Modern conservative and progressive thought–mainly progressive thought as it controls the academy–reminds me of a biased algorithm trying to estimate a model with tons of parameters. You need an algorithm that keeps trying parameter numbers, evaluating how well the model fits, and trying new ones. When I did economic research to estimate these models I would watch the four series, which were filtered from the parameters, as they would drastically change depending on the numbers chosen. In 100 dimensional space there are many areas in the parameter space your model can’t explore. You’re never sure if you have converged on a true estimate, or your model took a wrong turn on one parameter a few thousand iterations ago. If your algorithm gets stuck at a local maxima, it needs to take a huge guess in the right direction to escape. Our modern political thought–unlike all previous times and countries–might not be at a local maxima but is in fact progressing to the global maxima of a utopian society. If so, I hope to help prove that claim by exploring the most unfashionable arguments for you in order to help you build your confidence that we are approaching that global maximum. I do this all in the name of progress.
We live in a world of more than 100 parameters. If the number of parameters that govern our interactions with the world can even be quantified in a meaningful sense. All the same, whether it’s a true comparison or an analogy–no way to tell–it’s a useful way of envisioning the world. After all, our best scientific discoveries tend to arise from mathematical and logical models with parameters, so it’s safe to assume the same logic applies to reality as a whole. Let’s make some assumptions and impose structure on what we want to call our model of reality, and state that each parameter represents a clear factor that makes sense to the human brain. We recently had a mass shooting (I’m sure this sentence will still hold true even if you read this post a year from when I posted it). In this case the primary parameters would be gun legislation, Islam, immigration, homophobia, and so forth. You’ll notice this is a little shaky, since homophobia and Islam might have a distinct relationship, but let’s just stick with it for now. What if we are missing parameters, and that these parameters have high-dimensional nonlinear interactions? If these are emergent properties from complex interactions every nice little argument would be dead wrong. Shorter arguments can be perceived as more persuasive. And the memetics of nice arguments can interact with the structures of our government.
In the social sciences our goal is to explore the parameter space. Sure we do this with math and models, but it all starts with our brain’s learning algorithms. We try to filter out areas worthy of study in our n-dimensional reality. Political systems tend to be useful areas to study, as they explain how humans interact. The Mariana trench is not useful in explaining how humans interact. This is obvious, but it’s also not obvious. When you try to teach a computer the difference between obvious things, it often ends up being way way harder than you might expect. What, exactly, is our form of government? It’s a classification issue. There is no such thing, literally, as a Democracy (despite what the tombs of old political theorists tied to prove). It’s a word we assign when an extraordinary amount of attributes are satisfied falling into some boundaries. The definition changes over time, the attributes we consider change over time. A measure of the electoral connection seems to be one of the most salient features filtered out by political scientists. When we pretend we have firm definitions we are lying to ourselves. Our Democratic experiment continues, perhaps the defining attributes are yet to arise.
On Democracy, what exactly is a political centrist? As Soviet historian Martin Malia surveys academic thought in the introduction to his book, he writes that “The Soviet Union portrayed by Western social science represented a variant of ‘modernity,’ rough-hewn no doubt, yet in significant measure a success. Most specialists agreed, further, that the system was ‘stable.’” Malia continues to point out popular academic thought that the rise of Bolshevism was “Democratic” and tolerated “diverse views.” But those are boring examples. Let’s try some more fashionable conservative click-bait. If you’re not aware of this strain of communist apologists you might be a little shocked. More likely you have an idea that there are some of these people that exist, and take it for granted. Anyway, the red scare is so 1964. What we should really be concerned about is too many white men in our curriculum. That’s a topical link for me since I’m from Seattle, but it is commonplace.
There are enough blogs that highlight the absurd standards on what issues academics are (or aren’t) morally judged on. The question that interests me is how many times has this fight been lost already? We have entire departments devoted to all forms (except white) of ethnic nationalism, gender identity, and activism. The goals of these departments are to build a cohesive worldview that will build a better society, and proposes the core assumptions and worldviews that we must hold and enact to approach this optimal society. This blog focuses on only one question: Does this work as intended? Not whether it sounds nice, because of course it sounds nice. Plus, who doesn’t want to battle evil in pursuit of justice? Battle is fun. Unfortunately for the battle ready, Karl Popper, the most famous Philosopher of Science of the 20th century, doesn’t seem to think these battles should exist. In The Poverty of Historicism from 1957 he notes:
“i) Unintended consequences: the implementation of Historicist programs such as Marxism often means a fundamental change to society. Due to the complexity of social interaction this results in lots of unintended consequences (i.e. it tends not to work properly). Equally it becomes impossible to tease out the cause of any given effect so nothing is learnt from the experiment / revolution.
ii) Lack of information: large scale social experiments cannot increase our knowledge of the social process because as power is centralised to enable theories to put into practice, dissent must be repressed, and so it is harder and harder to find out what people really think, and so whether the utopian experiment is working properly. This assumes that a dictator in such a position could be benevolent and not corrupted by the accumulation of power, which may be doubted.”
Do you think Karl Popper is assigned in many modern activist departments? I might be getting ahead of myself – a few quotes on soviet era scholarship, a link to some radical student activists, and a claim that Popper isn’t assigned shouldn’t be sufficient to convince anyone that activist departments do exist, much less that they are more about gaining power than scientific discovery. I will try to convince you of this as carefully and scientifically as possible. Or convincing myself that I’m wrong. Or that I’m in some foggy no man’s land of pure noise. I don’t know. I think I’m right, but many wrong people have thought they are right, so I’ll exercise some humility.
Still, there are reasons to be concerned. Certain questions are off-limits and cannot be discussed by any professor at an elite institution with a dream of tenure, power, or influence. They are left to a few brave academics and smart bloggers. The problem isn’t that these guys are right (those two links are on human biodiversity). It’s that they cannot be spoken. Try it, next time you meet up with your most fashionable friends bring up the arguments in The 10,000 Year explosion. Don’t assert that they could be right, or hint to it. Actually, don’t, because you’ll make everyone uncomfortable and might upset a few people.
Our philosophy of science works very well for the STEM fields, it’s rapidly improving for laboratory and medical experiments, and it’s making progress in some areas of the social sciences. Yet in a world where our supercomputers cannot simulate complex molecular interactions in any amount of reasonable time, with what hope do we have of constructing a story of history that has filtered out the true causal drivers of history, with all pesky interactions controlled. What if some of the views of history are dangerous? If humans are tribal, than evidence supporting a tribe–even if it’s true–might lead to war, genocide, slavery, and a number of horrific outcomes. Would we then need to be shielded from these views? Conspiratorial logic is awful though, there is no conspiracy. No group of academics gathered in a musky view to suppress thought. It makes for good movies, but it’s unrealistic. It would come about from emergent properties.
I remember vividly I was forced to take a course in African American Political Thought. I say forced because I had no choice in the curriculum in the honors program. We read James Baldwin and Frederick Douglas exclusively. Growing a little bored of literature, I once asked my professor in a seminar what his view on Thomas Sowell’s argument was in his book White Rednecks Black Liberals. Maybe Sowell was right, maybe he wasn’t, I didn’t know, I was a student. I was told we weren’t to discuss him because my professor didn’t respect Sowell’s authority. That seemed a little strange. Plus, in what sense is literature science? I don’t think it’s obvious one way or another how to incorporate literature into scientific thought. It is a valid question, but not obvious. There was no conspiracy here, was this just one of those emergent properties in action?
How do we incorporate old books, stories, or the news into our conception of government? We must though, as we all have opinions on these that are a direct result of more than just some textbooks and courses. When I think of the success of capitalism, what goes on in my head? It’s a cacophony of sources, models, and stores that I have read. They fire through the neural network of my synapses and I filter out a reason why capitalism is probably good. All this conditional on there being an already filtered set of attributes that we can consistently classify as capitalism. My experience of the world seems that trying to derive complex systems from first principles doesn’t work. No matter how reasonable the axioms appear. What actually seems to happen is that we filter out key components from empirical observations. If that’s true though, teaching the field from assumptions, as I learned it, doesn’t really make sense.
The rest of my courses focused on a standard liberal education. I was lucky enough to have a professor that introduced me to the great libertarian thinkers of the 20th century. That was independent study, of course. It’s incredibly rare for Hayek or Milton Friedman to be taught in class. Other than the libertarian perspective, there aren’t many academics who disagree with modern progressive thought. Some Economists will have a conservative bend, typically due to economic policy, and there are some unfashionable religious schools that disagree with abortion and perceived debauchery of the left.
At the time the libertarian thinkers seemed to be the dark side. I was never willing to fully commit, in the back of my mind gay marriage, universal healthcare, and legalized marijuana were the defining fights of my generation. I remember vividly the feeling of outrage and hate when I saw the religious right fight against gay marriage. Who do these people think they are? I saw myself as an underdog fighting against an oppressive power. Sure we had the entire academic system, most policy makers, the presidency, the NYtimes, and everyone under age 25 on our side. Of course, that’s not to say some of the points aren’t legitimate. While tough on crime policy can’t be obviously attributed to one evil side, it’s no secret that modern public opinion has the right favoring the death penalty and tough on crime approaches. Plus, depriving women of reproductive health care and preventing gays from marrying is needless and petty. The reason I’m not worried is because the religious right is boring because they are losing. Every time progressives beat them on an issue they push the line forward and start the next battle while lamenting that war never ends. The difference seems to be that progressives will actually define the 21st century. Does anyone really think the American right will?
The problem is that as you obsess over your in-group and fighting the out-group, you slowly form a contorted and twisted version of the world. You’re presented with a picture of reality that states some set of issues are the issues. Your opponents take their stance on the issues. What are the issues? They tend to be the specific policy questions that best split the population into two and can be incorporated into one of two parties. The question we have to ask yourselves is does this group vs. group battle over the issues portray an accurate picture of reality? Or do we get so caught up in our side, our battle, our righteousness, that we completely lose sight of just how complex our world is? And if we do lose sight, who is going to tell us? Where is the guy, detached from any group mentality, reading primary sources from the past and present, that will tap you on the shoulder and say “I think you’ve given in to the hot blooded excitement of tribalism, and have gone slightly off course.”
I’m not convinced what I learned accurately represents reality, or a meaningful history of thought. The academy handed me a set of base assumptions. I started out with half my worldview assumed to be true, without realizing I was at all learning on assumptions. It’s not that what I was taught was necessarily wrong, but after seeing the world of high-fashion in the academy and corruption of scientific thought, I have no reason to trust anything I was taught as an unbiased picture.
Curtis Yarvin started the neoreactionary movement online, which really just amounted to him breathing life into a once renowned, now less popular philosopher, Thomas Carlyle. Is he right? Well, his combined evidence and criticism of modern progressive thought is overwhelming. Is his solution the right one–that Democracy is a broken system? They are interesting questions. Why aren’t they asked in the academy? One reason is that because they are so obviously wrong, like anti-vaccination or numerology, that they serve no purpose. They aren’t obviously wrong to me and at least a few other smart people I know. Maybe we aren’t that bright and everyone else has it figured out, honestly though, I don’t think that’s it.
In our developed countries the inertia required to exercise political violence is large, as it rarely promises rewards. This is due to centuries of institutional architecture. In other countries and times it hasn’t worked as well. Looking back these revolutions, insane political experiments, massacres, famines, and wars, seem wrong. They were brainwashed people, probably evil. Except they didn’t view themselves as evil, they truly believed what they were doing was right and would save their country. Malcolm Muggeridge was a British journalist stationed in the USSR. He met with many of the British liberals who had come to visit the grand Soviet experiment with optimism:
You would be amazed at the gullibility that’s expressed. We foreign journalists in Moscow used to amuse ourselves, as a matter of fact, by competing with one another as to who could wish upon one of these intelligentsia visitors to the USSR the most outrageous fantasy. We would tell them, for instance, that the shortage of milk in Moscow was entirely due to the fact that all milk was given nursing mothers – things like that. If they put it in the articles they subsequently wrote, then you’d score a point. One story I floated myself, for which I received considerable acclaim, was that the huge queues outside food shops came about because the Soviet workers were so ardent in building Socialism that they just wouldn’t rest, and the only way the government could get them to rest for even two or three hours was organizing a queue for them to stand in. I laugh at it all now, but at the time you can imagine what a shock it was to someone like myself, who had been brought up to regard liberal intellectuals as the samurai, the absolute elite, of the human race, to find that they could be taken in by deceptions which a half-witted boy would see through in an instant.
At the time if you were an intellectual liberal in Britain it was expected that you would fawn over the USSR and the great promises of communism. How can we be confident we aren’t falling into the same traps? None of us want to be made fun of in 100 years for being misguided. If you took an incredibly unfashionable argument, something well thought out and not base, and presented it on Facebook how many friends would revel in their disgust for you? Sharing fashionable posts is a great way to signal how smart you are, and historically they have been misguided (as newsletters and pamphlets before Facebook). They aren’t always wrong, but what would it take to instill a seed of doubt in your mind?
On the other hand, the neo-reactionary view stating that progressivism and Democracy is completely broken is outrageous. So there might be a simpler explanation for why we don’t consider these seemingly radical ideas: They are stupid. If we assume a sort of efficient market hypothesis ideas it makes sense that the intellectuals of our past would have already vetted and discarded the areas of parameter space that make no sense. Unfortunately, the existence today of academics who take seriously the mysticism of philosophers like Hegel and the unfalsifiability of Marx doesn’t support that argument. As Stove points out, wherein he quotes Hegel:
“His book is, naturally, full of quotations from Hegel’s early writings. In subject-matter these passages range from the astronomical to the zoological. For the examples which I promised earlier in this essay, I have chosen two of the astronomical ones. First:
In the indifferences of light, the aether has scattered its absolute indifference into a multiplicity; in the blooms of the solar system it has borne its inner Reason and totality out into expansion. But the individualizations of light are dispersed in multiplicity [i.e. the fixed stars], while those which form the orbiting petals of the solar system must behave towards them with rigid individuality [i.e. they have their fixed orbits]. And so the unity of the stars lacks the form of universality, while that of the solar system lacks pure unity, and neither carries in itself the absolute Concept as such.
In the spirit the absolutely simple aether has returned to itself by way of the infinity of the Earth; in the Earth as such this union of the absolute simplicity of aether and infinity exists; it spreads into the universal fluidity, but its spreading fixates itself as singular things; and the numerical unit of singularity, which is the essential characteristic (Bestimmtheit) for the brute becomes itself an ideal factor, a moment. The concept of Spirit, as thus determined, is Consciousness, the concept of the union of the simple with infinity;
Do you know any example of the corruption of thought which is more extreme than these two? Did you even know, until now, that human thought was capable of this degree of corruption?
Yet Hegel grew out of Kant, Fichte, and Schelling, as naturally as Green, Bradley, and all the other later idealists, grew out of him. I mention these historical commonplaces, in case anyone should entertain the groundless hope of writing Hegel off as an isolated freak. But now, remembering those historical facts, while also keeping our eyes firmly on the two passages I have just given, will someone please tell me again that the Logical Positivists were on the wrong track, and that we ought to revere the ‘great thinkers’, and that the human race is not mad?
I agree Stove, why were the Logical Positivists told that they were on the wrong track? Oh, and first, who were the Logical Positivists? We’ve been handed a set of great thinkers, philosophers, scientists, and lessons from our historical past. Combined together they tell a story of how the world unfolded, the best form of government, and the most refined ideas. If we could look backwards and pull out a different set of thought that has been forgotten, but that is equally robust and suggests our current conclusions are completely incorrect, what would that look like?
I’ve spent the past two years learning economics, coding, and what I want from my own life. When we model any system, we use past measured data series that we suspect are related to our questions. Our brains operate the same way as they turn our sensory information and experiences into inferences on the world and ourselves.
When I was a child my grandma told me a story about a child who didn’t listen to his parents, and spilled boiling water all over himself. The story is so simple, and the goal is to replace the desire to experience a new dangerous sensation without actually experiencing it in reality. Why was it a story, and not just a statement to stay away from boiling water and hot stoves? Because humans prefer experiences to calibrate their brains. If you tell a child not to touch, he still wants to touch it because of a base desire to build new experiences from sensory information. This same command wrapped in a story let me experience a simulation of the experience rather than just a mandate to avoid it, which I’d rebelliously ignore.
This is the same reason why the death of a fictional character can move me to tears, but the headline loss of civilian life doesn’t pull that same emotional feeling from my brain. I know it’s sad when people die, but without knowing the emotional bonds underlying that sadness, even if it’s simulated in a book, I won’t feel that profound sense of loss. That shouldn’t surprise you, because you’re probably the same. But when you stop to think about it it’s pretty weird huh?
It’s important to me to live my life correctly, really important. I don’t believe there is no right or wrong way, I’m pretty sure there is a right way, and that’s the way I intend to live. Similar to my innate ability at math and scientific inference, my innate ability at how to live the right life is probably wrong and poorly calibrated. We take for granted that scientific inference makes sense, but as I’ve written before, it was far from obvious. If you taught a 10th grade principles of scientific inference and sent him back to 16th century Britain, he could revolutionize epidemiology and save millions of lives based on a few concepts of controls and experiments. There is no reason to think we’ve solved science.
As we are born and grow up our calibration is set through a shared societal knowledge, historical stories, apocryphal stories, and religion. If you’re lucky, your family helped in your calibration. Maybe your school. But not everything is done by others, we’re responsible for calibrating our views of the world as well. To do this we need to simulate experiences we have never felt, and might never feel.
I have read many books that have moved or shifted my outlook on life by letting me simulate experiences I might never experience. I read the Brothers Karamazov a few years ago, which improved my outlook on the pious. The youngest brother of the Karamazov’s was the purest and kindest Christian. I hadn’t met anyone like him, but now I know people like him exist. I updated my model of the world by simulating experiences I haven’t had. It’s true that it was a fictional novel, but I trust Dostoevsky to get these things right.
This past year I read War and Peace by Tolstoy. Andrei Bolkonsky is a prince in War and Peace who claims to be an atheist and holds a nihilistic philosophy of the world. His view of the world is one where he acts and lives for himself, he acknowledges that this by extension means he will love his family and wife, but thinks it follows simply from his own self-love. Throughout the Napoleonic wars this shifts as he experiences pain and beauty in a way he had never felt before. There is a scene where after being injured he lies on the battlefield and looks up at the sky, overwhelmed by all the world has to offer and how he has never taken advantage of it.
I’ve kept thinking about the book, and not just this book but the experiences of others real and fictional. Why is it that so many soldiers who made it out of great wars physically and mentally together have radical changes in their life outlook? Why do refugees and immigrants have such different metrics for success? What is it about profound suffering, pain, struggle, and loss, that changes choices and views of the world?
The truth is when I’m unhappy it’s because my quality of life as a human across time and space has been something like only in the top 0.001%, when I wish it was in the top 0.0006%. Even if we collapse the time dimension, my life is still pretty good. Relative quality of life isn’t everything though, although it is important, and that’s where it gets interesting. Whether it’s in War and Peace or present-day Syria, the day-to-day trauma and suffering realigns peoples view on what is most important. We have all seen it in our lives as well, even if we haven’t experienced it, and we see how people change their view of the world following a grand or traumatic event.
So why do we all wait to change how we live our lives and evaluate our happiness? We know that we will have to re-evaluate eventually when we reach the painful experiences that lie waiting for all of us in the future (sorry). But if we look at those who have survived some sort of trauma, or those like Buddhists who practice detachment, or those who meditate, those who volunteer their time to those less fortunate, those who read and pay close attention to suffering, they seem to me to have a view of the world that helps keep them happier. We can use their experiences to train and calibrate our own brain on their data. If we simulate what they have gone through we can avoid deep regret or suboptimal choices in our own lives.
This is why I think War and Peace is such an incredible book. It’s a full simulation of the lessons of war and peace written down for you to learn from whenever you want. It is a story about individual men and women, and their search for meaning in a complex and messy world. The world around them is thrown into war. Tolstoy rejects the idea that this is a world driven by individual men and their quest for greatness. Instead the path of the world is unpredictable and detached from our best laid plans. In other words, they follow from a set of hyper-parameters that no individual can meaningfully change.
The character Pierre is a bastard who inherits a phenomenal amount of wealth from his estranged father. The arc of his character and who he is starts with his despair at having done so little with his life in comparison to his aspirations of greatness. As Moscow is lost to the French, he dreams a plan to kill Napolean, which he imagines will alter the course of history and in his sacrifice give meaning to his life. For unrelated reasons he is captured as a French prisoner, and meets a peasant who holds joy close to his heart and shares a potato with Pierre as they are locked in prison. As they are forced to march through Russia in retreat with the French in winter, his friend is shot when he lags behind, and Pierre is on the brink of death when rescued by a man whom he shot in a dual before the war. Sobbing, he hugs him and calls him his dear friend.
As Pierre regains his health his outlook on his own life shifts. In our lives this realization is never as profound. Tolstoy brings us close and walks us through, using his exceptional skills at telling stories along with his own experiences in war. We see that even in our machinations, there is beauty. Pierre calibrated his own view on the world. It no longer was an obsession to be one of the great, men who claimed to have been able to push a hyper-parameter in a certain direction and alter the course of humanity. It was about his own journey through life, and his discovery on the personal parameters that influenced him.
Tolstoy’s argument was that an obsession with greatness misunderstands how history unfolds and the roles we play. The purpose is to find value in our own individual sincerity. In my view, Tolstoy is right. He figured it out, and I’m glad he wrote it down for me to learn.
My fam took me on vacation recently to Mexico. I brought Dostoevsky’s novel “House of the Dead” with me for some beach reading. The novel is about character Aleksandr Petrovich’s experiences in a Siberian prison camp. The story isn’t plot driven, and instead focuses on Aleksandr’s relationships and experiences. Dostoevsky wrote this book following his own experiences in a Siberian prison.
Most of the book focuses on how the convicts find their own meaning through the mundane routines of a prison work camp. As well as Aleksandr’s reflections on how to improve as a person despite being a convict. At this point in my life it’s unlikely I’ll end up in a Siberian prison camp. I mean I don’t know that I won’t, but probably not. Dostoevsky’s four years in prison shaped the rest of his life. Losing his freedom was part of it, but it seems the main part was learning the line between good and evil was far less clear than those in prison vs. everyone else. The range of human kindness and depravity in the prison didn’t appear to be much different than In the outside world.
Unfortunately, I won’t be able to have those sublime discoveries as I’m not in a Siberian prison with Russian peasants. I’m similarly upset I never got the chance to be wounded in WWI and fall in love with a nurse like Hemingway. What a drag. Anyway, these are my favorite excerpts from the book. They are passages that captured Dostoevsky’s ability to learn what he wants from his life through his imprisonment:
1.) The thought once occurred to me that if one wanted to crush and destroy a man entirely, to mete out to him the most terrible punishment, one at which the most fearsome murderer would tremble, shrinking from it in advance, all one would have to do would be to make him do work that was completely and utterly devoid of usefulness and meaning. Even though the work convicts do at present is both tedious and lacking in interest, in itself, as work, it is reasonable enough: the convicts make bricks, dig the land, do plastering, construction; in this work there is a sense and purpose. Page 43
2.) “What did they send you here for?” interrupted one man who had been assiduously following Skuratov’s story.
“Oh, for going into the isolation blocks, for drinking vodka out of the barrels, for talking a whole lot of rot; so I didn’t really manage to make a whole lot of money in Moscow, one way and the other. And I really, really, really wanted to be rich. I can’t tell you how badly I wanted it.” page 117
3.) When the afternoon’s work was over, and I returned to the prison in the evening, weary and exhausted, a terrible feeling of anguish once again overcame me. ‘ How many thousands of days like this one still lie ahead of me’, I thought, ‘all of them like this one, all of them the same’. When it was already getting dark, I was wandering silently and alone behind the barracks, following the line of the prison fence. Suddenly, I saw our dog Sharik running towards me.
Sharik was our prison mascot, just as there are regimental mascots, battery and squadron mascots. The dog lived in the prison longer than anyone could remember, belonged to no one, considered everyone his owner and was fed on scraps from the kitchen. He was quite a large dog, black with white spots, a mongrel, not very old, with intelligent eyes and a fluffy tail. No one ever fondled him or paid him the slightest attention. From my first day, I stroked him and gave him bread out of my hand. When I stroked him he would stand quietly and look at me affectionately, gently wagging his tail as a sign of pleasure.
Now, not having seen me, the first person to fondle him in several years, for a long time, he had been running around looking for me among all the other convicts, and finding me behind the barracks came rushing towards me with a yelp of joy.
I don’t know what came over me, but I rushed forwards and kissed him, throwing my arms around his head; in one running leap he placed his forepaws on my shoulders and began to lick my face. ‘So this is the friend that has been sent to me by fate’, I thought, and every time I returned from work during those early sombre days, the first thing I did, before going anywhere else, was to hurry behind the barracks with Sharik jumping up in front of me, yelping with delight, embrace his head and kiss it again and again, while a sweet yet agonizing bitter sensation gnawed at my heart. And I remember that I would derive great satisfaction from the thought- as though taking pride in my own agony of spirit- that there was in the whole world left to me only one creature that loved me, that was devoted to me, my friend, my only friend- my faithful dog Sharik. page 140
4.) He was always quiet, never quarrelled, avoided all disputes as if from contempt for his companions, just as though he had entertained a high opinion of himself. He spoke very little, all his movements were measured, calm, resolute. His look was not without intelligence, but its expression was cruel and derisive like his smile. Of all the convicts who sold vodka, he was the richest. Twice a year he got completely drunk, and it was then that all his brutal ferocity exhibited itself. Little by little he got excited, and began to tease the prisoners with venomous satire prepared long beforehand. Finally when he was quite drunk, he had attacks of furious rage, and, seizing a knife, would rush upon his companions. The convicts who knew his herculean vigor, avoided him and protected themselves against him, for he would throw himself on the first person he met. A means of disarming him had been discovered. Some dozen prisoners would rush suddenly upon Gazin, and give him violent blows in the pit of the stomach, in the belly, and generally beneath the region of the heart, until he lost consciousness. Any one else would have died under such treatment, but Gazin soon got well. When he had been well beaten they would wrap him up in his pelisse, and throw him upon his plank bedstead, leaving him to digest his drink. The next day he woke up almost well, and went to his work silent and sombre. Every time that Gazin got drunk, all the prisoners knew how his day would finish. He knew also, but he drank all the same. page 56
5.) I also particularly enjoyed shovelling snow. This was usually done after blizzards, and was a very frequent occurrence in winter. After a twenty-four-hour blizzard, some of the houses would be covered with snow up to the middle of the windows, while others would be snowed up almost entirely. Then, when the blizzard had stopped and the sun had come out, we would be chased outside in large groups, sometimes all of us together, to shovel the drifts of snow away from the government buildings. Each man received a shovel, and a common assignment was given, sometimes an assignment such that it might well be wondered how it could be ever completed, and then all the men set to work simultaneously. The powdery, freshly fallen snow, slightly frozen on top, was easily shovelled up in enormous lumps which turned into glittering dust as we scattered them about. Our shovels cut straight into the white mass that sparkled in the sunshine. The convicts were nearly always cheerful when they did this work. The fresh winter air and the physical exercise warmed them up. Everyone grew cheerful; laughter, shouts, jokes rang out. Some of the men would begin to throw snowballs at each other, not, needless to say, without the ensuing shouts of the cautious prisoners, who were indignant at any laughter or jocularity, and the general animation usually ended in an exchange of violent abuse. Page 132.
6.) I remember how it seemed to me then: their desire for a just assessment of their performance was in no way self-deprecatory, but was rather an expression of their own personal dignity. The best and most outstanding characteristic of our common people is their sense of justice and their desire for it. The cockerel-like habit of always wanting to be first in every situation, and at all costs, and whether one is worthy of it or not – this is unknown among the common people. One has only to remove the outer, superficial husk and look at the kernel within attentively, closely and without prejudice, and one will see in the common people things one has no inkling of. There is not much that our men of learning can teach the common people. I would even say the reserves: it is they who should take a few lessons from the common people. page 191
It’s common knowledge that Seattle housing prices are increasing. I frequently hear that Seattle prices are currently in a bubble, and buyers should hold off. I also hear that houses are selling very quickly and for cash. On the other hand, it’s folk wisdom that demand is being driven by the tech sector. So if that’s true, then maybe it’s not a bubble. Or, maybe house prices are going up for good reason, but it’s also a bubble, but no one knows when the bubble will pop.
Any good model needs to have criteria for acceptance and falsifiability. A bubble is a nebulous term in finance, which appears to be a real phenomena, but is also extremely hard to measure. Robert Shiller’s careful econometric study of mean reversion in equity prices and valuations made a strong claim that there is some predictability in asset markets (when detrending growth and inflation). He attributes this in large part to ‘irrational exuberance,’ and the psychology of investors. This is hard to measure. When a new home buyer in Seattle is going out today to buy a home, is it based on rational expectations? Or is it because everyone else is buying homes, and he wants to get in before it’s too late? Shiller measured this as well, and found that people’s expectations of housing price increases (last in 2003) were much more optimistic than reality.
What is extremely interesting, is when controlling for inflation Shiller finds that based on his housing prices index, prices don’t increase that much. Since 1990 housing prices have only increased about 28% in real terms, which is orders of magnitude less than equity markets. Real estate though has a duality, as it generates wealth over time, either in the form of rental or consumption. Seattle is also often compared to San Francisco, and it’s true that the real-estate market has increased massively in San Francisco due to the tech boom. However, San Francisco is also surrounded by water and has extremely restrictive construction regulation both in the city and down near the Menlo Park region. In the short-run all cities are static, but Seattle can continue to expand, build, and create new neighborhoods.
While housing prices on average don’t seem to consistently increase, not dramatically at least, over the long-run, they do vary. Based on Zillow Seattle data to 2006, a one standard-deviation movement would be on average $40,000.
From here we know a few things:
1.) Seattle prices have been increasing for nearly six years straight, even when controlling for inflation.
2.) There is no strong evidence indicating that real-estate assets always increase in real value, as unlike equity there is no cash-flow or (measured) risk-premium
3.) Real-estate markets have high transaction costs, are very illiquid, and often rely on cyclical factors such as cost of credit.
Above is a graph of the Seattle Case-Shiller house price index, as well as the same index that I adjusted using the Seattle-CPI measure of inflation. Just eyeballing the chart there are two obvious hypothesis. The first is that Seattle housing prices are consistently increasing, probably due to the tech boom and it being an awesome city, with an exception for the housing-crisis. The second is that housing prices generally don’t increase, but do follow a mean-reverting random walk. We see the same thing using Zillow data, which only goes back to 2006, but has a better methodology using real micro-data.
To start I will just let the time-series speak for itself. I chose an AR(1) to model this data as a simple Gaussian distribution. Some other specification might be better, but the general point here is to show how the inflation-adjusted series is a stationary process. The question these models raise is whether the time-series process captures the dynamics of Seattle real-estate, or if there is some additional structural knowledge about the economy that means the recent increase isn’t just cyclical.
One explanation could be the leverage cycle. John Geanakoplos argues that there is a cycle of leverage driving asset price variation in addition to interest rates. If this were true, increases and variation in the Seattle housing market could be explained in part due to cyclical variation in credit. It’s hard to pin down the mechanism of action here. In retrospect the housing crisis came down to sub-prime mortgages. Asset prices due to a leverage cycle could be due to very small changes in the preferences of investors and homebuyers. As a starting point, I have plotted the R2 values of regressions of Seattle housing prices on the Bank of America US High Yield Spread. Using a high yield spread might show if something is going on as it proxies for credit-market risk, but won’t help in pinpointing causality. On this chart a positive lag represents a lag. So a lag of 6 means we are using a 6 month old Credit Spread to explain the current house price. A negative lag means using data from the ‘future’ to explain current house prices. Using the Zillow data, we see that the future of the high yield data explains around 50% of the current price. We know this was the case during the housing-crisis, so the core question is whether it is also cyclical on a lower scale, or only happens rarely during a crisis. If it only happens during a crisis, these charts are just picking up a structural issue, and have no use in understanding the dynamics of housing or the future. But otherwise, it could mean increasing house prices are able to predict increased risk in credit markets. This data keeps the question alive, but isn’t sufficient to answer it.
To finish, I decided to throw the high yield data into a vector autoregression of Seattle housing prices. These aren’t much different from the auto-regressive models, but I was interested in seeing if there were any joint-dynamics. A shock to the high yield series does lower average house prices by a few thousand dollars, but nothing massive. It would be interesting though to see what the dynamics are like if there were a better measure of leverage, ease-of-credit, and liquidity in the Seattle market. If Shiller’s research is correct, these variables could explain most of the movement in the house prices, which would be essentially mean-reverting.
What would be really interesting is if Seattle house prices do have an upwards trend, and by explaining the factors that are mean-reverting it would be easier to search for variables that predict price increases. This could be related to more traditional supply and demand factors. For example, if there are areas that are exceptionally nice in Seattle, living there could be a signal to others as to your value. In this case social-signalling and agglomeration could result in a few areas that are explained poorly by a model that captures the mean-reverting pricing factors. This would require a more granular view of subsets of the housing-market. My ultimate prediction is that Seattle house prices, in real terms, will decrease over the coming decade as more houses are built and new firms open offices in cheaper locations creeping the city away from the center. Bellevue has become a beautiful area, but before Microsoft it wasn’t particularly noteworthy. In addition, certain subsets of the market will increase massively due to specific economic factors. Whether those subsets are predictable, I am not sure yet.
Code I used to generate charts: Github
Shiller’s paper: https://www.nber.org/papers/w13553
Geanakopolos’s paper: https://www.nber.org/chapters/c11786
Washington Employment Data: https://fortress.wa.gov/esd/employmentdata/reports-publications/economic-reports/monthly-employment-report
FRED CPI Series: https://research.stlouisfed.org/fred2/series/CUURA423SAH
Zillow Seattle Data: zillow.com/seattle-wa/home-values/
FRED Case-Shiller series: https://research.stlouisfed.org/fred2/series/SEXRNSA/downloaddata
FRED Bank of America High Yield Debt Series: https://research.stlouisfed.org/fred2/series/BAMLH0A0HYM2
Lately I have been reading Karl Popper’s work on the scientific method in Social Sciences as well as following the German refugee crisis. These are just some of my thoughts on the dangers of thinking you are on the ‘right side of history,’ no matter how right it feels, which come straight out of Karl Popper’s essays. This isn’t a well vetted post, just my trying to sort out what’s been going on in my head.
We all want to be on the right side of history. Our study of the world seems to show a clear trend towards a better world, at least in developed nations. The 20th century stands out to me as both the most optimistic and most efficiently barbaric periods in human history. It was an experimental testing ground not only academically, but also for ideas of human collectivization and government. We take for granted knowledge we gained from these tests as having always been self-evident. In the U.S. we learned that testing diseases on humans without their consent is not worth the cost. This seems really obvious now, but at the time it was often rationalized as being worth it for the greater good. It’s easy to look back and think of these scientists as trivially evil, but they honestly believed what they were doing was right. For example, the Tuskegee syphilis experiment is a black mark in our history in the US. From 1932 to 1972 the U.S. Public Health Service purposefully didn’t treat syphilis in rural African-American men in order to study the disease. Our scientific knowledge in health gained from these experiments. The cost was a profound damage towards trust in government and social cohesion, not to mention individual suffering. It’s challenging to not think of this cost as being obvious at the time. Our view of morality feels mostly invariant to time, but it’s really not.
It is true that many ethical philosophies would have claimed this was unethical prior to the experiment (it wasn’t a controlled experiment, but it was an experiment in that we look back on it and study the outcomes). Of course, many could have claimed it was ethical as well. It wasn’t until all these experiments were done and the results measured that it was decided this type of experiment is unethical, as the negative outcomes outweigh the positive outcomes. Unethical experiments, race based legislation, eugenics, mandatory minimum sentencing, and many more experiments that we now view as unethical took place. Given the information at the time it was not self-evident these were unethical policies, it was only through experiment it became self-evident.
The right side of history could be classified as learning from these experiments, and preventing them from reoccurring. In my experience though it is typically used as a justification for a prediction, based on the view that the ethical choice is self-evident. Arguing that a policy or course of action is on the “right side of history” is claiming that before we even measure the outcome, it is clear that it is and will always be the correct choice. This is a clear distinction between predicting a policy or course of action will be on the “right side of history,” and setting criteria to evaluate the prediction at a future point.
Before the 20th century this same belief system that professed to always be on ‘the right side of history’ existed, but instead with an emphasis on God. Karl Popper wrote “Sinners against God are replaced by ‘criminals who vainly resist the march of history.” Whether it is a prophecy from God, or a progressive ‘march of history,’ these beliefs that social progress, ethics, or morality, are self-evident and have finally revealed themselves have historically failed. Christianity offered to solve the flaws of society, yet the Christian today follows a much different set of rules and values than Christians of the past. It seems then that the solution wasn’t clear. If the solution was clear, we wouldn’t have needed to keep learning throughout time, as the answers would have already been provided. Marx and Engel’s work, building on Hegel’s ethics, unveiled a utopia with promises similar to religion: If only you can follow this text, and follow it properly, the failures of society and humanity will disappear. Yet the failures didn’t disappear, and the dream of a set of rules that leads us to utopia and cures us of our flaws failed once more.
My favorite example focuses on the Soviet Union. The foundation of the Soviet Union found heavy support among the left academic elites in England. Journalist Malcolm Muggeridge was initially fascinated by the promises of communism. He wasn’t the only one. The British Fabian society consisted of the modern elites, including George Bernard Shaw (and the founders of where I studied, the LSE). He wrote that when stationed in Moscow as a journalist, he would play a game with other journalists to see who could convince the public intellectuals visiting from England the most outrageous stories. They would routinely convince visitors that the shortage of milk was only due to its being allocated most heavily to nursing mothers, thus making its shortage a good thing. The public intellectuals often believed this stories, because they deeply believed that Communism was the future of society.
George Orwell had a similar story. He essentially wrote the book for the ‘Left Book Club’ and Communist party. The first half of his book was well received by communists and those on the far left. The second half of his book did not prescribe communism as the answer. As Orwell documented the suffering of the working poor in England, his view was that the often one-dimensional and economically illiterate policies of the communist party could not solve these problems. It seems obvious to us now, but at the time he was viewed by many as being on the wrong side of history, and not supporting the cause. Orwell was still cautiously supportive of many policies associated with Socialism. Prior to great social programs, it was hard to completely reject an untested set of policies that pointed out that we ought to take some of the resources spent on the luxuries of the ultra-rich, and instead use them to provide healthcare to poor orphans. After testing various levels of socialism, we integrated some of the best and stand-alone policies.
Martin Malia, author of The Soviet Tragedy, points out that many academics in the 1960s and 70s attempted to explain the Soviet system “as the product of popular action, and hence as democratically legitimate.” This was necessary to preserve belief in communism. In this view Stalin’s rule was an aberration following legitimate rule, and the system could return to a “democratic and humane socialism.” This was the most widely adopted view, and as a result focused on what went wrong in the Soviet Union with Stalin, and how the system could return to its optimistic outlook. The prevailing view now would be that there is no optimistic outlook. Stalin himself wasn’t an aberration, but the mass murder was communism itself. As a result, it would be both ignorant and unethical to advocate for communist rule, as we now know the result.
Popper’s solution was to constantly try new policy experiments, mark our criteria beforehand, and continuously evaluate them. This would also mean we would spend more time analyzing the dynamics of social progress over the past 50 years opposed to trying to outright solve the problems of human ethics. This would create a more efficient way to evaluate our choices. It wouldn’t eliminate the need to make hard choices, as we still need to make uncertain choices. Admitting more refugees into Germany is a hard choice, and the historical evidence can simultaneously support the view that the cultural clash will damage Germany over the long-run, as well as the view that assimilation works and by taking in asylum seekers we are improving the world for everyone. However, we can decide now on the criteria to decide if the choice to admit refugees was correct or not. To start, all those who are in favor of admitting refugees need to set a series of empirical criteria that need to be met to have falsified their prediction. How bad would crime, rape, welfare-burdens, and other key metrics have to be for people like Merkel to admit they made a mistake? Conversely, those who don’t want refugees need to set their own criteria as well for their prediction on the failures of accepting refugees. How bad do they expect it to get, and if it doesn’t become that bad will they admit they were falsified? We could also formulate policy clauses that trigger based on the outcomes of certain predictions. As a very simple example, Germany could accept some refugees permanently, but many others on a potentially temporary permit. Over the next five years Germany could gather data and evaluate how well the program has worked, and as a result choose how many refugees to retain or send back. I offer that only as a toy example, but the greater point being even in the face of extremely challenging and unclear examples, we could create dynamic policies that we update based on pre-defined outcomes.
It’s impossible to agree on all policies, there will always be fierce disagreements. What we can do is try to agree on the right outcomes to measure beforehand. The less emotional and more methodological our debates become, the less prone we are ourselves to confirming our own biases and viewing those who disagree with us as idiots. The recent sexual assaults in Cologne are a big deal. The question now should be what systems can we set up now so as new information unfolds, we agree on what course of action we need to take, instead of having large emotional reactions.
Just under two years ago I started working at the Federal Reserve in Financial Research. My year at the LSE prior to the Fed had given me an intense year to begin thinking mathematically for essentially the first time in my life. After the LSE I wanted more academic training before I forever left the world of academia. At the Fed I’ve studied everything that interests me, both during work and outside of work, without obsessing how I will accurately signal my knowledge for my next job. I simply wanted to focus on becoming smarter. I knew that everything I was learning in the world of computational finance, scientific inference, statistics, programming, and math, was valued in other jobs. As a result I worked on plenty of personal projects, completed lots of segments of Coursera courses that were useful, and began to enjoy my new abilities to efficiently learn new disciplines. These two years have been great. Now it’s time to start shamelessly signaling. It is challenging to find a balance between developing my own skills on my terms, and also remembering to find credible ways to prove what I know to others. Time to restore balance and abandon life as San Francisco Economic Monk, and enter the world of Business.
For my own sake, here is a list of everything I’ve done and taught myself at the Fed (both in and out of work) over the past two years.
When I began working at the Fed I had a weak foundation in mathematics. It appears to me that the majority of Economics and private sector quantitative work sticks to linear models. The Financial Economics team I am on at the Fed is one of the more mathematically intensive teams at the Fed, since yield curves and asset pricing exists in the world of Stochastic Differential Equations (SDEs), continuous time, and filtering.I began with multivariable integrals. This was a topic I had briefly covered before, and needed to be better at in order to not struggle with probability distributions. I primarily used Stewart’s textbook, which doesn’t go into proofs but focuses instead on how to compute problems. I spent about 100-120 hours just doing integrals by hand this way after work. I then moved on to ordinary differential equations (ODEs), because this was the technique used to model the yield curve (technically the yield curve is modeled with SDEs, but they reduce to ODEs with a few theorems).
My first goal was to learn how to solve thisFor the first six months of living in San Francisco, before Alison joined me, every weekend I would wake up and then walk to the park near my place and lie on the grass doing math for a few hours. Eventually I decided I wanted to be even better at math, and began studying the proofs underlying calculus. I’m not sure if this was a good use of time. I had hoped it would give me a richer ability to follow practical applications, which I’m not sure that it did. At least it gave me a small peak into the beauty of math, which I hadn’t appreciated fully.
Otherwise I continued to become better at Matrix Algebra, both because I used it at work and in its practical usage it’s really not that hard. I spent about three months teaching myself the intro to SDEs and brownian motion in 2016, since I wanted to have a better understanding of the models that we work with at the Fed.
2.) The Paper:
At the Fed my boss Jens Christensen put me as a co-author on a (working) paper called The TIPS Liquidity Premium. This is an incredible paper that attempts to filter out the liquidity premium on Treasury Inflation Protected Securities issued by the U.S. Government. When we succeed, we will have the least biased measure of the U.S. real rate in the field. This is an interesting question because the TIPS market has historically not been as traded as normal Treasuries. This means if you buy a TIPS, you are assuming some risk in the form of fear that in the future if you want to sell it immediately you may not be able to find a buyer. And if you desperately need to sell it, you can only find a buyer by lowering your price below its true value. As a result, because of this fear, the buyer of a TIPS from the U.S. Government will require a slightly better price to compensate them for this risk. For example, today the 5-year constant maturity TIPS is trading at a yield of 39 basis points, and the 5-year Treasury at 176 basis points. The difference is considered to be break-even inflation, which is what inflation expectation has to be for the asset prices to be in equilibrium with one another. The TIPS is here representing the real rate of intertemporal substitution in the US economy.
I rewrote the computational heart of the model from R to C++ to improve the speed. This has been my biggest project over the past two years, and the one where I have learned the most. I began working at the Fed barely able to code in R. I had a project that required me to take a heavy filtering model in a language I barely knew, and rewrite it in a language I didn’t know at all, at a point where I had barely even learned how a loop works. I started with ‘Hello World’ in C++, and ended with a few hundred lines of dense mathematical code in C++ that I compiled into an R package. In between was a year of nights and weekends of constant frustration with occasional profound excitement.3.) More Asset Pricing:
Since I worked on an asset pricing team, I spent a lot of time outside work trying to learn the foundations of the field. I worked a lot out of John Cochrane’s book on Asset Pricing, and did about four weeks of his Coursera course. This took a long time since I had to spend a few months first learning more time series and stochastic processes to follow along. I also read the seminal work on return predictability in equities, and some in bond prices, by guys like Campbell, Shiller, Fama, and Cochrane. I studied yield curve modeling from my bosses paper and the book Yield Curve Modeling and Forecasting by Diebold and Rudebusch.
I have mixed feelings about this field. Financial markets are extremely interesting, and the years I have spent working in investments, studying economic theory, financial institutions, political economy, and pairing this with working in academic financial economics, I have developed a robust and deep knowledge of asset markets and investments that is more advanced than almost all who work in private sector investments research. While many in the field are better at the coding and math than I am, my time studying and working at the Fed has given me an advanced understanding of financial market reasoning and theory.
What does shock me is how rich the literature on predictability is, yet how little investments ‘professionals’ know about these papers. If your goal is to predict asset prices and beat the market, shouldn’t you have at least a great base knowledge of the academic literature, what works and doesn’t work, and where the current research is in this field? This isn’t necessary just for quant funds, but even for endowments or fundamental funds. For example, if I worked at an endowment or fundamental fund I would (and could) recreate the main valuation signals from Shiller’s papers. Even if investing in individual stocks, wouldn’t it be useful to have a program that uses time-series techniques to report on historical and mean-reverting processes of market valuations, and estimates the parameters to give a context within which to talk about individual companies or stocks? This would be even more useful for endowments, which invest across all asset classes.
4.) Econometrics and Time-Series:
I have spent a reasonable amount of time studying Jim Hamilton’s PhD textbook on Time-Series. I began with the foundations of ARIMA models, and eventually moved on to the Kalman Filter, which we use in our model at the Fed. I’ve also continued to study experimental design, by finishing reading and understanding the book Mostly Harmless Econometrics by Angrist and Pischke. I combined this with studying Asset Pricing to code up a toy-model to forecast the price of a short-VIX ETF.
I chose the short-VIX for a few reasons. The first is that the VIX is based off of option prices for the S&P500, meaning it is a mean-reverting process. The second is that the VIX is an asset that increases as expected variance in the future price goes (this is correlated with market fear, but not the same thing). This means that it is similar to insurance, which requires the buyer pays a risk premium. As a result the short-VIX receives a risk premium for selling market insurance. Lastly, selling volatility insurance is extremely risky for investments funds, as it means when the market does poorly they will have to pay out ‘insurance’ to other funds: In short, when the market does poorly, they will do the worst. I hypothesize that for this reason the risk premium received for selling volatility insurance is very high. Using this thesis, I coded up a relatively simple rolling ARIMA model to forecast the future price of short-VIX, which I’ll post to my website eventually. I’ve recently read more on Options, Futures, and Other Derivatives by Hull. It would be a fun challenge to recreate this model, but instead of using the SVXY ETF, to build it from scratch using the S&P500 options values. I might work on this with my brother at some point, as we want to build some investments strategies together.
I have also studied Bayesian Computation with R by Jim Albert. I only spent a few weeks on that book, but it was great to finally code up the basics behind Bayesian prior and posterior modeling. I hope to have an opportunity someday to learn more about pure Bayesian statistics. At the moment, I’m investing my efforts into machine learning algorithms, which leads me into my next category.5.) Data Science and Machine Learning:
I want to stay on the West Coast, and I really want to live in Seattle. The most booming industry over here is in data science. A poorly defined field that requires people who know statistics, computer science, and scientific inference. It sounds like a great fit for me, as I have been using a mix of the scientific method, causal inference, programming, and math, for the past three years. What I have learned is that the biggest challenge is that economists are never as good at coding as a software engineer and not as good at statistics as a Statistician. In general, we are better at asking the right questions and setting up good scientific practices and research design. Because ultimately equations and code are tools used to answer questions, but can’t solve anything by themselves.
For the past five months I have been studying data science tools. At the Fed we rebuilt our data infrastructure to use Python to query PostgreSQL databases. On my own time I have read a lot of machine learning textbooks, and academic papers on models (i.e. Random Forests). And recently I competed in a Kaggle data science competition, landing in the 40th percentile. Nothing to brag about, but not awful for it being my first competition, in a new programming language, using new models. I am confident I will do much better in my next one, as I spent about 100 hours struggling through lots of easy issues.
I am now doing a Stanford Coursera course on Machine Learning for a certificate. I’m annoyed because it’s very easy so far, as I’ve already taught myself most of these foundational topics. Unfortunately, I have no way to credibly signal it yet, so I have to play the game. As I finish this course and clean up my code from the Kaggle competition, I will have a credible signal that I can do Machine Learning. It still feels like I’m new at it, but with my background at the Fed I underestimate how fast I pick this stuff up compared to others.
I could barely code at all when I started at the Fed. I’ve learned an incredible amount over the past two years. I’m proficient in R now, and able to write fast code within the realm of data cleaning, analysis, and modeling, without having to google too much. I spent a lot of time coding in C++ using mathematical libraries, and learning how to address inefficiencies in R by creating new functions in C++. Again, this isn’t traditional development coding, but coding as a tool for quantitative scientific work.
Despite spending on average five hours coding a day over the past two years, I have done it all by figuring things out as I go, and spending most of my time working with data and models. Over the past half year, I have been coding in Python. I’m still not as efficient as I am in R, but I have been learning far more about what it means to be a true programmer since I started working in Python. At work we have been tackling problems that involve rebuilding production code, which has given me a chance to learn how to write stable automated code outside of simply modelling.
I’ve spent a few weeks studying basic algorithms, and learning best practices when programming. If I want a job in data science, I will need to spend at least 3-5 months studying data structures and algorithms. This will also help me in the long run, as currently most of what I know I have just picked up and never properly studied.7.) History and Journalism (and Russia):
I have written a few blog posts on this topic, and it’s a field I find fascinating. This year I read Homage to Catalonia and The Road to Wiggan Pier by George Orwell, as well as his published journals and letters. I also read Chronicles of Wasted Time by Malcom Muggeridge’s, who was a British journalist, as well as a spy during WWII. Both were men documenting the political environment of Britain and Europe during the 20th century. They both wrote heavily about Russia and the Soviet Union, as well as the misguided communist sympathizers on the left. It’s incredible how wrong the left was on the success of communism, while all the intellectuals at Oxford and Cambridge opined on how progressive it was as a state model. It’s easy to look back as Muggeridge and Orwell having simply been smart men, but at the time they were widely hated by both nationalists and communist groups. Many intellectuals had disdain for their work. Despite this, they abstained from excitement over ideologies and movements for excitements sake, and instead worked intensely to travel the world and base their worldview off of the empirical reality. Muggeridge saw first hand the starvation of the Russian people, and no rationalization by the intellectual left would change his view.
My interest in these two writers interestingly lined up with my reading of War and Peace and Anna Karenina by Leo Tolstoy. The first gave an incredible account on the will of the Russian People. Anna Karenina devoted a large amount of time to the failing structure of agriculture in 19th century Russia. Having moved from serfdom, to sharecropping, and then to collectivization under communist rule. A failure that would pave the path for the starvation of untold millions, and the rise of totalitarianism.
This journalist and novel account of communism and Russia resulted in my talking to a professor of Development, who specializes in Russian history, and recommended I read The Soviet Tragedy: A History of Socialism In Russia, 1917-1991 by Martin Malia, which I have not yet finished.8.) Syria:
The tragedy in Syria has no end in sight. I have followed it intensely for the past few years, keeping track of the different groups fighting within the country. As I have read the news, I have also paid close attention to the social media space of information. On reddit there is a subreddit called SyrianCivilWar, which posts twitter accounts, youtube videos, and primary source blog posts, tracking battles, rumors, and information. Having followed this for some time, it’s incredible how much more complicated the reality is than what is presented in the news. To an extent this is understandable, as the news must condense information. The issue is that the complexity of the sites, the reasons for fighting, the massacres, and the conditions for peace, are highly dimensional. There is no clear policy to solve this issue.
The foreign policy status quo has been something as follows: Identify the bad guy. Make sure he’s not making new alliances with other big bad guys (e.g. Russia or Iran). Find guys who are fighting the bad guys (call them moderates). Give moderates guns. Bomb bad guys. Threaten big bad guys to stay away.
This strategy worked to an extent during the Cold War. By that I mean it drained the Soviet Union of tons of resources (e.g. Afghanistan), which some argue resulted in the collapse of the Soviet Union. But it has yielded no obvious dividends in the 21st century. Each time our status quo analysis has missed a highly dimensional complexity, involving clashing value systems, cultures, and our own desire to completely solve the situation. In this case Assad is the ‘bad guy’ working with Russia ‘the badder guy.’ And the amount of civilian murder Assad has presided over absolutely means he has secured himself his place in hell, if such a place exists. On the other hand, Assad is the only hope for a secular government in Syria, and still has the popular support of the people under his control. Without Assad, it is almost certain an Islamist government would take power. Russia realizes this, and also realizes this would mean either more terrorist attacks within their country, or a new Western puppet government in Syria (less likely). Iran realizes a destabilized Syria, with an Islamist government, would threaten their security as well. Surprisingly, they aren’t evil countries who scheme together, but are instead looking out for their regional interests.
The answers are not obvious, but neither are they simple. Imposing a no fly zone for Russia, bombing Assad and ousting him out of power, and putting some US troops on the ground, will literally not solve a single issue.9.) Philosophy of Science:
This is the ace in my sleeve. It is the intellectual foundation of how I hope to be better at modeling the world than my competition. Scientific inference within the face of massive uncertainty is hardly taught at all within academia. It’s not taught at all in engineering disciplines, which instead exploit the logical rules of the universe. Programming follows logical rules, but doesn’t rely on inference. Within Economics it is taught more than the STEM fields, but even within Economics it is not formally studied. It is instead taught by osmosis through reading the great academic papers of the past. This is usually not enough, which explains why most academic research is shitty regressions with zero citations.
Not surprisingly, studying the connection between making scientific inferences on the world and combining it with statistics is extremely challenging. It is incredibly common for a researcher to pose a question, gather data, and fit a model to the data, only to present it to a roomful of scientists who all aren’t convinced of its accuracy. I’ve seen it happen all the time at the Fed. But on what basis do these scientists not find it compelling? They can’t prove the model is wrong, but they have a hunch that there is something missing. This hunch is based on their own empirical experiences, where they know you can make a model sing however you want if you torture it enough.
This happens more often in quantitative consulting and analysis. It is easy to ask a question, guess what the answer could be, find data and make it match your guess, and then turn it all into a neat story to sell your model. If those you present to don’t have a great background and intuition, they are likely going to look at your t-statistic and think “golly-gee, what’s significant! The model must be true.” When, the truth is, everything that has happened is probably just a step above randomness.
This year I have been reading Against Method by Paul Feyerband, Conjectures and Refutations , The Poverty of Historicism, and The Logic of Scientific Discovery by Karl Popper, and Error and the Growth of Experimental Knowledge by Deborah Mayo. I have also followed Andrew Gelman, Scott Adams, and Scott Alexander’s blogs, who cover the scientific method, statistics, and empiricism. Mostly Harmless Econometrics also focuses on issues of Causal Inference, which I was first exposed to at the LSE.10.) Success:
I have taken my goals of the pursuit of knowledge and rigorous skills very seriously these past three years. And I have been lucky to have been accepted to the LSE and then the Fed. Now I’m preparing to leave the world of academia, and work in the private sector (although at the Fed we did a lot of faster paced policy work). I want to live in Seattle again, and for much of this year despaired that it couldn’t happen. That the jobs I want are on the East Coast, that there aren’t the right jobs in Seattle, or that I’m not properly qualified to get jobs in Tech, as they don’t get many applicants from the LSE or the Fed. It was too easy to let myself become discouraged, and I’m working on ending that style of thinking. Finding a fulfilling job in the right city isn’t easy, but it’s not impossible either. After reading How to Fail at Almost Everything and Still Win Big by Scott Adams (author of Dilbert) I decided to form a new outlook. I’m usually not the type to read self-help books. But Scott Adams has the right mix of cynicism and optimism that appeals to me, without being too heavy on the ‘self-help’ garbage.
He writes a lot on our own psychology, and how we often form negative outlooks based on our own guesses of the odds of success, despite the fact that we rarely get the odds right. I assumed I couldn’t get a great job I want in Seattle, because I already ‘knew’ that all the jobs I want are on the East Coast. It’s certainly true the easiest and highest paying jobs for me to find would be on the East Coast. However, it’s also true that Seattle is a pretty big city, and I don’t know everything that exists. I’m working on forming a more positive and optimistic attitude. There is no reason I’m unable to find a great job in Seattle. I might have to work harder to find the right team, and network harder, but that’s within my abilities.11.) Final Thoughts:
I spent just over the first half of my twenties trying to learn as much as I possibly could. It was rough graduating with a Finance degree into a crippled job market in Seattle. My greatest fear was not being able to learn more in the fields I found most interesting. I had a deep admiration for analytical scientists and economists who were able to not only come up with clever questions, but to implement their own answers using a mix of statistics, modeling, and programming. While I will continue to improve for the rest of my life, my time at the LSE, the Federal Reserve, and my own auto-didactic ways have taken me to a rigorous foundation.
Now I need to not only refine these skills, but learn how to market and signal them in order to build my own career progression in the private sector. I’ll succeed at this too.
Tolstoy wrote War and Peace as a retort to all the armchair European analysts of his time, who wrote and discussed Napoleon’s conquests, and his failure in the Russian winter in the year 1812. Their analysis always sounded reasonable, and explained all the events by attributing clear decision making and brilliant analysis to Napoleon and his enemies. Tolstoy’s problem with this was that he saw all the intellectuals of his day as just observing all that happened, and then crafting a nice story as an explanation. A grand success from the French was due to Napoleon’s sheer brilliance. A failure was due to a tragic mistake, or a stroke of misfortune. The messy complexities of actual reality were smoothed over. Tolstoy saw it differently, and even suggested that Napoleon wasn’t particularly great. He viewed Russia’s success as a fluke. In his view the entire course of history is chaotic, and since we miss so much of what truly happens, we convince ourselves the course of history is set by the grand commanders and pivotal moments.
I sometimes wonder if we make this same mistake with Syria. We have our major players: The Assad regime, ISIS, the Free Syrian Army (FSA), Al-Nusra (al-Qaeda), the Kurds, Turkey, Iraq, Iran, the US, and now Russia. We then look to each player and ascribe strategic intentions, and use that in our analysis. What is interesting though is there seem to be two separate areas of information, with only slight overlap, on Syria. The first is the standard journalism information, and the second is from non-professionals scanning and interpreting the vast amount data posted by combatants and civilians on social media. The subreddit /r/syriancivilwar aggregates the most interesting data, which is usually far more personal and noisy than the stories on NPR or the NYtimes. On the other hand, they are often videos of fighting or discussion, or personal thoughts. The more I read and watch the more confusing Syria becomes. Things rarely become clearer, and it’s obvious they aren’t that clear for people on the ground either.
What is the Free Syrian Army? Is it really a moderate group of soldiers working together against oppression? To me it seems like a term we have assigned to groups of rebels each fighting to protect their cities from outside threats. What is the al-Nusra front, and what is their goal in Syria? The Institute for the Study of War argues that smaller opposition groups might ally themselves with larger groups (like al-Nusra) as larger outside threats approach. Part of the problem is when we speak about Syria, we assume there is a semblance of organization and communication that makes belonging to a faction simple and systematic. I think an important question is to understand what drives Syrian civilians to affiliate themselves with a group. My impression is that if you are a man, and not in a major city, you need to form a militia to protect your town or village, since without any formal protection you are essentially living in anarchy. These are then characterized as ‘opposition’ groups, and somewhere along the way they are clumped together with the FSA, or ignored.
I worry that the analysis and stories that we hear on Syria misunderstand the entire country. While there are concrete issues with Russia involved, in terms of understanding the civil war itself, it is closer to anarchy than anything else. And if that’s the case, I think trying to ‘arm moderates’ or support the right side won’t work, since there is no clear side or organization (although there are wrong sides, namely ISIS and al-Qaeda). I think the only end to the war will come when some person or group gains overwhelming power, and can create an authoritarian government.