlie Chan: Theory like mist on eyeglasses – obscures facts.

It took me a while to track down the full extended version of the former to copy and paste. There, Steinbeck is making a twofold observations that are often interlinked, which unfortunately, seems to get missed.  Usually, those who quote Steinbeck focus on how Lee has to speak pidgin to meet the expectations, in order to be understood, and generally segue into a commentary about racism and prejudice.

But the next part of the passage, about the power of the servants, is an extension of the same argument.  People see what they see because they have a “theory” about the world–including what to expect from a Chinese circa 1900.  What fits within this theory is easy to process–thus the Chinese man with pidgin and a queue.  A Chinese man without an accent and a queue, however, does not fit the theory and takes an effort to make sense of–provided that the person in question is willing to exert that effort.  Here, the pidgin story blends into the servant story:  a theory is, in principle, a servant, a tool through which to understand the reality.  But people are often lazy and are unwilling to understand the reality, with its messy variances, beyond the theory (and its means).  So arranging spices, folding laundry, or whatever it is that the servants do, they hold absolute power.  If the reality that the master sees is at odds with the comforts of his theory-servant, he would rather hide behind the servant and become dependent, rather than confront the facts.  Indeed, we’d rather have misty eyeglasses rather than see things straight.

Lee recognizes this, at the end of the passage, how he himself falls to the comforts of being a servant and its predictable universe:   “The trouble with pidgin is that you get to thinking in pidgin. I write a great deal to keep my English up. Hearing and reading aren’t the same as speaking and writing.” and “a servant loses his initiative.”  You need to break out of the routine where everything fits into the theory, or you become the servant of your own theorizing.  Sam Hamilton is a welcome distraction, someone who breaks Lee’s theory of how every white behaves, a “deviation” away from the theory, a welcome bit of variance from the monotonies of mean.  We need occasional bits of variances.

Soggy!

I have to confess that I did not know about the “cruchiness” vs. “sogginess” distinction laid out by British journalist Nico Colchester until now.   Unfortunate, since this captures my idea of variance-centric thinking quite well.

The premise behind variance centric thinking is that the world is soggier than we’d like to believe.  Consider the following scenario where we have some input into a system that generates a distribution of outcomes.

X -> [Black Box] -> Y

We want to learn how this “black box” works by working through the relationship between X and Y.  But, while we’d like to believe that a neat “functional” relationship exists between X and Y, i.e. where every value of X is associated with exactly one value of Y, this is not really true most of the time.  Every value of X is associated with a range of the values of Y, a probability distribution, if you will.  Instead of a neat and “comfortable” formulaic universe where pushing a red button will produce a can of Coke with certainty, we might get a situation where you get a can of Coke only with 75% probability, a ball of yarn with 15%, and a bowl of worms with 10%, or whatever.  Even if you do exactly same thing again, you will probably get different outcomes, and you’d be crazy only if you expect the same thing to happen each time, to turn a well-known saying upside the head.

An article in Computer Weekly from a few years ago, it seems,  arrived at a similar conclusion:  even if you have huge data piles, sometimes, the insights they offer have such large variance that you still don’t know what will happen if you push the red button.  All the that data tells you is that you don’t really know what you will get (exactly, at any rate) if you do press it.  The data about red buttons, if you will, is not sufficiently crunchy in that the relationship between the input and output is not clearly and neatly defined.  Perhaps better to seek out crunchier data where you know what you will get if you do exactly the same thing over and over again.

The problem with the search for crunchier data is that, often, crunchy data does not exist.  Even if some crunchy data might exist at some time (e.g. Christmas rallies in stock markets), human strategic behavior, e.g. arbitrage, quickly wipes them out once they become known.  Sometimes, besides, you have to deal with problems you have, not the problems you wish you had–and red buttons may be all that you have to deal with.

Or, in other words, you need to understand how crunchy or soggy your data is.  This is variance-centric thinking comes in.  Variance tells you how soggy your data is, and how to deal with it once you figure it out.  Maybe you do want to learn its mean, if the data is crunchy enough…but with the proviso that, depending on its crunchiness, the means may not be good enough.  If the data is very soggy, means may not be worth knowing.

Politics and Curiosity.

Dan Kahan, whose work I like a lot, has a fascinating new paper out.

The great advance that Kahan and his coauthors make is to attempt systematically defining and quantifying “curiosity.”  I am not sure if what they are doing is quite right:  enjoying science documentaries, for example, does not mean one is or is not “curious.”  (I’d found some science documentaries to be so pedantic that and assertive of the filmmakers’ own views that they were nearly unwatchable, but good science documentaries point to the facts, then raise questions that follow from them without overtly giving answers, for example).  But a more useful perspective on curiosity comes from how one reacts to an unexpected observation:  a curious person reacts by wondering where the oddity came from and investigating the background thereof; an incurious person starts dismissing the oddity as irrelevant.  The third component of their instrument, the so-called “Information Search Experiment,” however, gets at this angle more directly.

Observe that curiosity is, potentially, at odds with simple scientific knowledge.  On surface of the Earth, the gravitational acceleration is approximately 9.8m/s^2.  There was a physicist  wtih web page dedicated to scientific literacy (that I cannot find!) who had a story about how his lab assistant “discovered” that, under some conditions, the measured gravitational acceleration is much smaller.  While this finding was undoubtedly wrong, there are different approaches with which this could have been dealt with:  the incurious approach is to dismiss it by saying that this simply cannot be, because the right answer is 9.8m/s^2.  The curious approach is to conjecture the consequences that would emerge were the different value of the gravitational acceleration true and investigate whether any one of them also materializes.  The usual approach taken, even by scientifically literate persons, is the former, especially since they know, with very little variance, that the gravitational acceleration has to be 9.8m/s^2.  It is rare to find people who react by taking the latter path, and to the degree that “scientific literacy” means “knowing” that the variance of 9.8m/s^2 being the correct answer is small, it is unsurprising that “scientific literacy” is often actually correlated with closed-mindedness and politically motivated reasoning.  (which Kahan had found in earlier studies)

This does make for an interesting question:  I had mused about why creationism can be a focal point, but the proposition that 1+1 = 3 cannot.  Quite simply, 1+1 = 3 is too settled a question (or rather, ruled out by too-settled consensus) to serve as a focal point, while, for many, evolution is not yet sufficiently settled a question.  To the degree that, on average, social consensus tends to converge to the truth (even if not always the case), overtly false “truisms” cannot serve as focal points indefinitely–even if they might persist far longer than one might expect, precisely because they are so useful as focal points.  But the more accepted truisms are, the more likely that contrary findings–even true ones–are to be dismissed without further question as simply being “abnormal.”  In the example above, the probability that a lab assistant simply made a mistake that led to abnormal finding is simply too high compared to there being an actual discovery.  As such, this is not worth wasting time investigating further, beyond berating the hapless  lab assistant for not knowing what he is supposed to be doing.  However, to the extent that “knowledge” is simply an awareness of the conventions, it systematically underestimates the variance in the reality and discourages curiosity as a waste of time.  This, furthermore, is not without justification as the conventions reflect “established truths” that are very nearly certainly true (i.e. with very little variance.)  When people become too sure of the received wisdom where the true variance is actually quite high, a lot more legitimate discoveries are bound to be tossed out with dismissiveness.(Underestimating variance in the name of the received wisdom is exactly how the great financial meltdowns happen:  to borrow the line from the movie The Big Short, those who defy the conventional wisdom will be ridiculed by being badgered with “are you saying you know more than Alan Greenspan?  Hank Paulson?”  Well, physics progressed because, on some things, some insignificant young man named Albert Einstein knew more than Isaac Newton–before he became the Albert Einstein.  Physicists took the chance that Einstein might actually know more than Newton, rather than dismissing him for his pretensions.  The rest is history.  (NB:  one might say that the structure of physics as a way of thinking probably made this easier:  Einstein was able to show that he might be smarter than Newton because he showed what he did without any obvious mistake using all the proper methodology of physics.  But then, physics established that it is about the right methodology and logic, not about the “results.”  This is, in turn, what bedeviled Galileo:  he might have gotten the answer more right than the contemporary conventional wisdom, in retrospect, in terms of approximating the reality–although he was still more wrong than right overall–but he could not precisely trace the steps that he took to get to his answers because the methodology to do so, quite frankly, did not yet exist–they would be invented by Newton centuries later.)

The real scientific literacy, one might say, should consist of a blend between scientific literacy and curiosity:  knowing where the lack of variance is real and where the lack of variance only reflects the reflected consensus, so to speak.  Is 1+1 =2 really true, or does it seem true because everyone says it is?  I have to confess that I do now know what the best answer to this question is.  On simple questions like 1+1, demonstrating the moving parts may be easy enough.  On more complex questions, it is far easier to simply tell people, “trust us:  X is true because that is true, and we should be trusted because of our fancy credentials that say that we know the truth.”  Perhaps, beyond some level, truth becomes so complex that a clear demonstration of the moving parts may no longer be possible.  If so, this is the only path for even partial “scientific literacy,” especially since simple broad awareness of the social conventions that are approximately right (i.e. right mean, wrong variance) might be more desirable socially than everyone wandering about looking for real answers without finding them.

Unfortunately, this turns “science” back to a question of religion and faith.  Rather than product of scientific investigation doused with suitable amount of skeptical curiosity, “science facts” simply become truisms that are true because “high priests” say so, with the real moving parts consigned to “mysteries of the faith,” with the potential for a great deal of abuse, including the persecution of the heretics, literal or figurative, most of whom may be cranks, but may also include some real insights that happen to deviate from the received wisdom more than it is expected to.  This is, of course, politically motivated reasoning revisited, with the sober implication that we may not be able to separate “politics” and “religion” from “science” easily.

 

Variance vs. Means Centric Statistical Thinking: An Illustration.

I’ve written a lot about means-centric vs. variance-centric statistical thinking.  So what do I mean by it?  In the former, we focus on the ability to “predict” something, at its mean or whatever.  In this sense, variability in the data is a nuisance, even an enemy, something to be minimized.  In the latter, however, we want to know what variables cause the biggest variability in the data.  The variance is not only an essential component, it is the main subject of our thinking.  If we can predictably forecast outcomes, it is not only boring, it is also something that can be manipulated and “pumped,” until it is broken (as per my discussion of how signals in a correlated equilibrium can be abused until it breaks, or indeed, as is the logic behind Stiglitz-Grossman Paradox, which is really just a variation on the same argument).

In the end, really, math that accompanies both approaches turn out to be the same:  in the means centric approach, you identify the predictor variables that help minimize the “errors”; in the latter, you identify the variables that happen to be correlated with the variances–which turn out to be the ones that minimize the “errors.”  This convergent evolution, unfortunately, obscures the fundamental philosophical difference between the two approaches.

An illustration might be found using some data from the 2016 primaries.  Consider the following plot.

trump and dem2012 in white and black

The graph illustrates the support for Trump in primaries as a function of the Democratic voteshare from the 2012 election, with two different types of counties:  whether the county population is above or below 75% white, which is roughly the national average–the red dots indicate the counties with below 75% white population.  The biggest variability in the Trump support can be found in the areas where Romney did well in 2012 (i.e. small Democratic voteshares):  the Republican primary voters in Republican dominated areas with large minority populations did not like Trump, while those from the counties with largely white populations did not have much problem with him.  Yes, it is true that Trump did well in many counties with both large Republican majorities and significant minority populations, but the counties where he performed poorly conditional on large Republican majorities are mostly characterized by large minority populations.  As a predictor, this is terrible:  because the conjunction of large minority population and large Republican majority from 2012 does NOT predict weak support for Trump necessarily–there are too many exceptions for that.  But, the reality is that conjunction of all these variables moving in the same direction does not happen–to pretend that they do so feeds into the conjunction paradox identified by Tversky and Kahneman, in which people think the conjunction of characteristics believed to be correlated with each other, rightly or wrongly, is also the most likely–e.g. “Linda is a bank teller and is active in the feminist movement” rather than “Linda is a bank teller.”  People already prone to believe conjunctions happen with too great a frequency already (which partly accounts for the beauty contest game–how people trying to follow a “narrative” systematically downplay the variance)!

From the variance-centric perspective, the large gap that opens up for Trump’s support in Republican friendly areas with large minority populations is not merely interesting–it IS the point.   It is the variability that we are interested in.  Incidentally, this is why Trump’s support numbers are jumping wildly–his support in many red states (i.e. the South–where Republican electoral dominance and large minority populations coincide) is highly uncertain, leading to what Nate Cohn calls “Trump’s Red State problem,” which, to be fair, should already have been apparent from the primary data already–and the polls that showed Trump’s serious national level unpopularity consistently indicated that he is characterized by particularly low popularity among the Republicans.

The key reason that this cannot be readily translated into a prediction is that we know more than the data itself, or rather, we have a broader context that includes data from elsewhere, in which to place the present data.  As Gelman et al observe, that respondents say that they voted for a particular party in the last election (in a poll) is a significant piece of information known to be highly correlated with their present latent choice, even if we may not entirely trust their response to be accurate or honest.  To insist that this be ignored is foolish–even if it cannot be taken at its face value, especially if it is correlated with a particular variability seen in the data.  To the degree that the reality is inherently complex and uncertain, coming up with a fully accounted for prediction model that can predict everything is, quite literally, in the realm of impossibility.  Much better to adopt a two step approach to learning:  identify the sources of variability, then investigate for the correlates of the variability, with the awareness that variability itself is a random variable–i.e. the variance itself may be correlated with particular variables themselves.  (NB:  homogeneity is an absurd assumption and not really a necessary one, except to make OLS BLUE, sine variance is always variable…)

 

“So you are saying we should…”

This post by Nathan Robinson, whose title I just shamelessly stole, underscores a fundamental problem that always seems to come up when challenging sacred cows in any setting, especially when your stock in trade in the “principle of variance.”

My view on statistics is that the “mean” is right as a statement about the whole, but almost always wrong as a statement about the particular.  In repeated coin tosses, should the coin be fair, heads probably will not outnumber tails and vice versa in the long run.  In every single coin toss, however, either head or tail will definitely outnumber the other.  The general statement about the whole, then, is of only limited utility for the single coin toss:  it provides a clue as to who is more likely to win (by giving us the odds across a large enough sample size), but even if the odds are (truly) strictly even (and the long term data says so), it never tells us that neither will win the next coin toss, so to speak.  In other words, we want to know how wrong, vis-a-vis individual observations, the general statement is and whether these “wrongness” can be generalized across multiple observations.  This, of course, is the definition of variance in non-mathematical lingo and this is the heart of statistics (and science in general)–and lack of understanding of this is how people get lied to by abuse of statistics.

The criticisms of the sort Robinson brings up are precisely what gets people lied to by statistics however:  the statement about a “mean,” so to speak, is not a moral statement of absolute truth, but a descriptive statement about the average state of affairs across a large sample, one that is almost certain to be wrong in individual cases:  I don’t know what we should do about it, but as far as I can tell from the sample that we have, Earth is round and that’s just that.  There is no “so what you are saying.”  So Trump is representing many discontented people whose existence the Democratic insiders are eager to ignore, and this is true, whether one likes them or not.  If they are, on average, rather racist, that too may be true, but it does not change the fact that they are unhappy, there are many of them, and that Trump is channeling their anger, and it may be equally true that, even if Trump is, on average, less popular and less likely to win, if Democrats screw up enough, he might actually win.  (David Byler has an instructive, if somewhat pedantic, commentary on this.)

But, if “science,” even “political science,” as a critical assessment of uncertain facts, has no “so what you are saying,” political rhetoric and advocacy, are all about “so what I am saying.”  When “science” meets “advocacy,” facts give way to “so what I am saying” and that can only be met by “so what you are saying” by those who don’t agree with the point being advocated.  This is, in a nutshell, the Dawkins disease, how, Dawkins and others of his ilk did far more damage to science than they contributed by becoming caricatures that they became.

Econ, and Social Sciences in General, Really…

This is a wonderful characterization of what economics in particular and social sciences in general have to offer the “reality.”  I am especially fond of this wonderful quote that I’d love to steal.

“Or to steal a line from my friend Suresh, the best way to think about what most economists do is as a kind of constrained-maximization poetry. Makes no more sense to ask “is it true” than of a haiku.”

Or, I suppose, this other quote, which I think is a bit more appropos:

“The statement, say, that “queens are most effective when supported by strong bishops” might be reasonable in both domains, but studying its application in the one case will not help at all in applying it in in the other.”

This is, perhaps, not entirely fair.  Economic (and again, social science theoretic) arguments are “true” within the domain defined by their underlying assumptions and are applicable to the “real life” as long as the underlying assumptions constitute reasonable likeness to the reality.  That people take economics seriously is often enough to make economic theories relevant.  As long as everyone treats sunspots as a force that shapes the markets and make their decisions based on that assumption, sunspots do exert a significant influence on how the market works, even if the effect is not so much sunspots themselves shaping the market rather than people’s expectations about the sunspots, which in turn, shape the market.  (I often tell a variant of this story, why astrology is true–unfortunately, people often don’t get the point.)

The problem lies in how to respond when the reality starts contradicting theories.  The response of the cargo cultist is to reject the reality and double down on the theories.  The response of the scientist is to start investigating where the theory has gotten things wrong.  There are far more in the former camp than the latter–and many of the former proudly reside in the land of Rationalia, apparently, mistaking themselves to be “scientists.”

I think the theorizing in social sciences actually hasn’t been all that bad:  they are getting a lot of things right.  But that they does not entitle them to assert that their theories override when they are contradicted by the reality.  That would be the unscientific thing to do.

Formulaic Thinking, Cargo Cults, and “Science” (in its varied guises)

I had earlier wondered if the Cargo Cult can be broken–or, perhaps, even “should be” broken–if the cargo keeps flowing.  The situation is analogous, in a sense, to why there are neither true believers nor atheists in the fox hole, as the saying goes.  Soldiers and sailors do not necessarily know why or how bad or good things happen.  They believe that the world around them is complex enough that no simple “theory” is good enough and that they lack both the time and the wherewithal to come up with sufficiently “good” theories, if it is at all possible.  They also reside in a world where the good and the bad are, literally, a matter of life or death.  They are not sufficiently invested in any theory being right or wrong to risk their life and limb just to learn a bit more of the “the truth.”  So they are superstitious, not necessarily because they don’t know the “science,” (if anything, they are far more aware of the nuances and the “variance” thereof) but because they are not invested in proving any theory right or wrong.  Notice that this logic applies to the “anti-science” as well as to science.  They may take “lucky charms” of various kinds seriously enough, but they don’t trust them so much that they are willing to risk their safety on the chance that the charm is indeed so “lucky.”  Thus, they are just superstitious enough to believe in all manner of totems, but they are not so superstitious as to “trust” them.

In most walks of life, even if the stakes are not nearly as high as those facing soldiers and sailors, the same attitude prevails:  life may be complex in totality, but abiding by simple rules, accepted on the premise that everyday things are the way things usually are and “should be,” is usually good enough.  Following formulas keeps you on the safe side most of the time, while keeping you away from undue risk and headaches because the world does not change so drastically often.  Thus, people are creatures of habit, inherently “conservative” in their worldview, usually unwilling to change their minds quickly without a good reason–but not so wedded to their worldview that they are unwilling to change what they think even in face of a “good enough reason,” without attendant risks.  So even socially conservatively minded people, as long as their contact with transgenders is limited and have no reason to be biased against them, might be willing to rethink their opinion if asked nicely, for example.  The caveat “without attendant risks,” however, looms large here:  can the same approach be used to change people’s opinion about guns?  About Muslims?  Heck, even about a lot of race-related questions?  Transgenders, as a group, are simply “a bit odd” in the minds of many–even those who are predisposed to oppose their way of life.  They lack “good enough reason” to oppose them.  Hostility to guns and Muslims, however, belongs to a different plane.  The beliefs may or may not be justified on factual grounds, but there is a widespread perception of physical danger and direct harm that they pose, even with a small probability.  There is a “good reason” that people may persist in their belief in face of attempts to convince them otherwise.  For comparison, one might say that it is easy for a scientist to convince a sailor to start using compass (or, perhaps, to start wearing a red shirt, if the former can convince the latter, rightly or wrongly, the red shirt helps him stay safe) but not to convince him to stop carrying a parrot, if the sailor is convinced that the parrot keeps him away from shoals.  Starting to use the compass (or wear a red shirt) seemingly imposes little cost but promises a chance of potentially large benefits.  It’s a lottery ticket worth buying.  But people will not take up what they consider a big risk without due compensation, at the very least.  Taking away a sailor’s parrot offers him nothing.

To elaborate further on the point I was raising the other day, then, “scientific progress” is an inherently risky process.  “Science” demands that those who have been following a well-established set of routines to stop following them and start introducing variations, just to see what happens.  But every routine is characterized by a belief that it “works,” that following it brings considerable benefits and that not doing so is quite costly.  If the Aztecs reap the benefits of sunlight–huge, obviously–in return for sacrificing conquered subjects, which, for Aztecs is very easy, thanks to their warlike nature, why would they want to risk the world without sunlight for the trivial gain like making nice with the pathetic Tlaxcalans?  Existing mindset–the “culture” or “affect,” depending on whether you were trained in anthropology or political psychology–shapes how people value the consequences of the roads not taken, of the routines being broken.  They are never wrong because there is no evidence to say otherwise, because those paths are not taken.  All data comes from the paths that were taken, and naturally, offer justification for the the broad status quo, except perhaps for incremental “improvements” that may or may not be justified–perhaps cutting off Tlaxcalans heads before cutting out their hearts would make the sun rise faster, or not….  If the Tlaxcalans were not so easy to capture for human sacrifice, maybe things would be different, or not.  After all, ensuring that the sun keeps rising is a hugely important thing.  How do you know if the sun will rise again without the blood flowing?  That might be too huge a risk to take…especially since there is, by definition, NO evidence whatsoever to back it up.  The only thing that enables this leap to be taken is, literally, one of faith, justified by no evidence but a set of contrarian beliefs, as per Kierkegaard’s argument (I think I’m linking to the right book…)..

This leads to a curious paradox, in which “science” and “technology” often wind up being at odds with each other.  While “technology” might depend on “scientific” understanding, it rests on the acceptance of the status quo and the need for incremental improvement of the formulas.  It does not question the validity of everyday things or raise awkward question.  It simply says, yes, the formulas are inherently right, but we can add this one tweak and we can do better.  This was literally being done, to keep up the Ptolemaic astronomy in the Middle Ages:  an extensive system of “tweaks,” in form of epicycles, were added to the basic Ptolemaic formula to keep the basic structure intact.  The skepticism undergirding “science” does not, however, accept the status quo as given.  The formulas are not “inherently right,” but only provisionally so.  To learn where and when the formulas are not, some crazy risks, potentially with big repercussions, need to be taken contrary to things that “everyone knows” to be “obviously” true.   What’s worse, the skepticism yields no obvious short term benefits:  while Copernican theory made the calculations vastly easier, by reducing the number of epicycles that were required, the basic structure was still fundamentally “wrong” in both logical and empirical sense.  It took centuries of additional refinement to get to the classical physics as we understand it, and, other than the computational ease, there was no “good” reason to take Copernicus seriously when his book was published.  As long as the argument was not offensive, however, there was no good reason to overtly “reject” it, much the way social conservatives who partook in the Brockman/Kalla study found little reason to persist in their hostility towards transgenders.  Indeed, without the controversies wrapped up in the Reformation and papal politics, Copernican science might have won over by osmosis anyways.  But, politics happen and Galileo was a prickly and arrogant blowhard who stepped on many toes.

This paradox flies in the face of the popular understanding of “science.”  What people take to be “science” is in fact technology.  Yet, with enough epicycles, you can make creationism compatible with vaccines, oil deposits, and even fossils, at least for the common audience.  From the perspective of the sailor, the question becomes why he can’t keep both the parrot and the compass, and the argument against the parrot is not particularly convincing, given the potential “risks” involved.  The truth is that there is precious little argument, at least in the short term, for “science.”  “Science” will not make us happier or wealthier.  It may not even make us “wiser” until much later than we’d like. So why should we give up our formulas for them, especially if its practitioners are being a collective ass?  Given the proclivity of the social sciences to butt in on controversies of the day, coupled with the far larger uncertainties inherent in topics of research among social sciences, this is especially a pertinent question for them.

Much the same argument prevails in the public policy realm as well:  people are willing to partake in, essentially, a superstitious activity in face of what appears to be a very real risk–against immigration, Muslims, EU, etc.  Are these irrational and foolish?  Perhaps.  But what assurances can you offer against the perceived risks, other than ridicule those who fear them for fearing them in the first place and call them names?  That can only ensure that the argument against fear, already imperiled because of the very real presence of the fear–even if the object of that fear may not be as real as it is deemed–will be rejected with certainty:  not only are people afraid, they are forced to deal with those who are at best uncaring, callous, and oblivious, and at worst, actively seeking to prey on them. If people are behaving formulaically, they often do so for a consistent, even if not always logical, set of reasons.  They can be approached by better understanding where their formulas come from and what sustains them–although success may not always be guaranteed, as per Aztecs and human sacrifice.  It is foolish to believe that they can be simply supplanted by hectoring and ridiculing them.  (Ironically, of course, the same argument would apply to those on the opposite side–as much as Sanders and Trump supporters, in their respective camps, are “odd” and subscribe to formulas that seem “strange,” the supporters of the conventional wisdom also subscribe to various formulas that do not always have a logical underpinning other than they “work” empirically–see this essay for a further exposition.)  Perhaps, if they cannot be dealt with peacefully, they can be consquistadored, like the Aztecs, and converted at the sword point.  But that too is a serious undertaking.  No matter what the recourse, this is a challenge that needs to be taken seriously, which very few seem eager to partake in.