Author Archives: theusernamewhichismine

The log in your own eye?

There’s someone I love who really needs help but doesn’t want it. There’s an old saying about removing the log in your own eye before criticizing the speck in someone else’s eye. But at the same time, if someone just doesn’t see their problem, isn’t if your duty to try to help them? I can’t fix everything about myself, especially not all at once, but I can at least try to help someone fix themselves while also trying to fix myself. I don’t know what to do though, it’s hard to see someone not get the help they need especially if you love them, but it can destroy a relationship to try to help someone who doesn’t want it. I really hope I can help

Friday feelings: the importance of communication

I don’t want to get into too many specifics here, but this week was a lesson in the importance of communication.  Science is a collaborative process, the days of one person making discoveries have long since passed, and everything we do these days requires not just a team but multiple teams working in tandem.  With that comes a requirement for all teams to be on the same page so they can work together instead of going in circles.  My team recently received a sample to test but has no idea what the sample is or how it was produced.  Without this knowledge, how can we know what is in the sample?  If I see something odd in the sample, how can I know whether it’s important and must be removed or whether it’s a normal and expected part of the process?  And importantly, how can we replicate this work in our own lab if we don’t know how it was produced in the other lab?

Collaboration is of course difficult, we all have our own things to do any communicating to our collaborators sometimes only helps them and not us, so we don’t want to spend energy on it.  Still it’s necessary if a collaboration is going to work and collaboration is a thing that helps all of us. 

Just as important to collaborative communication is scientific communication to the wider community, usually through papers.  I’ve recently thought that scientific journals should also increase the standards to which they hold paper writers, too many will publish inscriptible images and vague methods that cannot be replicated at all, with your best bet in this case is usually the arduous process of calling the original scientist on the phone and asking him or her what the hell they did.  It’s like if you read a recipe and all it said was “cook it until it’s finished.”  What the hell does that even mean?  If you read a paper and you don’t know how the method was done, how can you ever build off that paper?  I’m not trying to accuse people of scientific misconduct or anything, I’m just trying to say that if I have no idea what you did, I’m not going to cite your paper or use it for my own research.  Good communication is important.

Can you beat the stock market?

Since my stock posts tend to get the most traction, let’s try this one.  I wanted to post because I was recently made aware of the Efficient Market Hypothesis which essentially states (in its weak, semi-strong and strong forms) that you cannot beat the stock market.  The weak form states you can’t beat the market using prior performance, the semi-strong states you can’t beat the market using prior and current performance, and the strong form states you can’t beat the market using insider knowledge.  Essentially weak = Technical Analysis is useless, semi-strong = fundamental analysis is useless, strong = insider trading is useless.  Taken together, these hypotheses seem unappealing to a day trader or stock picker, as they suggest the only winning move is the boring play of buying whole-market ETFs.  And yet that also creates a weird contradiction because if everyone believed the Efficient Market Hypothesis, everyone (including banks, hedge funds, and investment groups) would just buy and hold whole-market ETFs and never trade stocks individually.  There would essentially be no stock market in that case!

But getting back to the hypothesis itself, why would it be true that you can’t beat the market?  Let’s start with the weak and semi-strong forms, which only make statements about publicly available information.  The hypotheses in this case are yet another statement about the wisdom of the crowd: all of us are smarter than any one of us.  If you try to use available information to guess the next moves of a stock, you will find that the next moves are already “priced in” because the market beat you to it, and so there is no way to buy low + sell high.  Before you want to buy the price will go up, and before you want to sell the price will go down because the market is always faster and more accurate that the individual.  On the face of it this seems like the joke about the two economist walking down the street: one says to the other “look, a 20$ bill on the sidewalk!” and the other says “ridiculous, there are no 20$ bills on the sidewalk, someone would have already picked them up!”  The fact is that there always has to be someone who was first to use some particular information, and does that let them beat the market?  On the other hand this hypothesis isn’t talking about individual events but averaging across all possible events.  Yes you may have bought early this time, but you can’t consistently buy early and so you’ll buy late and lose as often as you buy early and win.

As to the strong form of the hypothesis, it’s the least defensible because remember it basically states “you can’t make money via insider trading.”  The conceit is that in this case any insider information isn’t purely such, and the wisdom of the market can “price in” insider information thanks to the constant stream of rumors and leaks that even the tightest-run ship is subject to.  Still strong-form hypothesis proponents were quick to point out that this doesn’t necessarily mean insider trading shouldn’t be illegal, it can still be true that the actions someone will take in order to perform insider trading are harmful and so insider trading should be banned.  Hiding bad or good information, making very short term decision to boost the stock at the expense of long-term corporate health, these are all bad things, even if the people making them can’t actually make money off of them, the fact that they think they’ll make money is reason enough to ban insider trading as a practice.

So to finish this ramble, I don’t know if I believe the efficient market hypothesis.  The weak and semi-strong forms obviously seem the most defensible, but it’s important to remember that many well-regarded stock traders with long histories of success don’t believe it.  And what’s true in mathematical economics isn’t always true in reality

The Chapwood Index is a very silly model of inflation

With inflation nearing double digits this year, so called “experts” have been crawling out of the woodwork to proclaim the Death of the Dollar and how they always said inflation would kill us all.  Most of these people make claims with no regard to reality, inflation is bad today and they say it will kill us all.  But 10 years ago when inflation was miniscule they also said inflation was bad and would kill us all.  The facts don’t matter, only the hatred of inflation.  Inflation is high?  We’re all gonna die.  Inflation is low?  It’s actually high.

I can understand the feeling of course, it doesn’t feel good knowing that year after year your money loses value.  But that feeling doesn’t translate much into reality, most Americans gained wealth in real terms from 2010 to 2020 (a trend only reversed around 2021).  So when your feelings of inflation conflict with the reality of inflation, what do you do?  If you’re Ed Butowsky (inventor of the Chapwood Index), you declare reality to be wrong.  You instead make up your own basket of goods (the Chapwood basket) and send open ended surveys asking people to track the changes in those prices.  And if your basket is unusually weighted towards such things as golf club memberships and financial planner’s fees, then yes you could show some strangely high inflation between 2010 and 2020 as the price of those things went up.  But the much larger and more representative basket from the Federal Reserve showed enough price drops in other things that it evened out into low inflation.

In the last decade you might have repeatedly heard calls about how the Fed and the Government are lying about the true rate of inflation, how everything is getting more expensive by double digits and how it’s destroying American wealth.  In the same breath these people probably tried to sell you gold, but that’s neither here nor there.  The point is that many people have tried to argue that the Federal Reserve is lying about the economy and that everything is going to hell in a handbasket, only “secretly” so that none of us plebs realize it.  This year as the economy has actually been rocked by inflation, those voices have been completely overwhelmed, because it’s very clear that genuinely high inflation feels nothing like the low-inflation period that characterized the last 10 years.

Basically the Chapwood Index (and indices like it) was designed to “prove” the point that America had super high inflation, to the tune to 10%, yet the index was completely ridiculous on the face of it.  If America experienced double digit inflation from 2010 to 2020, and if the nominal GDP numbers were accurate, then we would have experienced a real GDP decrease of around 50%.  That means 50% less total everything produced by the economy, 50% less cars, vegetables, and doctor’s appointments.  Yet this flies in the face of actual evidence showing a moderate increase in American production over that time frame.  Simple put, the evidence isn’t consistent with a prolonged decrease in real output.  Again, compare this to 2022 when America is experiencing actual inflation: nominal GDP is up by near double digits, but since inflation is also up by nearly that amount, the total American economy may have contracted slightly by the end of the year.  Super high inflation has been coupled to super high nominal GDP, and it’s still an open question as to whether inflation or GDP will win the year, but it’s clear that this year feels different than all the years from the last decade when people screamed about hidden inflation and buying gold.

Basically what I’m saying is inflation isn’t really missable, if it’s there it’s there and people know it.  Everyone knew the price of goods was increasing in 2021, but the Fed and the government acted slowly because they predicted the increase would be small and “transitory.”  But when inflation jumped to 4% and now nears 8%, it was obvious and didn’t require creating a whole new index just to see it.

How can you fix science that has become engineering?

One of the toughest questions in science is simply “when do you admit you were wrong?”  It’s never an easy thing to do, but we all understand that in the scientific method sometimes our most beautiful, most beloved hypotheses turn out not to describe the world as it truly is.  But people are human and it’s only natural that they’d prefer their favorite hypothesis to be right, and of course there’s always the possibility that just around the corner is some new evidence that will finally prove them right…

This process of clinging to an unsupported hypothesis in the face of repeated failures is something I discussed in a previous post.  There, I discussed working in a lab where we treating our hypothesis more as an engineering problem, we felt we knew that what we were doing was possible if only we could do it right.  Repeated failures never swayed our view of this point, and rather than admit it might be impossible, we would just double down and try again.  When that sort of thinking infects a lab, how do you treat it?  How do you get scientists to go back to being scientists, to go back to accepting or rejecting hypotheses based on the evidence and not taking them as gospel prior to even doing the experiment?

I think one thing that might help this process would be a revolution in the publishing industry in which null results would be considered publishable.  Right now it is very rare to get a paper published that says “we failed to prove something new.”  Novelty is desired, overturning the established paradigms is desired, and failing to accomplish either basically condemns your work to the trash bin, totally unpublishable.  I have often thought that null results should still be archived, if only to tell future scientists where the pitfalls lie and dissuade them from wasting more time on a fruitless endeavor.  But until null results are as publishable as positive results, people will still have a substantial interest in redoing failed experiments just in the hope that this time it will succeed, to do otherwise would force them to admit defeat and start all over from the beginning.

Games that play themselves

There’s a certain type of game I really really like.  It doesn’t have a good category, some games of this type would be called “management,” others would be called “strategy,” but what makes them enjoyable to me is that they’re the types of games where you struggle mightily to do every task the game throws at you, but by the end of the game you have developed systems in which the game basically plays itself.  Let me give some examples.

Factorio is the game that most comes to mind in this.  For Factorio the key word is “automation,” you start the game crash-landing on an alien world and have to hand-mine and hand-craft every single item you’ll need to survive.  Anything you want to build you have to place one by one across the world as well, and so the early game consists of running around mining, crafting, and building hundreds of things by hand.  The goal of the game is to defend yourself from the aliens and launch a rocket ship to escape, but as you progress closer to the rocket everything you want to build or research becomes exponentially more expensive and difficult. 

The trick is that the game gives you systems that you can do to make everything exponentially cheaper and easier.  This biggest game-changer is the ability to create little robots that can perform just about every job for you, and by that point in time the game almost feels like it plays itself.  You can put down big blueprints of what you want to be built and what you want to be crafted and the bots will do everything for you.  Need more resources?  The bots can build mining bases.  Need more science?  The bots can build your labs.  Suddenly everything you had been doing by hand can be done for you and the feeling is just so liberating that I often like to sit back and watch as the bots do everything for me.

The other game that comes to mind is Victoria 2.  Now this game is completely different, it’s not management but more strategy.  Victoria 2 puts you in control of a historical nation starting in 1836 and tells you to guide their destiny from the 19th into the 20th century.  Want to industrialize Japan and become a world power?  You can do that.  What to unite Italy into a single nation?  You can do that.  What to play as France and enact your Napoleonic fantasies?  You do you man, but you can do that. 

The important point is that at the start of the game your nation will normally be poor, illiterate, and un-industrialized, even the nations of Europe were like this in 1836.  This means that there will be tough choices to be made in order to grow your economy, educate your populace, and industrialize your society.  But doing all these things makes the game easier and easier, until by the end of the 19th century you’re likely to be rich, highly educated, and highly industrialized, at which point you can make lots of money even with a fully-funded state apparatus, and capitalists will run around building whatever factory your country needs before you can even ask.  By the end of the game, it is almost playing itself in this way.

I don’t know exactly why I like games like this.  Maybe it’s just about the feeling of liberation you get when something that used to be so hard becomes easy to you, but for whatever reason I really really like games like these and would be happy to be recommended more like them.

How do you read in a language you only half understand?

Whenever I learn a new language, there always comes a time when I start to get good enough at it to recognize and understand certain words, but not good enough to know every word I come across.  I can read half a sentence but not the whole sentence, understand half a paragraph but not the whole paragraph.  This is a difficult time for a learner because you’re just on the cusp of truly using the language to read, but you don’t feel good enough to actually use it because you only understand half of what you read.  How do you get better?

The answer (so I’ve been taught) is you still try to read.  Even if you don’t understand everything, even if you only understand half of it, you try to read what you can so you can get familiar with the language and start learning by using.  Most words we know were probably never defined to us specifically, did anyone ever define to word “anyone” to you?  Instead as learners we pick them up by context clues and other hints, and start using them the way we read or heard them.  This can occasionally lead to hilarity, like how I once heard someone describe a child as homely instead of comely, but it can also lead to learning as you start to use and understand each new word you read.

So if I’m reading something and I come upon words I don’t understand, I was taught not to look each one of them up, but instead to just keep reading and try to figure them out as I go.  I may read a sentence that says “he went to the 餐厅, and after he’d finished his meal he…”.  Although I don’t know what 餐厅 means directly, it seems that “he” ate their, so it must be some sort of eating place.  Now whenever I see that word again I see if it seems to have something to do with eating, and if it does then I can learn by usage that 餐厅 means “a place where you eat.” Through this process I can slowly pick up the language through usage rather than trying to stop and look up every word.

But here’s the secret: this trick also works with scientific writing.  Scientific writing is filled to the brim with jargon and odd definitions.  What is an SDS-PAGE?  What is an HPLC?  And not only are the words difficult, the concepts are difficult, why did they use centrifugation to separate out the nucleus?  Why does electron microscopy not let you visualize the less-rigid parts of a protein?  When you start out as a scientist, you are often told to read scientific papers, and scientific papers can feel like you’re reading a foreign language!  But the same rules apply as reading a foreign language, you don’t always have to know every word when you’re starting out, or even every concept.  It’s more important to develop scientific language fluency so that you can get the big idea out of a paper and understand it when speaking with others.  For example, they used HPLC to separate a protein of interest from all the other proteins in a cell.  OK so HPLC is a purification technique, I don’t need to know how it works if all I’m interested in is that protein of interest.  I can move on to what the paper says about the protein secure in the knowledge that it is indeed pure.  If later on if HPLC becomes more important then I can do a quick search or deep dive to understand more of it, but it isn’t always necessary to know every single word or technique in a paper. Reading scientific papers is a skill, one I’ve had to devote a lot of time to getting better at, but once you develop knowledge of the jargon and techniques it gets a lot easier, and importantly you develop the skills necessary to learn any new jargon or techniques that you come across.  And that is the real skill, not the knowledge of specific things but the ability to learn new things.  That is what truly makes a scientist.

Science has its holy wars too

In my continuing ramblings about what science is versus what it ought to be, I thought I’d touch briefly on a topic that is well understood in the community but doesn’t seem understood outside of it, that is the question of how a scientific hypothesis becomes scientific dogma.  I don’t mean dogma in a negative sense, in my area of science a dogma is simply something that is without question because all the evidence points to it being true.  The “central dogma” of biology for example is that DNA is where genetic information is stored, RNA is the messenger of information, and protein executes the functions that are demanded by the information.  DNA->RNA->proteins is a dogma taught to every aspiring biologist and bored high school student, and it underpins every piece of modern biology we do.

But dogmas don’t become dogmas out of nothing, there must be a mountain of evidence in their favor, and additionally there is usually a prior dogma or competing hypothesis that they must replace.  This last bit is important, it has often been said that you can’t reason someone out of a position they did not reason themselves into, but equally true is that you often can’t reason them out of something that they did reason themselves into either.  People just don’t like changing their mind.  And so when a new hypothesis comes along challenging an old dogma, scientists don’t just accept it straight away, instead they will demand more and more evidence for it while continuing to cling to what they learned in the old dogma.  Science advances not through persuasion but through retirement as these heralds of the old dogma retire and get replaced by people who learned the new hypothesis.  And those people in turn accept the hypothesis fully and turn it into a dogma to be taught to students who don’t yet have the full knowledge base yet to understand why something is true but who can be taught that it is true, hence dogma.

During the upwelling of a new hypothesis though, holy wars can happen.  I don’t mean fighting and purges, I instead mean the kind of holy wars that nerds engage in, the kind of demeaning of those on the “other side” in the sense of “oh you have a Gamecube instead of a PC? I should have known you were a console peasant.”  These holy wars infect science too, scientists try to be nice for professionalism of course but they will spend enormous efforts undercutting each other’s theories and at times even undercutting each other’s professional trajectories in their bid to garner support for their own theory.  This may seem needlessly cruel but there is an element of rational self-interest, if you think your theory is true then supporting the truth against the false is good praxis, and in more base terms there is only so much funding to go around so ensuring that your dogma or theory is held in higher esteem will ensure your side is the one receiving the lion’s share of scientific funding.I know this all sounds like pointless waffle, but I was specifically reminded of this when I recently saw a few talks on Alzheimer’s disease.  The holy war over Alzheimer’s can’t be summed up in a short blog post, but some people think Alzheimer’s is caused by a protein called “A-beta” and some think it is caused by one called “tau”.  A few hold a compromise position that perhaps both proteins are necessary but most of the scientists I’ve seen presenting talks hold to one side or the other, and both sides are competing to become the new dogma.  For the most part these two sides talk past each other, if you think that A-beta is the cause of Alzheimer’s disease then there isn’t as much a point in researching tau, and vice versa.  But occasionally you’ll find both sides present at a symposium and there they will feel the need to defend themselves to the audience and slyly denigrate the opposing position.  Never to the level of insults (in public) but instead to the level of “I respectfully suggest that those other scientists have grossly misunderstood the evidence.”  Which is a very kind way of saying fuck you.

When science becomes engineering, it ceases to be science

I just wanted to talk about the pitfalls of science for a moment.  We all know what science is “supposed” to be, you take evidence and create a theory about the world, then you test your theory rigorously to see if it is true, incorporating the new evidence from each round of testing to create a better and better theory.  But although that’s normally what science is in a macro sense, in a micro sense it isn’t always.  Science in a micro sense is the work done by students and researchers at labs all across the globe.  They don’t always have a theory, they don’t always do a good job testing their theories, and importantly for today, they don’t always incorporate new evidence into their theory to see if it is really true.

I worked in a lab before that didn’t incorporate new evidence.  We were trying to make… something.  It isn’t important what that something was, but it was pharmaceutical in nature.  We didn’t know exactly what it would look like, but we would know it when we saw it.  Our science day to day was to do large experiments, and in the experiment look for our special “something”.  If we didn’t find it this time, then we’d change our parameters and try again to run the experiment and look for our “something”.  Each time we failed to find our “something” we would use the evidence to change our experiment,  we would think that maybe some part of our process is destroying the “something,” maybe the “something” is in very small quantities and we can’t detect it, maybe we just ran the experiment improperly and we should try again.  What we would never do is think that maybe our “something” doesn’t even exist, maybe we’re doing experiments and collecting data searching for a mirage, and we should take our repeated null results as evidence that our hypothesis just isn’t true.

We didn’t think that because our minds had been set that this was an engineering problem, not a scientific one.  Scientifically we felt the something *must* exist, everything we’d ever studying said it must, and yet time after time we found it conclusively *not existing* despite our best efforts to find it.  If we could just get the engineering right: tweak the experiment, alter our detection methods, make sure to do it all correctly, then surely we’d find it.  But maybe that was all a lie and it just never existed.

I left that lab, and to this day they still haven’t found their special something.  They still work on it, and I’m sure many labs around the world still work diligently looking for a something that may or may not be there.  But on a micro level I feel that that lab had stopped doing science.