In the latest episode of Elucidations, Sam Enright (Progress Ireland, The Fitzwilliam) instructs us in the delicate art of learning forever. 

If you’re one of those people who responds well to formal education, chances are you’ve spent 10-20 years of your life as a student. When you finally graduate, it can feel jarring, like you’re kissing all this efficient infrastructure for mastering difficult skills goodbye. How are you going to keep learning, without a teacher you can pester with questions in the classroom, without regular feedback on homework assignments, and without exams? Sam Enright is here to tell you that just because you’re moving into the next phase of your life, that doesn’t mean you need to turn your back on the learning experience.

In this episode, he discusses his study regimen, which ranges over philosophy, history, economics, math, and computer science, via a couple different formats that are easier to integrate into your everyday life than full-time study in the classroom. The first is something called spaced repetition. This is a method that involves repeating your study practice less and less frequently over time, in order to maximize your direct recall ability. The version that our guest practices involves using software that leans into quizzing you more often on whatever you have the most trouble with, and less often on whatever you have the least trouble with. The quiz questions are of your design, and every time you answer one, you’re given the opportunity to revise it for the future. This allows you to update your study materials over time in light of the expertise you accrue.

Another method Sam Enright recommends is reading groups. Echoing similar recommendations from the Elucidations podcast in Episode 126, our guest tells us about a recurring reading group he runs in Ireland that spans a wide variety of disciplines. The key here is to select reading material that is too difficult for you to fully make sense of on your own, and to establish a culture of staying on topic. Sam Enright’s reading group has been in existence for years now and attracts researchers from all over.

Finally, our guest discusses how he is able to use AI chatbots to supplement the reading process and drill deeper. In addition to traditional techniques such as notetaking, being able to upload an entire text into a chatbot’s context window and then ask it questions about what you’re reading allows you to explore the terrain it opens up interactively. You can restate your understanding of what you just read, invite the chatbot to identify mistakes in your summary, revisit the parts of the original text that are relevant to those mistakes, and so forth. Interestingly, he even reports having success when the platform he is using hallucinates a little, because trying to sniff those hallucations out allows him to cultivate the kind of skeptical attitude that makes reading itself a bit more like the classroom experience.

It was a tremendously fun discussion for me to have, and I hope you enjoy it.

Matt Teichman

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This time around, Matt talks to Luca Gattoni-Celli about why it’s so expensive to buy a house.

In the 80s, people from all sorts of socioeconomic backgrounds were able to afford apartments and houses in places like New York City, San Francisco, or London. Now, on the other hand, even many wealthy people are getting priced out of the city. And indeed, the issue is no longer specific to urban areas: the problem of seemingly infinitely increasing real estate prices would appear to be creeping into the rest of the US, and into many other areas that were typically regarded as affordable in the recent past.

Why is this the case? In this episode, Luca Gattoni-Celli discusses three factors that have artifically inflated housing prices far beyond the equilibrium point between supply and demand. One is zoning regulations, which impose limits on how maximum building size in a given area, how many people can live on a single property, and so forth. Another is permitting, which has the effect of introducing delays into the building process that make it financially infeasible and thus effectively block it from happening. The third is building codes, many of which were introduced for the purposes of making buildings safer to inhabit, but which have the perverse effect of preventing the construction of new buildings that would be safer than the old buildings that are currently in use.

Our guest also makes the argument that zoning regulations have a sordid racist and classist past, which you can see, to an extent, in some of the original proposals that led to some of the original policies. More broadly, the claim is that population density is the way that low-income people band together to be able to afford real estate for which there is high demand, and that a push to block density effectively amounts to a push to keep lower-income people out.

I found the discussion quite stimulating; I hope you enjoy it.

Matt Teichman

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Note: this episode was recorded in August of 2022.

In the latest Elucidation, Matt talks to Witold Więcek about the difficulties that come up for researchers who would like to draw upon statistics. 

Lots of academic fields need to draw heavily on statistics, whether it’s economics, psychology, sociologym, linguistics, computer science, or data science. This means that a lot of people coming from different backgrounds often need to learn basic statistics in order to investigate whatever question they’re investigating. But as we’ve discussed on this podcast, statistical reasoning is easy for beginners to mess up, and it’s also easy for bad faith parties to tamper with in undetectable ways. They can straight up fabricate data, they can cherry pick it, they can keep changing the hypothesis they are testing until they find one that is supported by a trend in the data they have. So what should we do? We can’t give up on statistics; it is simply too useful a tool.

Witold Więcek argues that researchers have to be mindful of “p-hacking”. Statistical significance, the golden standard of academic publishing, can easily be guaranteed by unscrupulous research or motivated reasoning: statistically speaking, even noise can look like signal if we keep asking more and more questions of our data. Modern statistical workflows require us to either adjust the results for number of hypotheses tested or to follow principles of Bayesian inference. As a broader strategy, Więcek recommends that every research project making significant use of statistical arguments bring in in an external consultant, who can productively stress test those arguments in an adversarial way, given that they aren’t part of the main team.

It was a great conversation! I hope you enjoy it.

Matt Teichman

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In this episode, Matt sits down with Shruti Rajagopalan (Mercatus Center) to talk about what the future holds for India.

We often have a tendency to think of the current economic and geopolitical situation as simply the way things are. Especially for people who grew up in the United States over the past 50 years, the fact that it is an economic and military superpower sorta feels set in stone. But in this episode, Shruti Rajagopalan encourages us to take the long view, regarding the current state of the US as just one phase in a decades or possibly centuries-long economic development life cycle. First, the country logs a certain number of decades as a manufacturing hub, under conditions of minimal top-down interference from regulatory bodies. This enables it to build wealth, which eventually pushes it away from being a manufacturing economy, but it’s a race against the clock. With economic growth comes a rise in average life expectancy, plus a lower birth rate, which together can lead to large aging population. Once the aging population increases, the country’s economy needs to be strong in order to accommodate all the caregiving that an aging population makes necessary.

Interestingly, it’s starting to look like some other countries—particularly India—are currently poised to undergo a similar trajectory of economic development that the US did. What makes India stand out is that among the countries in the world with a large young population, they have an unusually high GDP per capita. They also have a pretty sizeable early-career, STEM-savvy middle class that is ready to move anywhere in the world, build a life wherever they end up, and culturally assimilate.

Our esteemed guest argues a) that the relaxation of economic restrictions which took place in India in 1991 made this siutation possible, and that b) a few conditions still need to be met for the future to unfold in the optimal way. One is that India needs to build up its manufacturing sector that the country can get richer before the population gets too old. Another is that other countries need to take advantage of the fact that India’s young workforce is ready to emigrate. She even suggests that wealthier countries with a rising elderly population, low birthrate, or declining heritage language could perhaps address those issues by welcoming an incoming population of young workers from India.

Join us as our guest outlines the (hopefully) upcoming rise of India on the world stage!

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In this episode, we are joined by Lainie Ross (University of Rochester Medical Center) and (once again!) Christos Lazaridis (UChicago Medicine), this time to talk about the different ways of defining death.

In our previous episode with Christos, we talked about death and the vexed history of attempts to define it. Prior to the advent of modern life support technology in the 1950s, it was usually enough to check whether a person had a heartbeat and could breathe to determine whether they were dead. But once machines were invented that could breathe and circulate blood in patients whose lungs or hearts were failing, a new moral conundrum was born: how do you decide whether a medical patient is dead when it is now possible to keep their lungs breathing and their heart beating indefinitely?

In this episode, our distinguished guests talk about the actual criteria that physicians use to determine whether a patient is dead, as well as some possible criteria that no one has tried applying but which some doctors think would be more appropriate. Furthermore, Lainie Ross argues that every person has the right to choose which criteria will be used to determine whether they are dead. These two topics interact in interesting ways.

For example, I might have a strong preference for my doctor to pronounce me dead only if I have permanently lost all consciousness, even if I can still spontaneously breathe. Although we currently have no good method of objectively measuring whether a patient has permanently lost consciousness, Dr. Ross argues that I have the moral right to sign an agreement stating my preferences. Specifically: the agreement could state that if, in the future, the technology for determining whether someone has permanently lost consciousness gets invented, and, at that time, I have permanently lost consciousness, then I should be declared dead. On the flipside, some patients prefer a stricter criterion, often for religious reasons. Perhaps it is my religious belief that if I am breathing, then I should be considered to be alive. Lainie Ross argues that in that case, I have the moral right to sign an agreement stating that that is the criterion that doctors will use on me, in the event that I lose consciousness but am still able to spontaneously breathe.

As of right now, people only have the legal right to sign these types of agrreements in a handful of states in the US. Join us for this episode as Christos Lazaridis and Lainie Ross argue for making this legal right more widespread!

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In this episode, Matt sits down with Christos Lazaridis (University of Chicago Medicine) to chat about what brain death is and whether brain death should count as, like, death death.

Modern life support technology really hits its stride in the 1960s, allowing doctors to buy themselves more time to save their patients by connecting them to machines that can assist with breathing, blood oxygenation and/or heart pumping. But the flipside to that incredible technological breakthrough was that the medical community now needed to get more precise about the moment at which a person goes from being alive to being dead. After all, what had previously been a quick window between the two was now, due to life support technology, happening in extreme slow motion. In addition, organ transplanation was becoming more and more commonplace, meaning that it was no longer as simple as saying e.g. ‘I count someone as dead just in case their heart has stopped.’

By the early 80s, the United States had settled on a standard definition for when someone counts as dead, which states that a person is dead if they have either permanently lost consciousness or permanently lost the ability to breathe and pump blood with their heart. That criterion makes certain life-saving practices possible; for example, it legally feasible for organ transplantation to begin once a patient has fallen into an irreversible coma, provided they agreed to donate their organs in advance.

But should a person really count as dead just because they fell into an irreversible coma? We call that condition ‘brain death’, or sometimes the wordier ‘death by neurological criteria’, and we legally count it as a full death. Critics of the notion of brain death say that it should not count as death, because a person in this condition is still biologically alive. Their argument is that saying a person in this condition is dead is just a story we’re telling ourselves.

In this episode, Christos Lazaridis—who is a practicing neurointensivist—argues that even if that is a story we’re telling ourselves, that’s fine, because this is a corner case in which it makes sense for the social/legal status of being dead to come apart from the biological status of being dead.

Tune in to hear why he thinks this is the case!

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In this episode, Matt talks to Gabriella Gonzalez about how basic concepts from the branch of math known as abstract algebra can help us simplify our

computer programs and organize our thoughts.

Algebra. That thing they make us do in school. What was that again? Oh yeah, that’s right; it’s where you get to manipulate equations containing variables. Like, if I have an equation that looks like this:

2⋅x = 16

Then I can divide both sides by two and get a new version where x stands alone, i.e. solve for x:

(2⋅x) / 2 = 16 / 2

x = 8

If you took algebra in school, you might remember learning a bunch of tricks for pushing parts of equations around to get one of the variables to appear only on one side and thus solve for it. Being able to solve for variables in equations proves useful for lots of things: like, if you can translate a word problem into one of those equations, finding the answer is often as simple as tinkering with the equation in some obvious way.

Abstract algebra is somewhat similar in that it also involves manipulating equations containing variables, except the twist is that now you aren’t necessarily manipulating numbers anymore. The variables can stand for something else, and there are more general versions of plus-like, times-like, etc. operations that you can do on these other things. You might be wondering: what on earth could a variable in an equation stand for other than a number? Well, in this episode, Gabriella Gonzalez gives a bunch of examples. You can have equations for cooking recipes, for computer programs, for transactions performed on databases, and for regular expressions. (A regular expression is a special type of computer program for identifying strings that fit a particular pattern and pulling information out of them.)

Gonzalez then goes on to argue that the point of all this is to avoid re-inventing the wheel. Often, when you write a computer program to add some numbers, though this isn’t necessariy obvious at the time of writing, you aren’t actually drawing meaningfully on the fact that they’re numbers. If that’s the case, then what you can often do is make your code that adds things abstract so you only have to write your program once, but then you can re-use it on all these different other kinds of entities other than numbers.

The overall payoff of all that, according to this month’s distinguished guest, is that by following algebra-driven design, you can keep your code simple and easy to understand, while still having it do fancy things. This is particularly important today, when our software just seems to keep getting fancier and fancier, but the usual ways of accomplishing that goal make it unreliable and well nigh impossible to keep up to date.

Join us as Gabriella Gonzalez gives us the tour through various algebraic systems that occur all over the place in computer science, philosophy, and linguistics!

Matt Teichman

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In this episode, Matt sits down with Gaurav Vankataraman (Trisk Bio) to talk about how human memory is physically realized.

Where do your memories live? In the brain, right? They’re, like, imprinted there somehow? We often think of memories as analogous with recordings, like when you do an audio recording and the air vibrations get translated into an electrical signal which reorients the magnetic particles on some tape. But is that really how it works? Is the brain some tape waiting to get recorded to, or a hard drive waiting to get data written to it? We don’t exactly have definitive answers to those questions, but in this episode, our distinguished guest discusses a line of research into whether memories could be stored outside the brain, in RNA. He then notes that there is also a lot of RNA in the human brain itself, which means that a similar mechanism for storing memories could exist there as well.

This research, as it turns out, originated in some rather astonishing scientific work from the 1950s involving planarian flatworms. Planarian flatworms have the extraordinary ability to regenerate: if you cut one in half, each of the two halves can actually grow back into a new worm. At that time, there was some preliminary evidence to suggest that if a planarian flatworm learned something, and you cut it in half, when the half that didn’t have a brain grew back, it still retained what the original worm had learned. What the what? It could remember something even though it had a brand new brain? Those initial studies went through a period of being discredited, but in recent years a number of researchers have been exploring new, more rigorous evidence that something of this nature could be going on. Perhaps the flatworms could actually be storing some of their memories in their RNA or DNA, and perhaps RNA has the ability to preserve some of that information both in and outside of the brain.

In this episode, Gaurav Venkataraman argues that the RNA in the brain not responsible for making proteins (called non-coding RNA) has a specific type of mathematical structure that is particularly well-suited for transmitting information both fast and accurately. Not only that, but entities with that kind of structure transmit information more accurately the faster they transmit it. So the fact that RNA in the brain is structurally arranged in the way it is actually makes it a viable candidate for being sort of like the brain’s “software” for storing and manipulating memories.

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This episode, Matt and Joseph sit down with Andrew Sepielli (University of Toronto) to talk about metaethical quietism. His new book on the topic, Pragmatist Quietism, is out now from Oxford University Press. Click here to listen to episode 145 of Elucidations.

Metaethical quietism is the view that ethical statements—or anyway, a large portion of the ethical statements we’re usually interested in—can’t be justified or disproved by statements from outside of ethics. There’s something autonomous about the topic of ethics (or rather, about a lot of ethics). Consider the question: in the scenario where a trolley is barreling down the track, on its way to clobber five people, and you have the ability to divert it to the other track where it will only clobber one, should you do so? According to quietists such as our guest, you can’t answer this question by asking metaphysicians or logicians for help. It won’t do to investigate whether moral facts are part of the furniture of the universe, or to study the grammar of words like ought. The only way you can answer a question like that is, well, whatever we usually do to answer ethical questions.

Why are philosophers often tempted to think we can turn to metaphysics, logic, or the philosophy of language to help answer ethical questions? Andrew Sepielli thinks it’s because we conflate two different kinds of ethical statements: the statements he calls deep and the statements he calls superficial. A deep statement is one such that, if you believe it, that belief can impact your mental picture of how things are laid out in the world and guide your action. The fancy word for this mental picture of how the world is laid out is non-conceptual representation. A superficial statement is one belief in which does not influence your non-conpceptual representation of the world. The questions that moral philosophers often write about—such as whether one should divert the trolley, or whether utilitarianism is true—are superficial, which is part of why you can only answer them from within ethics. But there are also deep moral questions, such as: will the party we’re thinking of going to be attended by a bunch of jackasses? When you ask that question, you’re deploying moral language—jackass, specifically, so it is definitely a moral question—but you’re also trying to find out which individuals are going to be at the party. And which individuals happen to be at the party is part of the information in your non-conceptual mental map.

In this episode, Sepielli argues that keeping track of when we’re having a superficial debate vs. when we’re having a deep debate can make it seem less mysterious how ethics could be its own autonomous area of inquiry. Tune in to see why he thinks this is the case!

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This episode, Matt talks to Christopher Beem (Penn State University) about how we can cultivate those skills that conduce to having a functioning democracy. His book on the topic, The Seven Democratic Virtues, is out now from Penn State University Press.

The storming of the US Capitol Building in 2021 was an eyebrow-raising event, to say the least. It prompted historians, political scientists, and political philosophers to ask whether deep down, everything was going okay with our democratic system. Was this event some kind of sign that the voting process was under unusual strain? Was it time to start thinking about measures we can take to try to support and maintain it?

Our guest says yes: that time is upon us. In light of evidence from evolutionary psychology that natural selection imbued us all with an instinct for tribalism, he argues that there are pretty strong causal forces working to congeal us into increasingly disjoint political camps. If we don’t do anything about it, members of these communities may one day no longer be willing to vote in the same elections. But what are we supposed to do about it? Although no one simple trick is likely to work, Beem argues that there are personal moral virtues that we can all work on developing. Democracy doesn’t just function by default; we have to actively work on ourselves in order to make it function. It’s not like we wave a magic wand, follow a couple simple steps and then we’re “done”. Rather, it’s always going to be an ongoing process to keep this crazy thing afloat.

Tune in to hear Christopher Beem’s thoughts about what these virtues are and how exemplifying them can get our democracy back into whack!

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