StreetEYE Blog

Christmas Story, by H. L. Mencken

THIS STORY was first printed in the New Yorker, to whose editors I am indebted for permission to republish it. In writing it I had valuable suggestions from my brother, August Mencken.

H.L.M

CHRISTMAS STORY

Despite all the snorting against them in works of divinity, it has always been my experience that infidels— or free-thinkers, as they usually prefer to call themselves— are a generally estimable class of men, with strong overtones of the benevolent and even of the sentimental. This was certainly true, for example, of Leopold Bortsch, Totsaufer (1) for the Scharnhorst Brewery, in Baltimore, forty-five years ago, whose story I have told, alas only piecemeal, in various previous communications to the press. If you want a bird’s-eye view of his character, you can do no better than turn to the famous specifications for an ideal bishop in I Timothy III, 2-6. So far as I know, no bishop now in practice on earth meets those specifications precisely, and more than one whom I could mention falls short of them by miles, but Leopold qualified under at least eleven of the sixteen counts, and under some of them he really shone. He was extremely liberal ( at least with the brewery’s money ) , he had only one wife ( a natural blonde weighing a hundred and eighty-five pounds ) and treated her with great humanity, he was ( I quote the text ) “no striker . . . not a brawler,” and he was preeminently ‘Vigilant, sober, of good behavior, given to hospitality, apt to teach.” Not once in the days I knew and admired him, c. 1900, did he ever show anything remotely resembling a bellicose and rowdy spirit, not even against the primeval Prohibitionists of the age, the Lutheran pastors who so often plastered him from the pulpit, or the saloonkeepers who refused to lay in Scharnhorst beer. He was a sincere friend to the orphans, the aged, all blind and one-legged men, ruined girls, opium fiends, Chinamen, oyster dredgers, ex-convicts, the more respectable sort of colored people, and all the other oppressed and unfortunate classes of the time, and he slipped them, first and last, many a substantial piece of money.

(1) A Totsaufer (literally, dead-drinker) is a brewery’s customers’ man. One of his most important duties is to carry on in a wild and inconsolable manner at the funerals of saloonkeepers.

Nor was he the only Baltimore infidel of those days who thus shamed the churchly. Indeed, the name of one of his buddies, Fred Ammermeyer, jumps into my memory at once. Fred and Leopold, I gathered, had serious dogmatic differences, for there are as many variations in doctrine between infidels as between Christians, but the essential benignity of both men kept them on amicable terms, and they often cooperated in good works. The only noticeable difference between them was that Fred usually tried to sneak a little propaganda into his operations— a dodge that the more scrupulous Leopold was careful to avoid. Thus, when a call went out for Bibles for the paupers lodged in Bayview, the Baltimore almshouse, Fred responded under an assumed name with a gross that had to be scrapped at once, for he had marked all the more antinomian passages with a red, indelible pencil— for example, Proverbs VII, 18-19; Luke XII, 19; I Timothy V, 23; and the account of David’s dealing with Uriah in II Samuel XI.

Again, he once hired Charlie Metcalfe, a small-time candy manufacturer, to prepare a special pack of chocolate drops for orphans and ruined girls with a deceptive portrait of Admiral Dewey on the cover and a print of Bob Ingersoll’s harangue over his brother’s remains at the bottom of each box. Fred had this subversive exequium reprinted many times, and distributed at least two hundred and fifty thousand copies in Baltimore between 1895 and 1900. There were some Sunday-school scholars who received, by one device or another, at least a dozen. As for the clergy of the town, he sent each and every one of them a copy of Paine’s “Age of Reason” three or four times a year— always disguised as a special-delivery or registered letter marked “Urgent.” Finally, he employed seedy rabble rousers to mount soap boxes at downtown street corners on Saturday nights and there bombard the assembled loafers, peddlers, and cops with speeches which began seductively as excoriations of the Interests and then proceeded inch by inch to horrifying proofs that there was no hell.

But in the masterpiece of Fred Ammermeyer’s benevolent career there was no such attempt at direct missionarying; indeed, his main idea when he conceived it was to hold up to scorn and contumely, by the force of mere contrast, the crude missionarying of his theological opponents.

This idea seized him one evening when he dropped into the Central Police Station to pass the time of day with an old friend, a police lieutenant who was then the only known freethinker on the Baltimore force. Christmas was approaching and the lieutenant was in an unhappy and rebellious frame of mind— not because he objected to its orgies as such, or because he sought to deny Christians its beautiful consolations, but simply and solely because he always had the job of keeping order at the annual free dinner by the massed missions of the town to the derelicts of the waterfront, and that duty compelled him to listen politely to a long string of pious exhortations, many of them from persons he knew to be whited sepulchres.

“Why in hell,” he observed impatiently, “do all them goddam hypocrites keep the poor bums waiting for two, three hours while they get off their goddam whim wham? Here is a hall full of men who ain’t had nothing to speak of to eat for maybe three, four days, and yet they have to set there smelling the turkey and the coffee while ten, fifteen Sunday-school superintendents and W.C.T.U. sisters sing hymns to them and holler against booze. I tell you, Mr. Ammermeyer, it ain’t human. More than once I have saw a whole row of them poor bums pass out in faints, and had to send them away in the wagon. And then, when the chow is circulated at last, and they begin fighting for the turkey bones, they ain’t hardly got the stuff down before the superintendents and the sisters begin calling on them to stand up and confess whatever skulduggery they have done in the past, whether they really done it or not, with us cops standing all around. And every man Jack of them knows that if they don’t lay it on plenty thick there won’t be no encore of the giblets and stuffing, and two times out of three there ain’t no encore anyhow, for them psalm singers are the stingiest outfit outside hell and never give a starving bum enough solid feed to last him until Christmas Monday. And not a damned drop to drink! Nothing but coffee— and without no milk! I tell you, Mr. Ammermeyer, it makes a man’s blood boil.”

Fred’s duly boiled, and to immediate effect. By noon the next day he had rented the largest hall on the water-front and sent word to the newspapers that arrangements for a Christmas party for bums to end all Christmas parties for bums were under way. His plan for it was extremely simple. The first obligation of hospitality, he announced somewhat prissily, was to find out precisely what one’s guests wanted, and the second was to give it to them with a free and even reckless hand. As for what his proposed guests wanted, he had no shade of doubt, for he was a man of worldly experience and he had also, of course, the advice of his friend the lieutenant, a recognized expert in the psychology of the abandoned.

First and foremost, they wanted as much malt liquor as they would buy themselves if they had the means to buy it. Second, they wanted a dinner that went on in rhythmic waves, all day and all night, until the hungriest and hollowest bum was reduced to breathing with not more than one cylinder of one lung. Third, they wanted not a mere sufficiency but a riotous superfluity of the best five-cent cigars on sale on the Baltimore wharves. Fourth, they wanted continuous entertainment, both theatrical and musical, of a sort in consonance with their natural tastes and their station in life. Fifth and last, they wanted complete freedom from evangelical harassment of whatever sort, before, during, and after the secular ceremonies.

On this last point, Fred laid special stress, and every city editor in Baltimore had to hear him expound it in person. I was one of those city editors, and I well recall his great earnestness, amounting almost to moral indignation. It was an unendurable outrage, he argued, to invite a poor man to a free meal and then make him wait for it while he was battered with criticism of his ways, however well intended. And it was an even greater outrage to call upon him to stand up in public and confess to all the false steps of what may have been a long and much troubled life.

Fred was determined, he said, to give a party that would be devoid of all the blemishes of the similar parties staged by the Salvation Army, the mission helpers, and other such nefarious outfits. If it cost him his last cent, he would give the bums of Baltimore massive and unforgettable proof that philanthropy was by no means a monopoly of gospel sharks— that its highest development, in truth, was to be found among freethinkers.

It might have cost him his last cent if he had gone it alone, for he was by no means a man of wealth, but his announcement had hardly got out before he was swamped with offers of help. Leopold Bortsch pledged twenty-five barrels of Scharnhorst beer and every other Totsdufer in Baltimore rushed up to match him. The Baltimore agents of the Pennsylvania two-fer factories fought for the privilege of contributing the cigars. The poultry dealers of Lexington, Fells Point, and Cross Street markets threw in barrel after barrel of dressed turkeys, some of them in very fair condition. The members of the boss bakers’ association, not a few of them freethinkers themselves, promised all the bread, none more than two days old, that all the bums of the Chesapeake littoral could eat, and the public-relations counsel of the Celery Trust, the Cranberry Trust, the Sauerkraut Trust, and a dozen other such cartels and combinations leaped at the chance to serve.

If Fred had to fork up cash for any part of the chow, it must have been for the pepper and salt alone. Even the ketchup was contributed by social-minded members of the Maryland canners’ association, and with it they threw in a dozen cases of dill pickles, chowchow, mustard, and mincemeat. But the rent of the hall had to be paid, and not only paid but paid in advance, for the owner thereof was a Methodist deacon, and there were many other expenses of considerable size— for example, for the entertainment, the music, the waiters and bartenders, and the mistletoe and immortelles which decorated the hall. Fred, if he had desired, might have got the free services of whole herds of amateur musicians and elocutionists, but he swept them aside disdainfully, for he was determined to give his guests a strictly professional show. The fact that a burlesque company starved out in the Deep South was currently stranded in Baltimore helped him here, for its members were glad to take an engagement at an inside rate, but the musicians’ union, as usual, refused to let art or philanthropy shake its principles, and Fred had to pay six of its members the then prevailing scale of four dollars for their first eight hours of work and fifty cents an hour for overtime. He got, of course, some contributions in cash from rich freethinkers, but when the smoke cleared away at last and he totted up his books, he found that the party had set him back more than a hundred and seventy-five dollars.

Admission to it was by invitation only, and the guests were selected with a critical and bilious eye by the police lieutenant. No bum who had ever been known to do any honest work— even such light work as sweeping out a saloon— was on the list. By Fred’s express and oft-repeated command it was made up wholly of men completely lost to human decency, in whose favor nothing whatsoever could be said. The doors opened at 11 a.m. of Christmas Day, and the first canto of the dinner began instantly. There were none of the usual preliminaries- no opening prayer, no singing of a hymn, no remarks by Fred himself, not even a fanfare by the band. The bums simply shuffled and shoved their way to the tables and simultaneously the waiters and sommeliers poured in with the chow and the malt. For half an hour no sound was heard save the rattle of crockery, the chomp- chomp of mastication, and the grateful grunts and “Oh, boy!”s of the assembled underprivileged.

Then the cigars were passed round (not one but half a dozen to every man ) , the band cut loose with the tonic chord of G major, and the burlesque company plunged into Act I, Sc. 1 of “Krausmeyer’s Alley.” There were in those days, as old-timers will recall, no less than five standard versions of this classic, ranging in refinement all the way from one so tony that it might have been put on at the Union Theological Seminary down to one so rowdy that it was fit only for audiences of policemen, bums, newspaper reporters, and medical students. This last was called the Cincinnati version, because Cincinnati was then the only great American city whose mores tolerated it. Fred gave instructions that it was to be played a outrance and con fuoco, with no salvo of slapsticks, however brutal, omitted, and no double entendre, however daring. Let the boys have it, he instructed the chief comedian, Larry Snodgrass, straight in the eye and direct from the wood. They were poor men and full of sorrow, and he wanted to give them, on at least one red-letter day, a horse doctor’s dose of the kind of humor they really liked.

In that remote era the girls of the company could add but little to the exhilarating grossness of the performance, for the strip tease was not yet invented and even the shimmy was still only nascent, but they did the best they could with the muscle dancing launched by Little Egypt at the Chicago World’s Fair, and that best was not to be sneezed at, for they were all in hearty sympathy with Fred’s agenda, and furthermore, they cherished the usual hope of stage folk that Charles Frohman or Abe Erlanger might be in the audience. Fred had demanded that they all appear in red tights, but there were not enough red tights in hand to outfit more than half of them, so Larry Snodgrass conceived the bold idea of sending on the rest with bare legs. It was a revolutionary indelicacy, and for a startled moment or two the police lieutenant wondered whether he was not bound by his Hippocratic oath to raid the show, but when he saw the whole audience leap up and break into cheers, his dubieties vanished, and five minutes later he was roaring himself when Larry and the other comedians began paddling the girls’ cabooses with slapsticks.

I have seen many a magnificent performance of “Krausmeyer’s Alley” in my time, including a Byzantine version called “Krausmeyer’s Dispensary,” staged by the students at the Johns Hopkins Medical School, but never have I seen a better one. Larry and his colleagues simply gave their all. Wherever, on ordinary occasions, there would have been a laugh, they evoked a roar, and where there would have been roars they produced something akin to asphyxia and apoplexy. Even the members of the musicians’ union were forced more than once to lay down their fiddles and cornets and bust into laughter. In fact, they enjoyed the show so vastly that when the comedians retired for breath and the girls came out to sing “Sweet Rosie O’Grady” or “I’ve Been Workin’ on the Railroad,” the accompaniment was full of all the outlaw glissandi and sforzandi that we now associate with jazz.

The show continued at high tempo until 2 p.m., when Fred shut it down to give his guests a chance to eat the second canto of their dinner. It was a duplicate of the first in every detail, with second and third helpings of turkey, sauerkraut, mashed potatoes, and celery for everyone who called for them, and a pitcher of beer in front of each guest. The boys ground away at it for an hour, and then lit fresh cigars and leaned back comfortably for the second part of the show. It was still basically “Krausmeyer’s Alley,” but it was a “Krausmeyer’s Alley” adorned and bedizened with reminiscences of every other burlesque- show curtain raiser and afterpiece in the repertory. It went on and on for four solid hours, with Larry and his pals bending themselves to their utmost exertions, and the girls shaking their legs in almost frantic abandon. At the end of an hour the members of the musicians’ union demanded a cut-in on the beer and got it, and immediately afterward the sommeliers began passing pitchers to the performers on the stage. Meanwhile, the pitchers on the tables of the guests were kept replenished, cigars were passed round at short intervals, and the waiters came in with pretzels, potato chips, celery, radishes, and chipped beef to stay the stomachs of those accustomed to the free-lunch way of life.

At 7 p.m. precisely, Fred gave the signal for a hiatus in the entertainment, and the waiters rushed in with the third canto of the dinner. The supply of roast turkey, though it had been enormous, was beginning to show signs of wear by this time, but Fred had in reserve twenty hams and forty pork shoulders, the contribution of George Wienefeldter, president of the Wienefeldter Bros. & Schmidt Sanitary Packing Co., Inc. Also, he had a mine of reserve sauerkraut hidden down under the stage, and soon it was in free and copious circulation and the guests were taking heroic hacks at it. This time they finished in three-quarters of an hour, but Fred filled the time until 8 p.m. by ordering a seventh-inning stretch and by having the police lieutenant go to the stage and assure all hands that any bona-fide participant found on the streets, at the conclusion of the exercises, with his transmission jammed would not be clubbed and jugged, as was the Baltimore custom at the time, but returned to the hall to sleep it off on the floor. This announcement made a favorable impression, and the brethren settled down for the resumption of the show in a very pleasant mood. Larry and his associates were pretty well fagged out by now, for the sort of acting demanded by the burlesque profession is very fatiguing, but you’d never have guessed it by watching them work.

At ten the show stopped again, and there began what Fred described as a Bierabend, that is, a beer evening. Extra pitchers were put on every table, more cigars were handed about, and the waiters spread a substantial lunch of rye bread, rat-trap cheese, ham, bologna, potato salad, liver pudding, and Blutwurst. Fred announced from the stage that the performers needed a rest and would not be called upon again until twelve o’clock, when a midnight show would begin, but that in the interval any guest or guests with a tendency to song might step up and show his or their stuff. No less than a dozen volunteers at once went forward, but Fred had the happy thought of beginning with a quartet, and so all save the first four were asked to wait. The four laid their heads together, the band played the vamp of “Sweet Adeline,” and they were off. It was not such singing as one hears from the Harvard Glee Club or the Bach Choir at Bethlehem, Pennsylvania, but it was at least as good as the barbershop stuff that hillbillies now emit over the radio. The other guests applauded politely, and the quartet, operating briskly under malt and hop power, proceeded to “Don’t You Hear Dem Bells?” and “Aunt Dinah’s Quilting Party.” Then the four singers had a nose-to-nose palaver and the first tenor proceeded somewhat shakily to a conference with Otto Strauss, the leader of the orchestra.

From where I sat, at the back of the hall, beside Fred, I could see Otto shake his head, but the tenor persisted in whatever he was saying, and after a moment Otto shrugged resignedly and the members of the quartet again took their stances. Fred leaned forward eagerly, curious to hear what their next selection would be. He found out at once. It was “Are You Ready for the Judgment Day?,” the prime favorite of the period in all the sailors’ bethels, helping-up missions, Salvation Army bum traps, and other such joints along the waterfront. Fred’s horror and amazement and sense of insult were so vast that he was completely speechless, and all I heard out of him while the singing went on was a series of sepulchral groans. The man was plainly suffering cruelly, but what could I do? What, indeed, could anyone do? For the quartet had barely got halfway through the first stanza of the composition before the whole audience joined in. And it joined in with even heartier enthusiasm when the boys on the stage proceeded to “Showers of Blessings,” the No. 2 favorite of all seasoned mission stiffs, and then to “Throw Out the Lifeline,” and then to “Where Shall We Spend Eternity?,” and then to “Wash Me, and I Shall Be Whiter Than Snow.”

Halfway along in this orgy of hymnody, the police lieutenant took Fred by the arm and led him out into the cold, stinging, corpse-reviving air of a Baltimore winter night. The bums, at this stage, were beating time on the tables with their beer glasses and tears were trickling down their noses. Otto and his band knew none of the hymns, so their accompaniment became sketchier and sketchier, and presently they shut down altogether. By this time the members of the quartet began to be winded, and soon there was a halt. In the ensuing silence there arose a quavering, boozy, sclerotic voice from the floor. “Friends,” it began, “I just want to tell you what these good people have done for me— how their prayers have saved a sinner who seemed past all redemption. Friends, I had a good mother, and I was brought up under the in- fluence of the Word. But in my young manhood my sainted mother was called to heaven, my poor father took to rum and opium, and I was led by the devil into the hands of wicked men— yes, and wicked women, too. Oh, what a shameful story I have to tell! It would shock you to hear it, even if I told you only half of it. I let myself be . . .”

I waited for no more, but slunk into the night. Fred and the police lieutenant had both vanished, and I didn’t see Fred again’ for a week. But the next day I encountered the lieutenant on the street, and he hailed me sadly.

“Well,” he said, “what could you expect from them bums? It was the force of habit, that’s what it was. They have been eating mission handouts so long they can’t help it. Whenever they smell coffee, they begin to confess. Think of all that good food wasted! And all that beer! And all them cigars!”

On ‘fake news’, market designs, and the fascist/libertarian nexus

Arbitrary power is most easily established on the ruins of liberty abused to licentiousness. – George Washington

To suppose that any form of government will secure liberty or happiness without any virtue in the people, is a chimerical idea. – James Madison

Reportedly, a CNN screenshot?

Reportedly, a CNN screenshot?

It seems richly ironic that the utopian, nominally libertarian visionaries of Silicon Valley, the folks of the Whole Earth Catalog and Think Different, in creating a many-to-many Internet platform where everyone can communicate with everyone via social media, have provided the tools for mass manipulation, mass surveillance, and mob rule on a mass scale.

Social media is turning into a cesspool of tar-and-pitchfork mobs, hate, false but truthy viral memes, spam, and lame celebrity and corporate shilling.

Surely we can agree that it would be a bad thing for democracy if a demagogue, through effective control of mass media, were to persuade people he won an election when he didn’t, and that people who say otherwise are unreliable and that only he can solve the country’s problems, etc., etc.?

Is that actually happening? Seems a question of degree. If you post that Clinton won the popular vote, some moron will come back to say she didn’t really because of illegals, or uncounted absentee ballots, or some other crazy argument that boils down to, “we won all the votes if you don’t count the people who voted for her, who shouldn’t count anyway.”

Which sounds like a great excuse for further disenfranchisement, convenient purges of the voter rolls, voter ID laws. “Always accuse your adversary of whatever it is you yourself are attempting.”

Which sounds like an un-American disrespect for our democracy, and an excuse for further cynicism, and decay.

Then, if people want to take common sense steps to prevent the spread of lies, people cry media manipulation.

If someone wants to be a troll and write that Trump won the popular vote, I certainly don’t advocate censoring them or sending them to jail. If someone wants to pay Facebook to run an ad pointing to a site writing similar nonsense, that’s also fine, as long as it’s obvious it’s an ad and Facebook is not presenting it as journalism.

However, once a lot of people start sharing that nonsense as news and Facebook presents it as news, that’s a real problem. Facebook is presenting false and misleading fiction as news adhering to journalism school norms.

At that point Facebook is misleading people. Facebook should let people flag it as false. Their tools to do that are shite, protestations notwithstanding. And if something is consistently flagged as false, and someone is sharing it as news, Facebook should show a disclaimer, ‘the accuracy of this item/site has been disputed, do you want to learn more before you share?’ with stats on who and how many people are flagging it, and the links to evidence they cite. A little nudge goes a long way with most decent people. And if people still want to share it, then fine. I’m not advocating Chinese style memory-holing Tienanmen Square here. Just don’t present conspiracy theories, wingnut fiction, and blatantly manipulative propaganda in context as real news.

Commingling false propaganda with news diminishes the credibility of all news media, it reduces the value of Facebook’s platform, and it’s malware infecting the operating system of democracy.

If you want to believe nonsense, like that Trump won the popular vote or that the moon landing was faked and it’s made of green cheese, it’s your own problem. But if everyone believes nonsense, it’s a sick society. Mark Zuckerberg and Facebook need to decide if the ad money, engagement, and sidestepping criticism are worth poisoning the well.

There is no valid argument against a minimal news filter for fraud and manipulation. Is it OK that people searching for election results get as a the top result a fake page showing Trump won the popular vote, and Google should just let it slide? We take it for granted that Google should care about search quality.

It’s impossible to avoid applying a minimal filter to avoid spam, illegal or offensive content. Surely blatantly false and misleading content shouldn’t be given a pass, while napalm children are blocked.

Facebook already optimizes for engagement, they need to tune it a bit for anti-spam.

The objections boil down to, the only truth that matters is, if Facebook were to stop presenting nonsense under the rubric of news, it would hurt one party more than another. Truth is what serves the party line and the Great Leader.

The argument that it’s a slippery slope to Stalinist media control is a fallacy. The alternative to simple common sense, not calling demonstrably false things news, looks exactly like Pravda. (And I don’t think it’s a coincidence.) And if you follow the slippery slope argument, you can never do anything to improve markets, or help a starving person, because it’s a slippery slope to socialism, communism and dependence.

The good-faith argument that we should err on the side of freedom of speech is a good instinct. But you can’t allow lies to drown out truth, and dark, extreme forces and foreign actors to manipulate narratives so easily.

A problem I have with libertarian arguments in general is that they take free markets as given, when in fact they are extraordinarily complex institutions which depend on norms and laws enforced by government.

Before you can have a free market, you need a market design that works. And this applies to the marketplace for ideas as well as the free market for goods and services. (And it’s a key function of a news aggregator like StreetEYE.)

Otherwise, the libertarian argument boils down to, “I’ve got the money, I’ve got the power, I know how to game the system, eff you and your superior attitude and your ‘fairness’ and your market design, I’m going to impose the market design that benefits me.”

When really the libertarian argument should be, how do we create a market design with a minimum of rules and arbitrary government intervention that achieves the objectives of the market, where government can’t abuse its power, and where bad-faith actors and big market players can’t abuse their power.

Which is extremely hard. And takes a complex understanding of government, markets, and the true meaning of liberty as maximum freedom for responsible actors. And willingness to do the hard work of constantly improving markets and rules, instead of throwing up hands and obstructing the people willing to do the work.

And if taken too far, the attitude that laws are always the problem, not the solution, becomes a disease that makes the republic go down the drain, instead of the cure.

Without responsible action, libertarianism sows the seeds of fascism, and the greatest communication tools ever invented become tools to spread the greatest lies ever invented, and eventually the greatest tools for repression.

Decent people should realize that a society where any lie can be the truth, isn’t a society that can lead the world, or one worth having.

Everyone lives in a bubble, and all models are overfitted

I beseech you, in the bowels of Christ, think it possible you may be mistaken. – Oliver Cromwell

Real knowledge is to know the extent of one’s ignorance. – Confucius

So, a lot of people are saying the media/elite got the election wrong because they live in a ‘bubble’.

And some are saying the pollsters and forecasters were all wrong and ‘data science’ is bullshit.

Well, this guy models, sometimes, and if you say that, you’re all as full of shit as the pundits and forecasters, and in most cases far more so.

Sure…media, analysts, Silicon Valley live in a bubble…unlike rural farmers who take time out of each day to consult a broad cross-section of Americans. (via this guy)

Sure…Nate Silver is full of BS…but an unemployed coal-miner who thinks Obama’s birth certificate is fake and Trump is going to build a wall and bring back his job is keeping it real.

The only way anyone can make sense of reality is by filtering it. We all create our own bubbles.

The only reality anyone knows is socially constructed, and subject to our own heuristics and behavioral biases.

Any sufficiently powerful model will overfit to past (in-sample) data, compared to the future (out-of-sample). The only way to prevent that is to pad the error bars for what you don’t know you don’t know, and build in a bias toward uncertainty.

What forecasters do is fight the curse of dimensionality and try to find the bias/variance sweet spot.

Fighting dimensionality means trying to explain a lot of variables with a few, finding simple patterns in complex data. The problem is, reality is incredibly messy, and the messier it is, the easier it is to find spurious patterns.

You can have an ultra-simple model, like a simple linear regression, and it may not fit the data very well, because the underlying reality is not linear, or because multiple predictors impact the response.

These models are biased in the sense that they start from an opinion about the data, a linear response to a single predictor, that is not true in reality.

So then you add variables and relax the linearity assumption, and if you do a good job your model starts to fit the in-sample data well and predicts the future a little better.

But if you add enough complexity to your model, you start to fit the quirks in your data too well, and your out-of-sample prediction gets worse. Instead of being overly wedded to the bias of your a priori model, you are overly sensitive to random variance in the data you happen to have encountered.

The tradeoff looks like this, via Scott Fortmann-Roe:

biasvariance

The data scientist is looking for that trough in the black line, the right balance between underfitting and overfitting, and trying to understand reality as well as possible to make the trough as deep as possible.

One thing data scientists do is divide data into training and test sets. You fit a model on the training set and then use the test set to measure the training error. Now maybe you go back and try a bunch of models and see which one performs best. Well, guess what, now you’ve selected a model using the test set, so you are in that sense fitting to the test set. By regression to the mean, you are more likely to select a model that got at least a little lucky, and your future performance will not match the test set.

So now you split into 3 sets, training to fit your model, cross-validation to select and fine-tune the model, and a test set, which you never look at until you are ready to predict your out-of-sample error. That should work in theory. But in practice, after that you will at some point go back and make adjustments based on out-of-sample performance. It’s very hard to stick 100% to that principle, although most scientists do so well enough that most results are correct. (Cough…Not! And yet that’s the nature of good science.)

Pollsters ask questions to determine whether respondents are likely voters. Then they predict whether they will actually vote based on past election data, and adjust sample weights accordingly. And there’s error from sampling variation, from your prediction of likely voters, from your sampling of past polls that you use to estimate the error of that prediction. It’s turtles all the way down. And if one particular type of voter is particularly excited to vote this election based on something that never happened in the past, you’re just not going to catch it. You just hope all the errors cancel out.

One of the funny concepts that I think is recent in machine learning is an emphasis on ‘worse is better.’

  • Regularization can add a penalty to really significant parameters on the theory that the most significant parameters got a bit lucky.
  • Dropout trains a neural network using only e.g. 50% of the neurons each iteration, so that the network develops independent paths to the correct result. Sounds nuts. Maybe it’s like shooting a basketball until you’re so tired you can’t see or feel your fingers, and it becomes automatic.
  • Random forests use an algorithm that builds a large number of decision trees which each use a randomly selected subset of predictors, and have them vote on the outcome. Kind of like a bunch of people who each see a different side of a jar of jelly beans vote on how many black beans there are. Again, sounds nuts until you see it work.

(Machine learning feels like street-fighting statistics. If it works in a well-designed out-of-sample test, use it. Throw out any opinionated model about what data looks like and where it comes from, and don’t worry about proofs or elegance.)

Nate Silver gave Trump 35% and thoroughly explained the limitations of that analysis. He said Trump was within a normal polling error. Does that make him an idiot? If his probability prediction is always perfect, he’s going to be an idiot 1 election out of 3 and a genius 2 out of 3. Others maybe not so much.

The Nate Silvers, and media, and Silicon Valley, are the guys confronting reality, creating it, with the best tools they have. Maybe they’re in a bubble, but they try to make it the most self-aware, attentive, deliberate bubble they can.

Those in the so-called ‘media/elite bubble’ get a lot of flak for both being too mainstream, and too sensitive to minority views. (i.e. both too much bias, and too much variance.)

Everyone’s models of the world are overfitted to their own experience…unless they make an intense, deliberate effort to appreciate others’ experiences…to back off a little from assuming our experience is the complete reality. If we’d been born where they were born, taught what they were taught, and lived what they live, we would live in their bubble, and believe what they believe.

I believe Trump and his followers should, for example, accept the reality of Obama’s birth certificate, and firmly reject the endorsement of the KKK. If they don’t, it seems like they need to get out of their own bubble and tolerate others. My acknowledging and appreciating your reality cannot always extend to conforming mine to yours.

The beautiful thing about science, and markets, and democracies, is that for all their faults, they harness the potential and decision-making of all participants, and when they screw up, they eventually self-correct.

It is a mistake to try to look too far ahead. The chain of destiny can only be grasped one link at a time. – Sir Winston Churchill

(inspiration credit to @firoozye)

A politics bullet-storm / linkfest

  • The Dems didn’t exactly get crushed or demolished in the Presidential. Hillary may have won the popular vote by up to 2%.
  • Trump got the fewest popular votes of any GOP candidate since W in 2000 [edit: this was based on early returns, no longer true]. Low approval. Crushed in home states that know him well. Like Waterloo, the nearest run thing you ever saw in your life.
  • But of course Dems have gotten systematically dismantled in Congress, at the local level.
  • Bill and Hillary Clinton moved Dems to center, curtailed redistributionist rhetoric, aligned more with elites. One would think that should have reduced political polarization and instability, right?
  • Then GOP moved to the right, positioned as anti-elite. Picked up some poor whites.
  • Now you have alt-right racist BS, unstable cynicism-inducing dynamic where left is the party of establishment, right anti-elite.
  • Right positions as anti-elite while pursuing policies that in practice, as a first-order approximation, are not anti-elite at all.
  • Trump comes in and fails, Dems return to anti-elite role, restoring a more traditional left-right dynamic, with both parties now on a far more populist axis.
  • When I say ‘fails’, eventually all political movements fail, sometimes they change the world and eventually peter out, sometimes they are disasters from the get-go.
  • But frankly no one has ever been less prepared, more of a political outsider, than Trump. Reagan was a two-term governor of California, had a team of some cronies and some heavyweights, like Jim Baker (picked up from GW Bush’s team), Don Regan, George Shultz.
  • That was the choice, the ultimate insider machine politician (who can’t even fathom why Obama didn’t handcuff Comey) against the ultimate outsider. You have chosen, poorly IMHO.
  • You never know how a President will govern, but Trump is a real wildcard. Can he really ally with establishment Republicans and movement conservatives and cobble together an agenda that doesn’t betray his populist base? Is he really going to let Ryan and McConnell run the country, kill Obamacare, reform entitlements, privatize Medicare and lay it on him?
  • A tea-party / liberal populist coalition? Between improbable and impossible.
  • Ineffectiveness, gridlock coupled with intensifying populist rhetoric seems a distinct possibility.
  • Finding the lost Apprentice N-word tapes and pushing Trump out seems like an avenue some on both sides of the aisle will vigorously pursue…which would inflame the populists even more.
  • On the Dem side, hard to see how anyone who follows the Clintons would not be more populist, anti-establishment like Warren or Sanders.
  • It’s hard to be party of poor whites and poor blacks at the same time, racial resentment effs up normal right-left dynamics. More so in tough times than prosperity.
  • But Trump seems racist enough that Dems will pick up the decent poor whites, the ‘non-deplorables.’ When you don’t repudiate David Duke, guys who taunt reporters about sending them to the ovens, that’s pretty bad.
  • 2-party politics is kind of like the Hotelling problem, or 2 vendors on a boardwalk.
  • The beach tends to have the vanilla lovers on one side and strawberry lovers on the other.
  • The tendency will be for both vendors to position close to each other in the center.
  • Products will tend to undifferentiate, but the one with better strawberry will be on the side with the strawberry lovers.
  • If the two vendors perversely switch places, word might spread on one side that the strawberry is better on the other side.
  • They walk the extra few steps, while maybe the vanilla lovers just go to the closer one. One vendor gets crushed.
  • Or if one just has crappier product overall, everyone goes to the other.
  • Then maybe the crappier one has to move more to the left or right, its natural side, to get any customers at all.
  • And with 3 vendors there is no stable solution. I don’t know what happens in practice, presumably 2 pair up or one goes out of business.
  • Now, I don’t identify as a liberal, I don’t like identifying as anything or joining any movements, I tend to be more of an economic realist than many liberals, but I share liberal values. I think one has to balance economic efficiency with fairness and freedom. And I’m not pleased at this election outcome.

    Trump is a promoter, not an operator. He’s all id and ego, no superego. You just can’t take the politics out of politics. Like “The Wire,” “the game is the game.” The things that breed cynicism are to some degree built into the game. It’s a divided country and the things Trump had to do to get his base more excited than Hillary’s base make it impossible to hire decent people or get anything done, or at least anything that doesn’t piss off more people than it makes happy. So Trump is already running away from his campaign at top speed.

    I’m not really a believer in the “Wall Street Trump bro relief rally.” Some of his proposed policies are highly stimulative, some are highly recessionary (rolling back globalization, tariffs etc.). Crisis could come from a variety of places and the system is fragile. If Trump doesn’t bring the yuge growth and jobs and America winning that he promised, what’s next? Will he get even more populist? If he goes down, how extreme will the true populists be who come after Trump?

    I’m hoping for the best but bracing for the worst.

    A few good links, don’t agree 100% with any of them but worth thinking about:

Safe Retirement Spending Using Certainty Equivalent Cash Flow and TensorFlow

This is not investment advice! This is a historical study/mad science experiment. It may not be applicable to you, it is a work in progress, and it may contain errors.

Certainty equivalent value is the concept of applying a discount to a stream of cash flows based on how variable or risky the stream is…like the inverse function of the risk premium.

TensorFlow is a machine learning framework that Google released last November 2015. TensorFlow is a powerful tool to find optimal solutions to machine learning problems, like neural networks in Google’s search platform.1

In this post we’ll use the concept of certainty equivalent cash flow to construct an optimized asset allocation and withdrawal plan for retirement using TensorFlow.

It’s an interesting problem; maybe it’s an interesting and/or original solution, and if nothing else it’s a starter code example for how one can use TensorFlow to solve an optimization problem like this.

1) The solution.

To cut to the chase, here is an estimate of the asset allocation and spending plan for a 30-year retirement, that would have maximized certainty-equivalent cash flow for a somewhat risk-averse retiree over the last 59 years:

Spending paths, 30-year retirements, 1928-1986, γ = 8

Spending paths, 30-year retirements, 1928-1986, γ = 8

The black line is the mean outcome. We also show the best case, worst case, the -1 and +1 standard deviation outcomes that should bracket ~68% of outcomes, and the spending path for each individual 30-year retirement cohort 1928-1986.

Year const_spend var_spend stocks bonds spend_mean spend_min spend_max
1 $1.803374 2.742079% 85.995595% 14.004405% 4.740133 3.604047 5.817874
2 $1.803374 2.895269% 85.919296% 14.080704% 4.954235 3.239628 6.990869
3 $1.803374 2.998764% 85.473425% 14.526575% 5.102155 3.338954 7.131929
4 $1.803374 3.056361% 85.205353% 14.794647% 5.231042 3.328030 8.398137
5 $1.803374 3.146801% 84.754303% 15.245697% 5.401859 3.355214 8.518801
6 $1.803374 3.278922% 84.731613% 15.268387% 5.640160 3.220717 9.473607
7 $1.803374 3.381219% 84.674124% 15.325876% 5.843391 3.262251 10.780489
8 $1.803374 3.519538% 83.207330% 16.792670% 6.106355 3.482782 11.021653
9 $1.803374 3.732725% 81.886883% 18.113117% 6.406250 3.268162 11.260031
10 $1.803374 3.953266% 81.886883% 18.113117% 6.743933 3.372960 13.164527
11 $1.803374 4.171134% 81.787428% 18.212572% 7.143623 3.348702 14.116656
12 $1.803374 4.420522% 81.082294% 18.917706% 7.594771 3.409277 14.611927
13 $1.803374 4.711470% 80.935846% 19.064154% 8.120312 3.246629 17.223627
14 $1.803374 4.937597% 80.915248% 19.084752% 8.577191 3.273653 18.662278
15 $1.803374 5.134573% 80.869082% 19.130918% 8.939804 3.422242 20.469916
16 $1.803374 5.421706% 79.235040% 20.764960% 9.347002 3.205772 21.213132
17 $1.803374 5.717492% 78.697519% 21.302481% 9.727362 3.317734 24.702617
18 $1.803374 6.080383% 78.566021% 21.433979% 10.143674 3.450519 27.386865
19 $1.803374 6.505818% 77.606353% 22.393647% 10.531063 3.401243 25.689757
20 $1.803374 6.976587% 77.226108% 22.773892% 10.881431 3.542791 25.921611
21 $1.803374 7.350485% 76.418702% 23.581298% 11.038810 3.749375 25.284224
22 $1.803374 7.920143% 75.957408% 24.042592% 11.355122 3.645121 23.647565
23 $1.803374 8.721912% 75.066357% 24.933643% 11.747497 3.685455 24.225980
24 $1.803374 9.574536% 74.210995% 25.789005% 11.987134 3.848685 26.258617
25 $1.803374 10.919882% 68.810212% 31.189788% 12.405500 3.660422 29.108868
26 $1.803374 12.781818% 68.212009% 31.787991% 12.913144 3.878824 28.843689
27 $1.803374 15.488311% 66.794616% 33.205384% 13.578350 3.829700 27.946354
28 $1.803374 20.138642% 66.270963% 33.729037% 14.794993 3.828073 30.237365
29 $1.803374 29.915170% 65.979618% 34.020382% 17.323086 3.581394 37.396578
30 $1.803374 100.000000% 61.499307% 38.500693% 35.762553 3.056128 85.260856

In this example, you allocate your portfolio between two assets, stocks and 10-year Treasurys. (We picked these 2, but could generalize to any set of assets.)

  • Column 1: A fixed inflation-adjusted amount you withdraw by year. In this example we start with a portfolio of $100, so each year you withdraw $1.803, or 1.803% of the starting portfolio. This amount stays the same in inflation-adjusted terms for all 30 years of retirement. (All dollar numbers in the model are constant dollars after inflation. In a real-world scenario, you would initially withdraw 1.803% of your starting portfolio and increase nominal withdrawal by the change in CPI to keep purchasing power constant.)
  • Column 2: A variable % of your portfolio you withdraw by year, which increases over time. So in year 25 you would spend $1.803 in constant dollars plus 10.92% of the current value of the portfolio.
  • Column 3: The percentage of your portfolio you allocate to stocks by year, which declines over time.
  • Column 4: The amount allocated to Treasurys, which increases over time (1 – stocks).
  • Column 5: The mean amount you would have been able to spend by year if you had followed this plan, you retired in years 1928-1985 and you enjoyed a 30-year retirement.
  • Column 6: The worst case spending across all cohorts by year.
  • Column 7: The best case spending by year.

This is a numerical estimate of a plan that would have maximized certainty equivalent cash flow over all 30-year retirement cohorts for a moderately risk-averse retiree, under a model with a few constraints and assumptions.

To view how optimal plan estimates change under various values of γ, go here.

2. How does it work? What is certainty-equivalent cash flow (the value we are maximizing)?

Certainty-equivalent cash flow takes a variable or uncertain cash flow and applies a discount based on how risk-averse you are, and how volatile or uncertain the cash flow is.

Suppose you have a choice between a certain $12.50, and flipping a coin for either $10 or $15. Which do you choose?

People are risk averse (in most situations). So most people choose a certain cash over a risky coin-flip with the same expected value2.

Now suppose the choice is between a certain $12, and flipping the coin. Now which do you choose?

This time, on average, you have a bit more money in the long run by choosing the coin-flip. You might take the coin-flip, which is a slightly better deal, or not, depending on how risk-averse you are.

  • If you’re risk-averse, you may prefer the coin-flip (worth $12.50) at $12 or below. (You get paid on average $0.50 to flip for it.)
  • If you’re even more risk-averse, and you really like certain payoffs, the certain payoff might have to decrease further to $11 before you prefer the coin-flip worth $12.50. (You need to get paid $1.50 to flip for it.)
  • If you’re risk neutral, anything below $12.50 and you’ll take the $12.50 expected-value coin-flip. (You don’t care at $12.50, and flip every time for $0.01.)

We’ll refer to that number, at which you’re indifferent between a certain cash flow on the one hand, and a variable or uncertain cash flow on the other, as the ‘certainty equivalent’ value of the risky stream.

We will use constant relative risk aversion (CRRA). CRRA means that if you choose $12 on a coin-flip for $10/$15, you will also choose $12,000 on a coin-flip for $10,000/$15,000. It says your risk aversion is scale invariant. You just care about the relative values of the choices.

How do we calculate certainty-equivalent cash flow? For a series of cash flows, we calculate the average CRRA utility of the cash flows as:

U=\frac{1}{n}\sum_{i}\frac{C_i^{1-\gamma}-1}{1-\gamma}

Using the formula above, we

  • Convert each cash flow to ‘utility’, based on the retiree’s risk aversion γ (gamma)
  • Sum up the utility of all the cash flows
  • And divide by n to get the average utility per year.

Then we can convert the utility back to certainty equivalent cash flow using the inverse of the above formula:

CE = [U(1-\gamma) + 1] ^ {\frac{1}{1-\gamma}}

This formula tells us that a variable stream of cash flows Ci over n years is worth the same to us as a steady and certain value of CE each year for n years.

No need to sweat the formula too much. Here’s a plot of what CRRA utility looks like for different levels of γ.

CRRA utility vs. cash flow for selected values of γ

CRRA utility vs. cash flow for selected values of γ

You can look at 1 as a reference cash flow with a utility of 0. As you get more cash flow above 1, your utility goes up less and less. As you get less cash flow below 1, your utility goes down more and more. As γ goes up, this convexity effect increases. (But recall that levels don’t change choices with CRRA and same can be said for any point on the curve. Trust us, or try it in Excel!)

The key points are:

  • We use a CRRA utility function to convert risky or variable cash flows to a utility, based on γ the risk aversion parameter.
  • After summing utilities, we convert utility back to cash flows using the inverse function.
  • This gives the certainty equivalent value of the cash flows, which discounts the cash flows based on their distribution.
  • γ = 0 means you’re risk neutral. There is no discount, however variable or uncertain the cash flows. The CE value equals the sum of the cash flows.
  • γ = 8 means you’re fairly risk averse. There is a large discount.
  • The higher the variability of the cash flows, the greater the discount. And the higher the γ parameter, the greater the discount.
  • The discount is the same, if you multiply all the cash flows by 2, or 1000, or 0.01, or x. Your risk aversion is the same at all levels of income. That property accounts for the somewhat complex formula, but it describes a risk aversion that behaves in a relatively simple way.

If we think that, to a reasonable approximation, humans are risk averse, they make consistent choices about risky outcomes, and their risk aversion is scale invariant over the range of outcomes we are studying, CE cash flow using a CRRA utility function seems like a reasonable thing to try to maximize.

In our example, we maximize certainty-equivalent cash flow for a retiree over 30 years of retirement, over the historical distribution of outcomes for the 59 30-year retirement cohorts 1928-1986. The retiree’s risk aversion parameter is 8. This is risk-averse (but not extremely so).

Maximizing CE spending means the retiree plans to spend down the entire portfolio after 30 years. Presumably the retiree knows how long he or she will need retirement income. Perhaps the retiree is 75 and 30 seems like a reasonable maximum to plan for, perhaps the retiree has an alternative to hedge longevity risk, like an insurance plan or tontine.

3. How does this work in TensorFlow?

TensorFlow is like a spreadsheet. You start with a set of constants and variables. You create a calculation that uses operations to build on the constants and variables, just like a spreadsheet. The calculation operations you define are represented by a computation graph which tracks which operations depend on which. You can tell TensorFlow to calculate any value you defined, and it will only recompute the minimum necessary operations to supply the answer. And you can program TensorFlow to optimize a function, i.e. find the variables that result in the best value for an operation.

We want to set the values for these 3 variables, in order to maximize CE cash flow:

1: Constant spending (a single value): A constant inflation-adjusted amount you withdraw each year in retirement. This is like the 4% in Bengen’s 4% rule. The inflation-adjusted value of this annual withdrawal never changes.

2: Variable spending (30 values, one for each year of retirement, i.e. a list or vector): A variable percentage of your portfolio value you withdraw each year. In contrast to the Bengen 4% rule, we’re, saying, if the portfolio appreciates, you can safely withdraw an additional amount based on the current value of the portfolio. Your total spending is the sum of 1) constant spending and 2) variable spending.

3: Stock allocation (30 values, one for each year): We are going to study a portfolio with 2 assets: S&P 500 stocks and 10-year Treasurys.3

Our key constants are:

  • γ = 8. (a constant because we are not optimizing its value, unlike the variables above).
  • A portfolio starting value: 100.
  • Inflation-adjusted stock returns 1928-2015 (all numbers we use are inflation-adjusted, and we maximize inflation-adjusted cash flow).
  • Inflation-adjusted bond returns 1928-2015.

Operations:

  • Calculate 59 30-vectors, each one representing the cash flow of one 30-year retirement cohort 1928-1986, using the given constant spending, variable spending, and stock allocation.
  • Calculate the certainty equivalent cash flow of each cohort using γ.
  • Calculate the certainty equivalent cash flow over all cohorts using γ.
  • Tell TensorFlow to find the variables that result in the highest CE spending over all cohorts.

We initialize the variables to some reasonable first approximation.

TensorFlow calculates the gradient of the objective over all variables, and gradually adjusts each variable to find the best value.

See TensorFlow / python code on GitHub.

Below, you can click to set the value of γ and see how the solution and outcome evolves.

          

          

          
const_spend var_spend stocks bonds spend_mean spend_min spend_max
0 2.25168 2.203127 81.789336 18.210664 4.604840 3.724279 5.431478
1 2.25168 2.287617 81.584539 18.415461 4.731527 3.418391 6.254532
2 2.25168 2.349896 81.042750 18.957250 4.826188 3.513405 6.347958
3 2.25168 2.400992 80.498949 19.501051 4.932219 3.517121 7.302567
4 2.25168 2.457950 79.886688 20.113312 5.049210 3.505759 7.408146
5 2.25168 2.529678 79.489911 20.510089 5.197089 3.386326 7.941039
6 2.25168 2.606445 79.154073 20.845927 5.351562 3.420979 8.891322
7 2.25168 2.713869 78.390047 21.609953 5.557626 3.522455 9.081053
8 2.25168 2.836364 77.651215 22.348785 5.743047 3.348000 9.203257
9 2.25168 2.981631 77.651215 22.348785 5.980422 3.458124 10.540080
10 2.25168 3.137015 77.086559 22.913441 6.282104 3.425701 11.225243
11 2.25168 3.303466 76.476785 23.523215 6.610572 3.473727 11.643038
12 2.25168 3.478377 76.048041 23.951959 6.969665 3.317807 13.439483
13 2.25168 3.625880 75.627575 24.372425 7.310505 3.343283 15.059415
14 2.25168 3.770352 75.205921 24.794079 7.616833 3.459124 16.401976
15 2.25168 3.936515 74.476987 25.523013 7.908735 3.274292 17.074267
16 2.25168 4.134133 73.971955 26.028045 8.230050 3.366878 20.061546
17 2.25168 4.377164 73.565393 26.434607 8.588685 3.435252 22.118598
18 2.25168 4.646451 72.994245 27.005755 8.918598 3.395044 21.213925
19 2.25168 4.954221 72.523218 27.476782 9.251004 3.521019 21.152092
20 2.25168 5.292311 71.995758 28.004242 9.595167 3.708905 21.061695
21 2.25168 5.753135 71.535585 28.464415 10.053624 3.599248 20.643662
22 2.25168 6.370145 71.022398 28.977602 10.585032 3.637109 22.381867
23 2.25168 7.174056 70.527878 29.472122 11.207199 3.846862 23.833581
24 2.25168 8.313613 69.984383 30.015617 11.968715 3.682980 27.458563
25 2.25168 9.946128 69.518839 30.481161 12.956893 3.900901 29.879319
26 2.25168 12.422349 68.999420 31.000580 14.327986 3.908337 30.120498
27 2.25168 16.581733 68.496478 31.503522 16.463549 3.948743 35.387707
28 2.25168 24.927346 68.006998 31.993002 20.297396 3.750516 46.393183
29 2.25168 100.000000 67.503853 32.496147 54.071078 2.945589 135.483633

 

4. Comments and caveats.

The results above are just an approximation to an optimal solution, after running the optimizer for a few hours. However, I believe that it’s close enough to be of interest and I believe that in this day and age of practically unlimited computing resources, we can likely calculate this number to an arbitrary level of precision in a tractable amount of time. (Unless I overlooked some particularly ill-behaved property of this calculation.)

Numerical optimization works by hill climbing. Start at some point; for each variable determine its gradient, i.e. how much changing the input variable changes the objective; update each variable in the direction that improves the objective; repeat until you can’t improve the objective.

It’s a little like climbing Mount Rainier, by just looking at the very local terrain and always moving uphill. It’s worth noting that if you start too far from your objective, you might climb Mt. Adams.

Similarly, in the case of optimizing CE cash flow, we might have just found a local optimum, not a global optimum. If the shape of the solution surface isn’t convex, if the slopes are flat in more than one place, we might have found one of those and not the global optimum. So this solution is not an exact solution, but finding a very good approximation of the best solution seems tractable with sufficiently smart optimization (momentum, smarter adaptive learning rate, starting from a known pretty good spot via theory or brute force).

We see that in good years, spending rises rapidly in the last few years. The algorithm naturally tries to keep some margin of error to not run out of money, and then also naturally tries to maximize spending by spending everything in the last couple of years.

As γ increases, constant spending increases, stocks decrease, and bonds increase.

It’s worth noting that we added some soft constraints: keep allocations between 0 and 100%, i.e. you can’t go short. Keep spending parameters above zero, you can’t save more now and spend more later. Also, we constrained the stock allocation to decline over time. The reason is that a worst case of running out of money has a huge impact on CE cash flow. The worst year to retire is 1966, and the most impactful year is 1974, when stocks were down > 40%. So an unconstrained solution reduces stocks in year 9 and then brings them back up. While we laud the optimizer for sidestepping this particular worst case scenario, this is probably not a generalizable way to solve the problem. We expect stock allocation to decline over time, so we added that as a constraint, and avoid whipping the stock allocation up and down.

How the optimization handles this historical artifact highlights the contrast between a historical simulation and Monte Carlo. Using a historical simulation raises the possibility that something that worked with past paths of returns may not work in all cases in the future, even if future return relationships are broadly similar. Monte Carlos let us generate an arbitrary amount of data from a model distribution, eliminating artifacts of a particular sample.

However, a Monte Carlo simulation assumes a set of statistical relationships that don’t change over time. In fact, it seems likely that the relationships over the last 59 cohorts did change over time.

  • Policy regimes, i.e. the fiscal and monetary response to growth and inflation changes under constraints like the gold standard, schools of thought that dominate policy.
  • Expectations regimes, whether investors expect growth and inflation, based on how they may have conditioned by their experience and education.
  • Environment regimes, changes in the world as there are wars, depressions, economies become more open.

Pre-war, dividend yields had to be higher than bond yields because stocks were perceived as risky. Then it flipped. Growth was seen as predictable, companies re-invested earnings, taxes made them less inclined to distribute. Today, once again, dividends are often higher than bond yields.

For 3 decades post-war inflation surprised to the upside, for the last 3 decades it surprised to the downside.

The beauty of a historical simulation is it answers a simple question: what parameters would have worked best in the past? Monte Carlo simulations can give you a more detailed picture, if you can only believe their opinionated assumptions about a well-behaved underlying distribution.

One has to be a bit cautious with both historical simulations, which depend on the idiosyncrasies of the past, and Monte Carlos, which assume known, stable covariances. It would be wise to look at both historical simulation and Monte Carlos, do a few Monte Carlos with the range of reasonable covariance matrix estimates, use the worst case, and run historical simulations over all cohorts, and include a margin of error (especially in the current ZIRP environment which might repeat a 1966 cohort of the damned).

Another assumption in our simulation is that a certain dollar in year 30, when you may be 90, is worth the same as a dollar in year 1.

A dollar may be worth spending on different things at 60 vs. at 90, and, in later years the retiree is more likely to be dead. With respect to the mortality issue, in the same way we are computing certainty equivalent cash flow over a distribution of market outcomes, we can also compute it over a distribution of longevity outcomes. This feature is in the code, but I will leave discussion for a future blog post. The current post is more than complex enough.

Of course, this simulation doesn’t include taxes, expenses.

Finally, there are reasons to choose a less volatile portfolio that doesn’t maximize CE cash flow, if the volatility is stomach-churning in and of itself, or if it leads the retiree to re-allocate at inopportune times or otherwise change plans in a suboptimal way.

5. Conclusion.

Optimizing CE cash flow over historical data might be flawed, it might be simplistic, or it might be useful. It’s just an itch that I’ve wanted to scratch for a while. It may seem complicated, but that’s because the problem is interesting. The one takeaway should be that if you can decide what your utility/cost function is, you can find a way to maximize it using today’s computing tools and resources.

Ultimately, you have to optimize for something. If you don’t know where you want to go, you’re not going to get there. Since we have tools to optimize complex functions, perhaps the discussion should be over what to optimize for. A CRRA framework is a good possibility to start with, although I there are others as well.

This is not investment advice! This is a historical study/mad science experiment. It may not be applicable to you, it is a work in progress, and it may contain errors.

Notes

On 9/25 I updated this post. After running for many additional hours from additional starting points, found a &gamma=8; plan that improved the original by about 1%. The change is small. But it’s important to note that the optimization doesn’t converge on a single solution quickly, and the solution varies a bit depending on the starting point. It appears more work is needed to make this analysis an aide to practical decision-making. Also added the visualization allowing you to click to see how spending plans change as γ changes.

1 TensorFlow lets you definite a calculation sort of like a spreadsheet does, and then run it on on your Nvidia GPU (Graphical Processing Unit). Modern GPUs have more transistors than CPUs, and are optimized to do many parallel floating point calculations. The way you numerically optimize a function is by calculating a gradient vs. each input, and gradually changing the inputs until you find the ones that produce the best output. 100 inputs = 100 gradients that you calculate each step, and GPUs can calculate all 100 simultaneously, and accelerate these calculations quite dramatically. That being said, this optimization seems to run 4-5x faster on CPU than GPU. ¯\_(ツ)_/¯ Without knowing a lot of TensorFlow internals, a single operation that needs to be done on CPU might mean the overhead of moving data back and forth kills the GPU advantage. Or maybe the Amazon g2 GPU instances have some driver issues with TensorFlow. Them’s the breaks in numerical computing.

2 This may beg the question of lotteries, why people gamble, whether homo economicus is a realistic assumption. We’re assuming rational people here. In general in financial markets, the more risky an investment is, the higher expected return it needs to offer to find a buyer. So the assumption people prefer less risky and variable retirement cash flows seems well established. It would also be possible in theory to do the same optimization for any utility function, although some would be more troublesome than others. If we have a cost function that measures the result of a spending plan, we measure how it performs and compare spending plans. If we don’t have such a cost function, we can try different ways of constructing plans and compute the results, but we don’t have a systematic way to compare them.

3 Bengen used intermediate corporates as a bond proxy. They have a higher return than Treasurys. I would use the same data, but it would involve a trip to the library or possibly a Bloomberg. I used this easily available data. At some point I can run an update so it is comparable to Bengen’s result.

The Game Theory of Assholes

The reasonable man adapts himself to the world; the unreasonable one persists in trying to adapt the world to himself. Therefore, all progress depends on the unreasonable man. – George Bernard Shaw

I beseech you, in the bowels of Christ, think it possible you may be mistaken! – Oliver Cromwell

Nassim Nicholas Taleb has a pretty good piece on the tyranny of the stubborn minority.

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Pokémon economics, secular stagnation, and cognitive dissonance

There are these two young fish swimming along, and they come across an older fish swimming the other way, who nods at them and says, “Morning, boys, how’s the water?”

And the two young fish swim on for a bit, and then eventually one of them looks over at the other and goes, “What the hell is water?” – David Foster Wallace

A physicist, an engineer, and an economist are stranded on an island with nothing to eat. A can of beans washes ashore. The physicist says, “Let’s build a fire and heat the can, the pressure will make it pop open, and we can eat the beans.” The engineer says, “The can will explode and beans will go everywhere. Let’s smash the can open with a rock.” The economist says, “Lets assume that we have a can-opener…” – Original author unknown

Do economists really understand the essence of what’s going on in the economy, or are they like fish who don’t know what water is, assuming can openers to solve what ails it?

Vox had an article on what Pokémon Go says about capitalism.

The gist: all the money from the digital economy goes to a few people in large companies like Apple and Nintendo, and the rest of the world is in a brutal race to the bottom.

Now, that’s not 100% true…Pokémon Go creator Niantic is a startup, if an unusually well-heeled and well-financed startup.

But it feels essentially true.

The reason I started writing this long and digressive rant, is that I posted the Vox story about Pokémon Go in an economics forum, and it got banned for not contributing to the economic discussion. The notion that there could be secular stagnation, and it could have to do with income distribution, and there might be policy implications, was, to some folks, not even a proper subject for analysis and debate.

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A fun 3D visualization of the financial Twittersphere

Here’s a fun little update of that visualization of the financial Twittersphere I posted in May. This one is in 3D, you can zoom (with scroll wheel) and drag it around (with mouse, also see controls in top right).

It might take a minute to load up, not work too well on older computers/browsers. Just wait out/ignore any popups, warnings about script on page running slowly. If the iframe below is wonky, try this full-page version.
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Negative interest rates are an unnatural abomination

Mayor: What do you mean, “biblical”?
Dr Ray Stantz: What he means is Old Testament, Mr. Mayor, real wrath of God type stuff.
Dr. Peter Venkman: Exactly.
Dr Ray Stantz: Fire and brimstone coming down from the skies! Rivers and seas boiling!
Dr. Egon Spengler: Forty years of darkness! Earthquakes, volcanoes…
Winston Zeddemore: The dead rising from the grave!
Dr. Peter Venkman: Human sacrifice, dogs and cats living together… mass hysteria!
Mayor: All right, all right! I get the point!
Ghostbusters (1984)

Happy 4th of July weekend! Some macro ‘blinding glimpse of the obvious’ blogging.
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A weekend Brexit reading list

This business will get out of control. It will get out of control and we’ll be lucky to live through it. – Admiral Josh Painter, The Hunt for Red October
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