Tag: learning

Optimizing for Delight

Here, take a look at this video:

As the video makes clear, the icon looks “sad and old” if you don’t use Duolingo for a while. The more you use it (and the more regularly you use it), the “younger” it will look.

This, if you want to be a grumpy, cantankerous cynic, has nothing to do with anything. If, on the other hand, you want to be a person who appreciates delightful little touches full of whimsy and wonder, this is just wonderful.

This, and other delightful little experiences woven into the Duolingo app, are due to the “Delight” team:

As the post makes clear, Duolingo wasn’t (and isn’t) optimizing for any specific metric – no change was expected in “their numbers”. Duolingo was (and is) optimizing for delight, plain and simple.

And, it turns out, optimizing for delighting your customers tends to work well for “your numbers”. Or to copy a much better turn of phrase, delight facilitates learning in ways one cannot anticipate.

There’s an important lesson in there for those of us working in education. Get your processes right, have a solid foundation, provide a good experience and make sure your academic processes are rigorous.

But do make sure that somebody, somewhere, is optimizing for delighting your customers.

In Praise of Random Questions

I’ve written about this before: I am in the habit of asking my students to ask me five random questions at the end of each class. The questions can be about absolutely anything, the only condition being that they cannot be about the topic we have discussed in class.

The reason I bring this up is because Tim Harford’s column in the Financial Times serves up it’s own share of delightfully random questions:

Last year, inspired by Randall Munroe’s delightful books What If? and What If? 2, I invited the good folk of Twitter to ask me absurd hypothetical questions about the economy, to which I would attempt some serious answers. This year, we’re going to do it all again.

https://timharford.com/2024/01/more-of-your-crazy-economics-questions-answered/

Tim Harford covers five questions in this column, and while I encourage you to go read his answers in his original blogpost, I’ll list out the questions here:

  1. How big would an asteroid made of precious metal have to be for it to be worth doing a space mission to bring it back?
  2. Could a universal currency ever be based on electricity, or currents?
  3. What if your tax bill was discounted by the distance you lived from the centre of London (eg if you lived in Kingsway, you paid the full amount; if you lived in Shetland, you would pay no tax)?
  4. What if inflation was made illegal? Could we legislate that no prices could ever rise?
  5. In the UK, we used to print banknotes on paper, now it’s a horrible slippery plastic. Could we use a more environmentally friendly material, like leaves? Or perhaps something edible — printed on some sort of simple flour and water biscuit? No waste!

Magnificent – just magnificent questions. In answering them, Tim Harford covers (without always naming them explicitly) scarcity, relative prices, optimization, the functions of money, volatility, Jane Jacobs, relative prices, price as a signal (not to mention an incentive!), Douglas Adams (!) and the supply of money.

One needn’t stop there, of course. You could introduce many other topics into the discussion, in addition to those already covered by Tim Harford. But the point here is that all of these topics could be introduced in response to the questions asked by the students. These topics aren’t being introduced “because they are a part of the syllabus”, but because these are helpful concepts that help in formulating sensible responses to delightfully random questions.

Sensible responses to delightfully random questions is exactly what “What If” and “What If 2” are all about, as Tim Harford points out in his column. And that, of course, is the guiding spirit behind such questions – students should tap into their natural curiosity about the world, and ask mind-bending and patently ridiculous questions.

This forces the person answering the question to build up their response from first principles, in order to show why and how the answer to the question cannot help but be ridiculous. What a remarkably powerful way to teach, no?

Why is it remarkably powerful? It is remarkably powerful because we ask the students to imagine a better world, if only we could do x. Look at the fifth question in the list, for example.

And then the question becomes, well, why can we not do x? Well, here’s why: reason 1, reason 2 and reason 3. In order to explain our reasoning, we introduce concepts. We don’t introduce concepts Because That Is What We Do So That The Syllabus Is Completed – we introduce concepts in order to answer questions that students came up with.

And I don’t know about you, but both my theory of how the world work and my experience for having taught introductory economics for many years tell me that students are much more likely to be attentive when it is weird questions being answered sensibly.

Or here is another way of putting it: memorizing a textbook, and occasionally asking if it is applicable to the world can get pretty boring. But looking at the world, asking questions about how we can make it better, and then learning about concepts that either validate or invalidate the premise of the question – that’s not boring at all.

Would you rather memorize the functions of money, for example, or would you rather wonder about why we cannot use electricity as a currency? Or time, for that matter! Now, if you were to ask me why we couldn’t use electricity or time as money, I may have to end up explaining the functions of money to you, and also explain why these functions can’t be carried out by time or electricity.

Ah, you might say, that makes sense. Now you know why time can’t be money (hehehe), plus you’ve ended up learning about the functions of money. Fun discussion, plus learning. What’s not to like, I ask you.

If you aren’t asking weird questions, you aren’t learning.

Ask weird questions!

The Economist on AI and Transforming Education

Almost three years ago to the day, I’d written a post on The Long, Slow, but Inevitable Death of the Classroom:

When the pandemic ends, whenever that may be, do we swing back to the other end of the spectrum? Does everybody sit in a classroom once again, and listens to a lecture being delivered in person (and therefore synchronously)?

Or does society begin to ask if we could retain some parts of virtual classrooms? Should the semester than be, say, 60% asynchronous, with the remainder being doubt solving sessions in classroom? Or some other ratio that may work itself out over time? Should the basic organizational unit of the educational institute still be a classroom? Does an educational institute still require the same number of in person professors, still delivering the same number of lectures?

In other words, in the post-pandemic world…

How long before online learning starts to show up in the learning statistics?

https://econforeverybody.com/2021/01/28/the-long-slow-but-inevitable-death-of-the-classroom/

And three years later, we have our answer, from the Economist:

The sector remains a digital laggard: American schools and universities spend around 2% and 5% of their budgets, respectively, on technology, compared with 8% for the average American company. Techies have long coveted a bigger share of the $6trn the world spends each year on education

https://www.economist.com/business/2024/01/11/ai-can-transform-education-for-the-better

Higher education is a bundle, of course. When you enrol with a university, you are purchasing an education, a degree and the ability to build the kind of networks with your peers that you’re never ever going to be able to build again. And online education takes away the misery of having to listen to bad professors drone on in classrooms, sure, but does nothing to solve the problem of having a degree that very few other people have. And it is really, really bad at helping you build out good peer networks.

So the death of the physical classroom isn’t imminent just yet – not because we fell in love with bad professors and musty classrooms with “smart” boards after the pandemic, but because the degree continues to matter, and because nothing (nothing!) beats bunking classes with friends.

But the answer to the question implicity posed by The Economist article – why did classes resume much as before post the pandemic – is quite simple. Because in addition to the lure of being one among a select few who gets to clutch a degree from a hallowed university and the awesomeness that is hanging out with friends IRL, online education simply meant that you got to listen to the same bad professor, except it was online.

And that is worse! The prof is as boring, but you are listening to that boring prof in your PJ’s, in bed. Which is very welcome one day out of five, sure, but for two long years? Fuhgeddaboutit.

But a fun prof who gives you customized, tailored teaching and mentoring? A prof who customizes their teaching style, their pedagogy and their problems tailored to how well you seem to be learning? A prof whose lectures you can pause and resume, as needed, on a 24/7 basis – maybe that will work?

Two-fifths of undergraduates surveyed last year by Chegg reported using an AI chatbot to help them with their studies, with half of those using it daily. Indeed, the technology’s popularity has raised awkward questions for companies like Chegg, whose share price plunged last May after Dan Rosensweig, its chief executive, told investors it was losing customers to ChatGPT.

https://www.economist.com/business/2024/01/11/ai-can-transform-education-for-the-better

The Economist article goes on to point out how education specialists might end up doing a better job than plain vanilla GPT. They argue how education specialists, such as Chegg’s and their like know the ins and outs of the education business, and will therefore likely do a better job at customizing and deploying AI in education. This is, the Economist says, because of the following reasons:

  1. Pearson, McGraw Hill and some other publishers haven’t made their data available to ChatGPT, and are instead incorporating AI into their own products
  2. Chegg’s and friends are following a similar approach, and have years of mentoring related data ready to deploy.
  3. Firms in this sector have “an in” with educational institutes already, and that will make their pitches about deploying AI more palatable to educational institutes.

Maybe so, and I honestly don’t know how this will play out. Maybe ChatGPT will get better, especially with the launch of their store. Maybe the competition will be definitively better than ChatGPT.

But us boring ol’ profs have competition, and lots of it. As The Economist mentions, we may have to “shift our attention to motivating students and instructing them on how to best work with AI tools”.

That last bit I agree with most passionately. The job of educators in the age of AI isn’t to teach, but to mentor. Our job is to help students learn, not teach them. This statement is banal to the point of being a platitude in education, but with AI, there may well be an “iota of truth” in there now.

Along with – for now – an iota of inevitability.

Professor Harberger’s Midterm and Final Examinations (and ChatGPT)

A book that hardly anybody reads these days (but really should) is The Theory of Price, by George Stigler.

Take, for example, Stigler’s “A Note on Block Booking.” Block booking of movies was the offer of a fixed package of movies to an exhibitor; the exhibitor could not pick and choose among the movies in the package. The Supreme Court banned the practice on the grounds that the movie companies were compounding a monopoly by using the popularity of the winning movies to compel exhibitors to purchase the losers.
Stigler disagreed and presented a simple alternative argument. If Gone with the Wind is worth $10,000 to the exhibitor and Getting Gertie’s Garter is worth nothing, wrote Stigler, the distributor could get the whole $10,000 by selling Gone with the Wind. Throwing in a worthless movie would not cause the exhibitor to pay any more than $10,000. Therefore, reasoned Stigler, the Supreme Court’s explanation seemed wrong.
But why did block booking exist? Stigler’s explanation was that if exhibitors valued films differently from one another, the distributor could collect more by “bundling” the movies. Stigler gave an example in which exhibitor A is willing to pay $8,000 for movie X and $2,500 for Y, and B is willing to pay $7,000 for X and $3,000 for Y. If the distributor charges a single price for each movie, his profit-maximizing price is $7,000 for X and $2,500 for Y. The distributor will then collect $9,500 each from A and B, for a total of $19,000. But with block booking the seller can charge $10,000 (A and B each value the two movies combined at $10,000 or more) for the bundle and make $20,000. Stigler then went on to suggest some empirical tests of his argument and actually did one, showing that customers’ relative tastes for movies, as measured by box office receipts, did differ from city to city.

https://www.econlib.org/library/Enc/bios/Stigler.html

If you want a more “modern” take, you could read this lovely essay, by Chris Dixon.

But to come back to Stigler, one reason to read his book is because “a typical Stigler article laid out a new proposition with clear reasoning and then presented simple but persuasive data to back up his argument”. Which, if you ask me, is a better way to teach microeconomics than via real analysis.

If you aren’t yet subscribed to Irwin Collier’s blog, please do so. His blog is a delightful treasure trove of question papers from the past, and some of these papers really and truly make you think. Here, for example is just one question from November 5, 1957. This particular question paper was set by Professor Harberger (and the examination took place a year before Stigler joined the University of Chicago), but nonetheless, it is a great way to check how well you know price theory microeconomics.

The price elasticity of demand for a good will be higher, the higher is the income elasticity of demand for that good.

For a question like this (and before you run it through ChatGPT), think about whether the statement makes intuitive sense. If the income elasticity of demand for a good is high, that means that small changes in income will lead to large changes in demand for that good. Eating out at expensive restaurants will go down if you lose your job, for example.

Well, then, yes, you might think – the price elasticity also ought to be high then, no?

So it would seem that this is true then.

Ah, but is it? Do you know of folks who buy the latest iPhone regardless of how much (and whether) they earn? Can you, in other words, think of a single counter-example to your own argument? Does your answer change? If yes, why? If not, why not?

(My own take is that this is false, by the way. Do you agree?)

Now that you have thought a little about this yourself, ask ChatGPT what it thinks (or Bard, etc.). See if your answers match. If they don’t, have a conversation with it. Tell it why you thought your answer was whatever it was, and ask it to critique your answer.

Rinse and repeat for all the other questions in the examination, and I envy you your journey of learning price theory. Oh, and by the way, if you don’t like your current (ahem) price theory Prof, feel free to call upon the knowledge and abilities of Professor Stigler himself:

“What book should I read to learn economics?”

“The Internet, and ChatGPT!”

Underrated Ideas in Economics

I and Anupam Manur had a lot of fun in a session we conducted for the Takshashila Institute yesterday. Credit to Anupam, it was his idea. And what an idea it was:

Come up with five weird/underrated ideas in economics each, and get the other person to respond to each. Have a bit of a discussion, and then have the participants take the discussion forward.

Here’s the list of ideas that we ran past each other (you may have to open the image in a new tab):

These notes were taken as we spoke, but they cover only a small part of the entire conversation, naturally. Alas, it was not a recorded session, and I cannot share it with you. But it was so much fun!

  1. Discussions work better than lectures. Anytime you want to host a session for students of any age, getting two people to argue in front of an audience is always better than getting one person to deliver a monologue.
  2. For at least me (and I suspect for Anupam as well), coming up with our five ideas was a lot of fun. It forced me to step back and think about economics and what I’ve been working on related to economic theory in a much more introspective fashion, and when is that ever a bad thing?
  3. We ended up agreeing with each other, alas! Why do I say “alas”, you ask?
    • Your arguments become far sharper when you have to defend them, particularly against a worthy “opponent”. If you want to learn better, find someone awesome to argue with.
    • The space for civil disagreement shrinks daily in front of our eyes, and I was hoping to get lots of it from Anupam. The noun (disagreement) is in plentiful supply everywhere we look, the adjective (civil) not so much, and the combination not at all.
    • Given what you know about a field, optimize for being surprised in conversations. Which means you should hear something counterintuitive in a discussion. Unfortunately for me (and for Anupam), both of us ended up picking points that may have been counterintuitive to others on the call, but not for the two of us. My personal lesson: think harder the next time around!
  4. More of these conversations need to happen, and more people need to see them. Not, to be perfectly clear, conversations necessarily involving me or Anupam in particular, but involving people who have some amount of expertise in a particular field (and said people are willing to talk, debate and discuss underrated ideas from said field). This is my favorite example.
  5. Let’s make more of these “debates” happen!

Learn at Twitter Speed, Get Tested at AOL Speed

The title of today’s post is directly lifted from an MR Post from yesterday, which you should read in its entirety.

Instead of hearing a rumor at the coffee shop and running down to the bank branch to wait on line to withdraw your money, now you can hear a rumor on Twitter or the group chat and use an app to withdraw money instantly. A tech-friendly bank with a highly digitally connected set of depositors can lose 25% of its deposits in hours, which did not seem conceivable in previous eras of bank runs.
But the other part of the problem is that, while depositors can panic faster and banks can give them their money faster, the lender-of-last-resort system on which all of this relies is still stuck in a slower, more leisurely era. “When the user interface improves faster than the core system, it means customers can act faster than the bank can react,” wrote Byrne Hobart. You can panic in an instant and withdraw your money with an app, but the bank can’t get more money without a series of phone calls and test trades that can only happen during regular business hours.

https://marginalrevolution.com/marginalrevolution/2023/03/banks-as-meme-stocks.html

Try this variant on for size:

Instead of hearing about a concept in a classroom, and running to the library to get access to the book that explains it in greater detail, now you can hear about a concept on Twitter, or the group chat, and use ChatGPT to learn all about it instantly. A tech-friendly classroom with a highly digitally connected group of learners can learn much more about a topic in a couple of hours, which did not seem conceivable in previous learning environments.
But the other part of the problem is that, while learners can learn faster and LLM’s can give them additional nuance and context much better, the exam system on which all of this ultimately relies for certifications is still stuck in a slower, more traditional era. “When the learning environment improves faster than the testing environment, it means learners can learn better than colleges can meaningfully test them,” wrote a grumpy old blogger. You can learn much more about a topic in a semester than you ever could before, but the college will still insist on making you memorize stuff so that you can choose five questions out of six to answer in a closed-book-pen-and-paper examination.

It’s not an exact analogy, of course. But there are two points to this blogpost:

  1. Where colleges and universities are concerned, this is a useful framework to deploy. And sure I had fun tweaking that excerpt in order to maximize my snarkiness – but I’m not joking about the point being made. When students are able to learn far better, far more effectively and far faster, but the testing environment doesn’t keep up with either the learning or its applications, it is a problem. Simply put, if teaching and learning with LLM’s is best, but the college thinks that testing without access to LLM’s is best, there’s a disconnect.
  2. The broader point, of course, is that you should be applying this framework to everything. Banks and colleges, sure. What about government (at all levels)? What about software companies? What about delivery apps? What about <insert the place you work at here>? Which parts of your organization are already using LLM’s in their workflows, or will sooner rather than later? Which parts will be the most reluctant, and therefore the last to adopt to this brave new world? What imbalances might result? How should we incentivize the rate of adoption such that we optimize appropriately?

Note that this doesn’t necessarily mean incentivizing those reluctant to adopt! You might want to incentivize a slower adoption of ChatGPT, if that’s what you think is best (and yes, that goes for colleges too). But if that’s the route you’re going to go down, think first about the competition. And note that in the age of LLM’s, defining who your competition is isn’t as easy as it used to be.

Thinking Aloud on Teaching with ChatGPT

Say I have to teach an introductory course on the Principles of Economics to students who are just starting off on their formal study of the subject. How do I go about teaching it now that ChatGPT is widely available?

  1. Ignore the existence of ChatGPT and teach as if it does not exist.
    • I am not, and this is putting it mildly, in favor of this proposal. ChatGPT knows more about this subject (and many others) than I do now, and ever will. It may not be able to judge how to best convey this information to the students, and it may (so far) struggle to understand whether its explanations make sense to its audience, about whether they are enthused about what is being taught to them, and whether it should change tack or not. But when it comes to knowledge about the subject, it’s way better than I am. I would be doing a disservice to the students if I did not tell them how to use ChatGPT to learn the subject better than they could learn it only from me.
      So this is a no-go for me – but if you disagree with me, please let me know why!

  2. Embrace the existence of ChatGPT, and ask it to teach the whole course
    • I do not mean this in a defeatist, I’m-out-of-a-job sense. Far from it. What I mean is that I might walk into class, give the prompt for the day, ask the students to read ChatGPT’s output, and then base the discussion on both ChatGPT’s output and the student’s understanding. (Yes, they could do the ChatGPT bit at home too, but you’d be surprised at the number of students who will not. Better to have all of them do it in class instead.) Over time, I’ll hope to not give the prompt for the day too! But it will be ChatGPT that is teaching – my job is to work as a facilitator, a moderator and a person who challenges students to think harder, argue better – and ask better.

  3. Alternate between the two (roughly speaking)
    • The approach that I am most excited to try. In effect, ChatGPT and I will teach the course together. I end up teaching Principles of Economics, where ChatGPT adds in information/examples/references/points of view that I am not able to. But I also end up helping students understand how to use ChatGPT as a learning tool, both for Principles of Economics, but for everything else that they will learn, both within college and outside of it. This is very much part of the complements-vs-substitutes argument that I have been speaking about this week, of course, but it will also help me (and the students) better understand where ChatGPT is better than me, and (hopefully) vice-versa.

Whether from the perspective of a student (past or present) or that of a teacher (ditto), I would be very interested to hear your thoughts. But as a member of the learning community, how to use ChatGPT inside of classrooms (if at all), is a question I hope to think more about in the coming weeks.

Prompts To Get You Going on Learning With AI

I’m assuming, in today’s post, that you have some knowledge of both economics and of economists, and that you are a student from India.

Feel free to copy these prompts word for word, but the major reason for doing this is to give you ideas about how you might go about constructing prompts yourself. Try modifying these prompts by choosing a different economist, specifying different time periods, or tweaking it however you like. Feel free to go meta too, as one of the prompts below does. But the idea behind this post, which itself is a continuation of yesterday’s post, is to help you learn how to use ChatGPT as your own personal tutor.

What if Paul Krugman could be asked to give you ten introductory lectures in economics?

See what kind of answer you get, and feel free to ask follow-up questions before asking ChatGPT (in this case, aka Paul Krugman) to move on to the next lecture. Note that the “Yes, I do.” in the prompt below is in response to ChatGPT asking me if I had any questions. Also note that these aren’t necessarily the questions I would ask of ChatGPT myself – I’m trying to think of myself as a first year undergraduate student, and am framing my questions accordingly. If you would like to ask slightly more advanced questions, please do so, by all means. And of course, that cuts both ways – feel free to ask simpler questions!

I followed up with another question:

And then on to the second lecture:

Again, if you like, begin with these exact prompts and see where they take you. But I would encourage you to make changes to these prompts to suit your own learning style better (“recommend only podcasts or YouTube videos”, for example).

If only I could have used this next prompt about twenty years ago. Pah.

And if all else fails, go meta:

I know that you’ll be able to come up with better prompts, more suited to your learning style. The idea behind this post is just to get you started. The more you converse with AI, the better your prompts will get, and the better a conversation you will end up having.

The ability to have a personal tutor who can customize learning pathways suited to your interests is what makes this such an exciting time to be a student. For example:

What a great time to be a student!

Our Job Is To Help Them Make Something Of It

Now, after more than a year out of the classroom, Wataru, 16, has returned to school, though not a normal one. He and around two dozen teenagers like him are part of the inaugural class of Japan’s first e-sports high school, a private institution in Tokyo that opened last year.
The academy, which mixes traditional class work with hours of intensive video game training, was founded with the intention of feeding the growing global demand for professional gamers. But educators believe they have stumbled onto something more valuable: a model for getting students like Wataru back in school.

https://www.nytimes.com/2023/02/25/business/japan-esports-school-refusal.html

I came across this article in the New York Times, and found it to be fascinating. Wataru, the sixteen year old mentioned in the article, had dropped out of school after the pandemic, because “he was getting nothing from school”. He preferred to stay at home and play video games the whole day.

This school though, the one featured in the article, is a school in which you’re taught competition strategies for games such as Fortnite and Valorant. Or you might be given – and this was my favorite sentence in the article – “a scientific lecture about the relative merits of Street Fighter characters”. And it’s not just theory, of course – post this lecture, the students then formed groups to put the lesson into action.

This is what a classroom looks like:

https://kotaku.com/esports-high-school-opening-in-japan-next-spring-1848263433

If you’re curious, and are able to speak and understand the language, here’s what the infrastructure of the school looks like – it has forty Galleria XA7C-R37 gaming PC’s. The curriculum includes the following genres of video games: FPS, third-person shooter, RTS and MOBA. I don’t know what these genres are, for I don’t play video games all that much.

But I applaud the initiative, and hope it scales, both within Japan and in other parts of the world.

You may ask why I applaud a school that teaches students how to play video games. And my answer is that I’m actually quite agnostic about how an educational institute is weird. All I ask is that it be sufficiently weird in at least one way. This particular school is weird about video games, but what about schools that are weird in other ways? What about a school that teaches you about dancing, for example?

Lynne’s gift for dancing was discovered by a doctor. She had been underperforming at school, so her mother took her to the doctor and explained about her fidgeting and lack of focus. After hearing everything her mother said, the doctor told Lynne that he needed to talk to her mother privately for a moment. He turned on the radio and walked out. He then encouraged her mother to look at Lynne, who was dancing to the radio. The doctor noted that she was a dancer, and encouraged Lynne’s mother to take her to dance school

https://en.wikipedia.org/wiki/Gillian_Lynne

And if you’ve been tempted to sneer while reading about these newfangled ideas about alternate education – “video games and dancing in schools! Hmph, whatever next?!” – note that the first story is from December 2022, while the other story is from sometime in the 1930’s. Everything with Sir Ken Robinson in it is always worth watching, but this video is a particularly fascinating one. Gillian Lynne’s story comes on at around the 15 minute mark, if you’d rather not watch the whole thing, but I hope you do.

But whether it is video games today or dancing a century ago – or whatever else might be around a hundred years from now, for that matter – the point isn’t about how young people learn best. Well, it is, but the first point that all of us would do well to internalize is that everybody learns differently.

And the idea that everybody learns best by sitting in a classroom and listening to a person drone on for hours on end is one that has been rejected by students year after year after year. But because it is cheap, scalable and easy to endlessly replicate, it is now a part of our culture. To the extent that we will think of students who are unable to be a part of this dreary ritual as being not normal.

Of course they’re not normal, none of them are. They’re special, in their own way, as all of us are. That was the message in the brilliant talk given by Sir Ken Robinson. That everybody is talented in their own way.

And his call to action at the end of the talk is the title of today’s blogpost.

Our job isn’t to browbeat our students into downcast and sullen obedience and compliance. Our job is to figure out what motivates them to learn, by figuring out their special talent.

And then to help them make something of it.

The Times, They’re A-Changing Part II

“The putting-out system is a means of subcontracting work. Historically, it was also known as the workshop system and the domestic system. In putting-out, work is contracted by a central agent to subcontractors who complete the project via remote work. It was used in the English and American textile industries, in shoemaking, lock-making trades, and making parts for small firearms from the Industrial Revolution until the mid-19th century. After the invention of the sewing machine in 1846, the system lingered on for the making of ready-made men’s clothing.
The domestic system was suited to pre-urban times because workers did not have to travel from home to work, which was quite infeasible due to the state of roads and footpaths, and members of the household spent many hours in farm or household tasks.”

So begins the Wikipedia article on the putting-out system, a system that is about sub-contracting work.

This system isn’t just suited to pre-urban times, of course, it is also especially suited to pandemic times. The question to ask, of course, is whether it is also suited to post-pandemic times. And an article in the Economist seems to suggest that this may well be the case:


The Industrial Revolution ended the “putting-out system”, in which companies obtained raw materials but outsourced manufacturing to self-employed craftsmen who worked at home and were paid by output. Factories strengthened the tie between workers, now employed directly and paid by the hour, and workplace. The telegraph, telephone and, in the last century, containerised shipping and better information technology (IT), have allowed multinational companies to subcontract ever more tasks to ever more places. China became the world’s factory; India became its back office. Nearly three years after the pandemic began, it is clear that technology is once again profoundly redrawing the boundaries of the firm.

https://www.economist.com/business/2023/01/08/how-technology-is-redrawing-the-boundaries-of-the-firm

If you are a person embarking upon a new career today, not only is it possible for you to earn a fairly comfortable living working out of your home, wherever it may be located in the world, it is actually desirable to do so. Not for all people of course, but the pandemic, and the acceleration of technologies associated with the consequences of the pandemic, has made it possible for you to easily do so.

Part of the reason is, as the Economist article puts it, because of the fact that ‘measuring workers’ performance based on their actual output rather than time spent producing it’ has become progressively easier. That’s not a light sentence to write, by the way, because it hides at least two Nobel Prizes’ worth of work, if not more. And because it has become easier to specify what you want, and how to measure whether it is being done or not, the ‘putting-out’ system seems to be making a comeback of sorts.

A survey of nearly 500 American firms by the Federal Reserve Bank of Atlanta last year found that 18% were using more independent contractors than in previous years; 2% said they used fewer. On top of that, 13% relied more on leased workers, compared with 1% who reduced this reliance.

https://www.economist.com/business/2023/01/08/how-technology-is-redrawing-the-boundaries-of-the-firm

Which, to my mind, means that we need to think about five big-picture questions as a consequence of this trend:

  1. How will this impact patterns of urbanization? This is not an easy question to think about!
  2. How will this impact education? Will there be the evolution of the putting out model in academia also? Why or why not, and what will the equilibrium look like? Also not an easy question to think about, and I now have a better appreciation for inertia.
  3. How will the certification of both learning and working evolve? Will freelancing now carry more weightage on a CV? Or less, as before? How should we think about what to look for on a fresher’s CV?
  4. How far away is ubiquitous VR? How will that impact the dynamics of working/learning from home?
  5. How will this impact work culture and college culture in the years to come, and how should we think about this from a normative perspective?