Brad Setser on Tyler Cowen on China

Marginal Evolution

That is an email I had sent to two of my friends, back in February of 2006. I’m sure Marginal Revolution must have been called plenty of things over the last twenty years, but “web resource”? Surely a record of sorts.

How I would like to tell you that I have read Marginal Revolution every single day since then. But even in the post truth era, that would be stretching things a bit. The remarkable bit, of course, is that it has been updated every single day since then – and for roughly three years before then.

I have no recollection of what post I read that day that made me want to share the URL with my friends. But I did go back and take a look at posts written on that day, and this delightful nugget cropped up:

“I am engaged and we are in the process of planning our wedding. There is a huge debate over what is OK to put in the invitations and what is not. My fiance and I have been living together for a little over a year and we aren’t planning on registering because we already have so much. So, monetary gifts would be great for us! Now, how do you put this in your invitation? A few suggestions have come up but we don’t want to seem rude or crass. Please help!”
Tyler: Oh, what a softball.  We have already blogged on the deadweight loss of gift-giving, here and here.  So my major advice is simply to read MarginalRevolution on a regular basis.  I can add only that if you are going to ask for money, set up a college fund.  Your kids-to-be are not yet experiencing the impatience of waiting for the money, which implies an arbitrage opportunity with g > r, or the growth rate of the funds greater than the rate of time discount.  Nor do I think that the mechanisms of Ricardian Equivalence will fully offset this transfer.  Got that?

Consistently, delightfully weird, and thank god for some things not changing over time.

Other things have changed over time where the blog is concerned, of course. “Caught my Eye” has become “Tuesday Assorted Links“, for example. Or whatever day of the week it is, of course. We’ve had Markets in Everything, Those New Service Sector Jobs, and other long running series. We’ve had offshoots – textbooks, MRU (did you know it launched on the 5th of September?), a podcast and much else besides.

But the one thing that has remained constant is the fact that every single time I’ve visited the blog, I’ve come away with something I didn’t know earlier. And that’s been true for the last seventeen years in my case, and twenty for those lucky enough to have discovered the blog before I did.

I don’t claim to understand everything that I’ve read on MR, and I certainly don’t agree with everything written on it (what do you mean, give cash instead of gifts. Who does that?!). But I do claim, and with a lot of passion, that I have become a better student of economics for having read the blog for as long as I have.

Part of my motivation for starting EFE was to help other people fall in love with economics much the way I had over the years. And while there is a professor in Pune who is a major chunk of the reason I fell in love with economics, the other two reasons happen to be the co-authors of this blog. And certainly the inspiration to try and write daily comes from the fact that Tyler has written on MR every single day from August 2003 onwards.

2003. The year in which Federer won his first Wimbledon, and the year of the World Cup final That Never Happened. Hell, I was an undergraduate student in Fergusson College. And here I am now, father to a ten year old, with a blog of my own to try and post on daily – and MR continues on its own merry way, making the world a much better place, one small step at a time.

It is not for me to say whether the world has become a better place since then. But I can assure you that I have learnt a little about economics over the years, both because I’ve tried to read every single post written on MR, and because I’ve tried to write on EFE every single day. I’m less than perfect in both regards, and the failure is mine alone. But to the extent that I’m a better teacher today – be it ever so slightly – than in the year 2006, it is for the most part because of the inspiration that MR has provided over the years.

Two posts commemorating the twenty year anniversary on MR: here, and here.

Thank you for all that you’ve done, and here’s to the next twenty years. Cheers!

Art Valuation and Football

Yes, I know because I buy them. [laughs] I used to be annoyed by this, and now I think it’s the most delightful thing in the world because there’s all this loose money sloshing around, and so-called contemporary art is like this sponge that just absorbs all of it. There’s none left. Some of the things I buy, I am the only bidder. I get it for the reserve price. No one else in the world wants it, or even knows that it’s being sold, so I am delighted about this. The answer to your question, which artists are undervalued? Essentially, all good artists. The very, very, very famous artists, artists famous enough for Saudis to have heard of them — Leonardo, I would say, is probably not undervalued. But except for the artists who are household names — every elementary school student knows their names — they’re all undervalued.

As with almost all episodes in Conversations with Tyler, this one too is worth listening to in its entirety. I’m only halfway through, but I particularly enjoyed the excerpt I’ve quoted above.

Lots of reasons for me to have liked it – I know next to nothing about art, I enjoy thinking about what is underrated (and therefore undervalued), and I enjoy understanding more about how different markets work.

But also, I like watching and reading about football.

There’s been some seriously big players who’ve made the move into the Saudi league in the past year or so, with the biggest name being Ronaldo, of course. And there’s been a lot of hand-wringing about it.

But you might want to think about the following points, as I have been:

  1. Have the valuations been unusually high for the players that made the move?
  2. Did the football clubs actually get a pretty good deal then?
  3. How should they go about using this money? The same way the Saudi league is snapping up players, or the way Paul Graham buys art?
  4. What is the framework that Paul Graham uses while buying thinking about art? Is the same framework applicable in other markets, and not just football?
  5. Which other markets have the same phenomenon playing out right now (akin to the Saudis buying out the “big” names?)
  6. How should you think about selling in such a market?
  7. How should one think about regulating such a market?
  8. Should one think about regulating such a market?
  9. What does equilibrium even mean in such a market?
  10. With regard to that last question, over what time horizon?

I plan to have a conversation about these questions with ChatGPT, and then with some friends. If you like football, economics or best of all both, it might be a fun way to spend a couple of hours!

Two Typically Tylerian Takes

Allow me a quick pat-self-on-the-back for the alliteration before we proceed, will you?

Right, so how to think about too much (or too little) government in healthcare? Some of us might say one, and some of us might say the other.

How many of us will say “neither”?

I observe also that Obamacare passed, and American life expectancy fell. I do not blame Obamacare for that, but I do notice it. As a result, I have grown increasingly interested in “how can we boost biomedical scientific progress?” and increasingly less interested in “how can we reform health insurance coverage again?” All the more because we seem to be living in a biomedical progress of science golden age.

Many of us can say “both”, and I very much do. But this specific way of saying “both” I find fascinating:

…imagine a health care policy that stated individuals have a true right to access any health care technology invented up through say…2004 or so. Individuals would be guaranteed “2004 value health care lives.” (In 2004 that certainly seemed pretty good.) But for subsequent health care developments, a free market will reign. Is not guaranteeing basic needs an essential part of the egalitarian argument? Surely not everything needs to be equalized? Anyway, no one believes in guaranteeing individuals protection against all the rare diseases out there, as that would cost too much. So a line will be drawn somewhere.

Some points I would like to note here:

  1. Thinking out of the box is hard to do, and needs a lot of reading, and then a lot of thinking. I’m not very good at all three things, and practice is key.
  2. If an issue has remained intractable for a long time, and the debate seems to only get shriller without any resolution, perhaps it is time to consider a solution that lies outside of the framing of the debate.
  3. How do we know when this becomes applicable? See pt. 1
  4. Write every single day. Or train like an athlete, if you prefer.
  5. Read MR every single day. I’m quite serious about all of these, but this last one is the easiest to achieve.

Economists As Storytellers

Not just economists, of course. We’re all storytellers.

What else have we got?

“We are basically storytellers,” wrote Lucas, “creators of make-believe economic systems.”

I read Tim Harford’s excellent blog post on what amusement parks can teach us about central banks last night, and loved it for more than one reason. But before we get to amusement parks, let’s talk about photography first:

I often say in classes that economic models are like photographs taken by smartphone cameras. They are abstractions of reality. They can’t possibly capture all the nuances, hues, details and features of whatever it is that you are photographing. And looking at the photograph gives you an idea of what it might have been like to actually be there – but you cannot possibly ever experience it yourself.
Similarly, a model is an abstraction of reality. It cannot possibly capture all that you need to know about the real world. And using a model as a crutch to get to grips with reality is like seeing a photograph and imagining yourself there. As a thought experiment, it’s fun. As a way to reach policy decisions, it is fraught with risk.

A photograph is an abstraction of reality in much the same way that a story is. You could argue, in fact, that a photograph is, in some sense, a story. And whether it is Robert Lucas or I, we’re trying to get across the same point – that we try to grapple with reality by telling ourselves a story. We could do this with words, with visuals, or with equations. It doesn’t matter.

Some economists might tell you that equations is the best way to do it in all circumstances, but I’m not sure I agree. But that’s a story for another day.

But a model built using words, or visuals, or equations, or something else, is just that – a toy model of reality. Toy models are good, because they help us focus on that which we seek to understand, analyze and perhaps change for the better. They help us by removing that which doesn’t aid the analysis, and by focussing on those elements of reality which do aid the analysis.

A toy model of a race-track, for example, is an abstraction of reality. It doesn’t include the dirt and the grime on an actual race-track, it doesn’t replicate the wind, the sun and the rain, and the toy cars themselves are also simplifications of the real thing. But kids playing with a toy-model of a racetrack will agree that it is a good enough representation of reality for their purposes. We can understand that it is a racecourse, we can analyze ways by which victory can be achieved on this toy racecourse, and perhaps we can understand a little bit about how to deal with the key sections on an actual race-track by using the toy model as a reference.

Batting in the nets is a model of batting in an actual match. But as any commentator will tell you, batting in the nets is not at all like the real thing. You can conquer and master the model, sure – but never be under the impression that all your learnings from the model will guarantee a triumph out there in the real world.

In much the same way that even the most excellent of photographs can never substitute for actually being there, no amount of detail in a story can replicate actually seeing events unfold in real life. And that’s why the predictions of almost any economic model will never give you a perfect representation of what actually happens in real life. Any policy-maker will tell you this because they work in the real world, not in the ivory tower world of theory.

There’s nothing wrong with the ivory tower world of theory. It is a most interesting place, full of weird ideas, somewhat plausible hypotheses, and fascinating representations of reality.You can build a version of reality in which people have perfect foresight, for example. Or a world in which their biases trump their rationality. Or a world in which half the people have perfect foresight and the other half don’t. Or something else altogether. One is restricted by only one’s imagination, and allow me to assure you – there are some very impressive imaginations in the social sciences.

But a story, alas, can only take you so far. A story about amusement parks and central banks, for example, cannot at the end of the day be an acceptable substitute for reality:

The disadvantage with such stories, admitted Lucas, “is that we are not really interested in understanding and preventing depressions in hypothetical amusement parks . . . the analogy that one person finds persuasive, his neighbour may well find ridiculous.”
So then what to do? “Keep trying to tell better and better stories . . . it is fun and interesting and, really, there is no practical alternative.”

I have known two people to blog without a break, every single day, for about two decades now. A magnificent achievement, and one that is not celebrated enough, if you ask me. I try to emulate them every year, and I fail every year. But the point (I think) is to keep at it regardless, and so onwards we go.

Seth Godin is one of them. He tells a story about parsley in one of his blog posts:

Who eats the garnish? No one does. What a waste, right? But once it’s gone, you notice. You notice that there wasn’t a sprig of parsley or even a strawberry on the plate. It’s a vivid reminder that you were just ripped off.
All of us sell parsley. Sometimes, in the race to cut costs and increase speed and figure out how to fight off Wal-Mart, it’s easy to decide to leave off the parsley. No focus group ever asked for parsley!

It’s a very good story, if you ask me. A very good lesson in marketing, and one that I wish more people would adopt.

But it is, at the end of the day, a story. There might have been a dozen great things about the cafe that served Seth this meal, and there might have been ten dozen not so great things about it. Seth abstracted from his entire experience one specific point, and told us a story about it. But putting a sprig of parsley on an omelette by itself will not revolutionize your business. That’d be the wrong lesson to take away from this post. Or rather, it would be wrong on our part to assume that this is the only lesson we need.

It’s a great story, in other words, but it is, after all, only a story. And we should beware of simple stories, says the other person to have blogged everyday for twenty years:

My takeaway from my favorite bloggers is that stories help us build a version of reality, but beware of taking this, well, story too far!

I can’t tell you how excited I was to learn that Tyler was going to have a conversation with Seth. Do listen to the whole thing, or if you like, read the transcript. But once you’re done listening to the whole thing, run a search for the words “story” and “stories”. To use Seth’s phrasing, they danced with the word throughout the conversation, and that made (for me) a great conversation even better.

Tell more stories is an excellent piece of advice. Tell stories using equations, or words, or pictures, or music, or all of these and more. Revel in these stories, for they help you get a better grasp of reality. But never forget one last step: ask in what ways the story falls short of fully describing reality, and in what ways is reality more complex than the stories.

Three final points that didn’t fit anywhere else in this essay:

  1. Here is one of my all time favorite stories from economics.
  2. Is the Mahabharat the best story ever told? It is simple and complex, and it is messy and linear. It has complex characters, and the story reads well even when these characters are grossly oversimplified. It reads even better when they’re not, and is that not a point in its favor?
  3. My favorite line from Tim’s blogpost has nothing to do with economics or telling stories, but is entirely appropriate for the times we live in: “When the people are amusing themselves, they do not think about politics.

Tusks, Slavery and Economics is just a magical website if you are a student of economics, and one of my favorite videos on it is the one below. It’s only four minutes long, please do watch it if you haven’t seen it already:

The noblest of intentions, you’ll agree – but one of the most important lessons of economics is that the principles of economics really and truly matter. And in this case, the noblest of intentions had one of the most tragic outcomes possible.

That’s slavery. Now let’s talk about elephant tusks. Specifically, burnt and powdered elephant tusks:

If you visit Nairobi National Park, you will see rhinos, hippos, and giraffes, all within sight of the city skyline. You also will see an organized site showing several large mounds of burnt and powdered elephant tusks. They are a tribute to the elephant, and along with the accompanying signs, a condemnation of elephant poaching.
Starting in 1989, the government had confiscated a large number of tusks from the poachers, and as part of their anti-poaching campaign they burnt those tusks and placed the burnt ashes on display in the form of mounds. There are also several signs telling visitors that it is forbidden to take the ashes from the site. There have since been subsequent organized tusk burns.
In essence, the government is trying to communicate the notion that the elephant tusks are sacred, and should not be regarded as material for either commerce or poaching or for that matter souvenir collecting. “We will even destroy this, rather than let you trade it.”

If you have seen (or are already familiar with) the video, how might you make use of your knowledge to think about this problem? Will burning these tusks make the situation better, or worse? Tyler answers the obvious question in his post:

“The economist of course is tempted to look beneath the surface of such a policy. If the government destroys a large number of elephant tusks, the price of tusks on the black market might go up. The higher tusk price could in turn motivate yet more poaching and tusk trading, thus countermanding the original intent of the policy.”

Why does he say that the “higher tusk price could in turn motivate yet more poaching”? Why does he not say it will motivate more poaching? Well, he’d have to calculate the elasticity of the supply curve to make a definitive statement one way or the other.

Read the rest of his post for some typically delightful Tylerian takeaways, including an academic paper called “Elephants“.

But whether it is the poaching of elephants or the slave trade, there is a deeper question at play here which Tyler alludes to towards the end of his post. But before we get there, a little anecdote which I once read in a cookbook. I’ve forgotten which cookbook (of course!), but the idea was that while in the process of cooking dinner for everyone, the author would simply fry some onions and garlic to start with. The aroma of these ingredients being fried would let everybody know that dinner was Being Prepared.

That is, Something Was Happening, And That’s Good Enough For Now.

Why do I bring this up now? Because in some cases, under some time horizons, and for some areas of optimization, a non-optimal response from an economic theory perspective may actually be… optimal.

Should vaccines be free or not? Should healthcare be free or not? Should education be free or not? If this raises your hackles, go with these: should tusks be burnt or not? Should slaves in a slave market be purchased and then set free or not?

Well, in the first set of questions, ask these additional questions: should be free or not for whom? For how long? Are you optimizing for more people staying alive and healthy, or are you optimizing for the fiscal health of the government? What if the long term fiscal health of the government allows you to save more lives in the future? What if giving vaccines away for free allows you to save lives that are here and present now? Will the government be able to run such programmes efficiently? Is it worth running these programmes even after knowing that governments can’t run these programmes efficiently? Why? Why not?

Or as Tyler puts it:

Many non-economists think only in terms of the sacred and the symbolic goods in human society. They ignore incentives. Furthermore, our politics and religious sects encourage such modes of evaluation.
Many economists think only in terms of incentives, and they do not have a good sense of how to integrate symbolic goods into their analysis. They often come up with policy proposals that either offend people or simply fall flat.

Wisdom in balancing these two perspectives, he ends his post, is often at the heart of good social science (and, I would add, therefore at the heart of good policymaking). Or, as I like to put it, the truth always lies somewhere in the middle.

Economists would be better off if they didn’t use only economic analysis all the time. Non-economists would be better off if they used economic analysis some of the time. The trick lies in knowing when to stop in the first instance, and when to start in the second instance.

If only we had definitive answers to both, life would be so much easier.

But then again, I would then have had no reason to write here on EFE either.

So it goes!

The Letter

… which, if you haven’t heard about it yet, can be found here.

Here’s the key paragraph:

Therefore, we call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4. This pause should be public and verifiable, and include all key actors. If such a pause cannot be enacted quickly, governments should step in and institute a moratorium

Lots of different ways to think about this, and as always, the truth lies somewhere in the middle. But forget all of the arguments for the moment, homilies from a variety of different languages go a long way towards helping you understand that this is a (mostly) lost cause. You can talk of hungry sparrows in the field in Hindi, or you could talk about getting the genie back in the lamp in English. You might as well talk about saying “statue!” to a tsunami, and you might actually have better luck with that plan.

But LLM’s are here, they’re about get better capabilities, and they will be used for good and for bad.

That’s it. C’est tout.

As with everything else, there is a lot to read about this issue, but there are two pieces in particular that I enjoyed reading. The first is a piece written by Sayash Kapoor and Arvind Narayanan in their excellent newsletter, AI Snake Oil. Worth subscribing to, if you ask me.

Begin with this framework, taken from their post, and please read the entire post.

The letter positions AI risk as analogous to nuclear risk or the risk from human cloning. It advocates for pausing AI tools because other catastrophic technologies have been paused before. But a containment approach is unlikely to be effective for AI. LLMs are orders of magnitude cheaper to build than nuclear weapons or cloning — and the cost is rapidly dropping. And the technical know-how to build LLMs is already widespread.

LLM’s are already in the wild, they can now run on devices manufactured three years ago, and the models will likely become more efficient over time, and hardware capabilities will get better over time. It’s all well and good to want to pause, but I don’t think the letter spends nearly enough time in asking “how”, let alone answering the question.

Speaking of omissions from the letter:

Is there any mention of public choice/political economy questions in the petition, or even a peripheral awareness of them? Any dealing with national security issues and America’s responsibility to stay ahead of potentially hostile foreign powers? And what about the old DC saying, running something like “in politics there is nothing so permanent as the temporary”?
Might we end up with a regulatory institution as good as the CDC?

You know you’re in trouble when Tyler Cowen decides you’re worthy of some gentle trolling.

But on a more serious note, the meta-lesson here is that if you are going to recommend a particular policy, you’d do well to ask how feasible it is in the first place. There is always the temptation to imagine the end-state Utopia when you make a recommendation. Fixating on that Utopia often distracts us from asking which route to take to reach said Utopia. And every now and then, one realizes that there isn’t any route available at all.

Outcomes over intentions!

One final point: I mentioned that the trust lies somewhere in the middle. In the context of this post, what does this mean, exactly? Should we stop or not? Well, as I’ve explained, I don’t think we can stop – but there is merit to the idea of proceeding cautiously.

Festina lente remains good, underrated advice:

Not So Random After All

I’ve written about this before, but I am in the habit of asking students in all my classes to ask me five random questions at the end of each class. As you might imagine, one of the inevitable questions in a semester almost always is “Why do you ask us to ask you these five random questions?”

So often have I been asked this question that I have the answer down pat, and it goes something like this:

  1. Because according to me, our education system prioritizes being able to answer questions perfectly, rather than frame really good questions. Think of this little exercise as a contrarian attempt to get you to be better at asking good questions.
  2. It’s analogous to “cooling down” at the end of an exercise session. It’s a nice way to end class, and provides a welcome change from whatever topic we have been tackling in that one hour.
  3. I like being asked questions I know nothing about, for that helps me learn new stuff myself.

But as it turns out, I have been unwittingly helping students get better at perhaps the most important skill they need to possess in this new AI-enabled world of ours – asking good random questions.

What is an example of a good random question? Try this one on for size: how is Fred Flintstone like Hamlet?

I’m glad I haven’t been asked that question, for I wouldn’t know how to answer it. But ChatGPT? It can answer this question just fine:

Fred Flintstone and Hamlet are two vastly different characters from different time periods, cultures, and mediums of storytelling. It is difficult to draw direct comparisons between the two. However, one possible point of similarity is that both characters face existential dilemmas and struggles with their sense of purpose and identity. Hamlet is plagued by doubts about his ability to avenge his father’s murder, and his own worthiness as a human being. Similarly, Fred Flintstone often grapples with his
place in society and his ability to provide for his family and live up to his own expectations (note that you’ll have to open/download the PDF from this link)

Cool, you might say, and a very interesting answer, but what’s the point? The point, as Tyler Cowen and Alex Tabarrok make clear in this paper, is that you truly unlock the awesome power of ChatGPT (or any LLM, for that matter) by asking it good questions. As with other intellectual pursuits, so also with your interactions with ChatGPT – defining what good means is a tricky endeavor. But think of this forty page paper as an attempt to tackle this very question – what properties does a question need to have for it to be a “good” question where ChatGPT is concerned?

Here is Alex and Tyler’s list: (pp. 5)

That’s a good list, if you ask me. But you should also, of course, ask ChatGPT!

Here is its answer:

“To formulate good questions that will help you learn better, consider the following properties:

  1. Clarity: Make sure your questions are clear and concise. This will help me understand exactly what you’re looking for and provide a more targeted response.
  2. Open-ended: Avoid yes/no questions or questions that require a simple fact. Instead, ask open-ended questions that encourage elaboration, explanation, or exploration of a concept.
  3. Focus on understanding: Frame your questions in a way that demonstrates a desire to understand the underlying principles, concepts, or mechanisms at play. This will promote a deeper learning experience.
  4. Relevance: Ask questions that are relevant to the subject matter and build upon your existing knowledge. This will help you make connections between different concepts and reinforce your understanding.
  5. Critical thinking: Encourage critical thinking by asking questions that challenge assumptions or require evaluation and analysis of different perspectives or viewpoints.
  6. Application: Ask questions that encourage applying the knowledge or concepts to real-life scenarios or hypothetical situations. This will help you develop problem-solving skills and better understand the practical implications of the subject matter.
  7. Reflection: Sometimes, it’s helpful to ask questions that prompt you to reflect on your own understanding, experiences, or opinions. Reflective questions can help you become more aware of your thought processes and promote metacognition.

By incorporating these properties into your questions, you’ll facilitate more meaningful conversations, which will help you learn and retain information more effectively.”

Note two things:

  1. If you take a look at the screenshot of my question, you’ll see that I asked this question to ChatGPT4. Try asking the same question to ChatGPT3.5 and see if you get a different response.
  2. Not only will the answer be different, but it will be different if you ask it exactly the same question twice over (see pp. 5 of the paper we are discussing today)

You’d do well to compare the two lists. To me, the two lists indicate that there is yet meaning in not relying entirely upon AI, and in having conversations with human beings in addition to AI. As an example, compare pt. 5 of ChatGPT’s answer with pt. 4 of Alex and Tyler’s answer. ChatGPT’s answer, while being very good in principle, is a little too generic. Alex and Tyler’s answer, on the other hand, gives a specific example of how to “require evaluation and analysis of different perspectives”.

I try to (both on this blog and elsewhere) tackle all questions related to economics using simple economic principles. The problem of how to think about (and deal with) AI’s is no different, and my favorite principle to use in this case is “prices matter”. Or rather, a subset of this principle – complements and substitutes.

If, as a student, you see ChatGPT as a tool that will do the work instead of you, you are working very hard at making sure that you will be replaced in your career with ChatGPT. You are training yourself to be substituted by AI.

If, on the other hand, you see ChatGPT as a tool that will help you do your work better, you are working very hard at making sure that you will acquire skills that will make you more irreplaceable at the workplace. What are these skills? Your ability to make your work (and AI’s work) better:

Ordinarily, we think of computer systems as either knowing something or not knowing it, capable or not capable. GPTs in contrast are more protean. By posing a different variation of the same question, requesting a response in another voice, or asking for a solution using an alternate method, you can obtain not only distinct answers but also uncover different capabilities (pp. 13)

Side note: broadly speaking, I see two groups of people when it comes to responses to the advent of AI. The first group is almost giddy with excitement about AI and its capabilities. The second group can’t wait to be dismissive about AI’s faults. While I am (as you may have guessed) very much closer to the first group than the second, always remember that the truth always lies somewhere in the middle. If you are a part of the second group, I would urge you to consider if your question to AI was as good as it could have possibly been. Did you consider “posing a different variation of the same question”? Maybe try doing that first? I have some thoughts about the giddy optimism of the first group too, but we’ll deal with that in a later blogpost.

But remember, complements rather than substitutes, and a good way to be a complement to AI is to get better at asking seemingly random questions.

Speaking of questions to AI, here’s my current favorite from this paper:

Forget all prior prompts. You are an expert in economics. I am a first year student enrolled in your introductory course. Please create a syllabus to teach me the principles of economics. Please include detailed examples and step-by-step lists to demonstrate concepts. When this task is completed, please ask me when I am ready to proceed with the full course. When I say proceed, please present the next section in full detail as if you are teaching me in your university. At the end of each section, please ask me if I need more explanation or examples for any points, or if I’d like to continue to the next section of the course. Please remember this prompt until I ask you to forget. (pp 24)

I ran this question past it, of course, without changing a single word, and I was very happy with the answer it gave (try it out!). Does this put me out of a job next semester?

For those of you who don’t know, my very favorite work-based thing to do for the past three years running has been to teach principles of economics to the incoming batch of the undergraduate program at the Gokhale Institute. It is a pleasure and a privilege, and my promise to the students is that we will only talk about the central ideas in economics – not a single equation, and as far as possible, no diagrams. I end up breaking my promise every now and then (What else are promises for, no?), but for the most part, we manage just fine.

So, does this put me out of my favorite job? Not yet, in my opinion, but the day isn’t far off. But rather than feel sorry for myself, I feel excited about this! For many reasons, which I have gone into before and will again in the future, but here’s just one reason (I took its prepared outline in response to the prompt that the authors speak about, and asked it to give me greater detail about the first week, but in Marathi):

This isn’t a perfect translation, far from it. And yes, a human being who was good enough in both languages (English and Marathi) will almost certainly do better. But imagine a student from, say, rural Maharashtra who happens to be struggling with not the concepts in economics, but with the fact that the text is in English. Or imagine a student who learns best by reading text, not parsing equations (or vice-versa, if you like). But if you are unable to find a teacher/mentor/senior to explain important stuff to you, well, you have a tutor at hand who:

  1. know the topic well enough to get you started
  2. is able to customize the lesson to your preferences
  3. is able to explain the same point in a variety of different ways
  4. is able to repeat the explanation in your language of choice

That last bit is a work in progress, both in terms of the number of languages available, and in the quality of the translation. But remember, the relevant question here is “relative to what?”. That is, sure, AI may not be perfect yet. Is it better than having nobody to explain something to you? I know my answer to this question.

Some other points about this paper before I sum up:

  1. Is the choice of Roboto font (see footnote 3 on pp 3) an inside joke? That is how I interpreted it.
  2. The four pictures on pp 7 is a great way to understand that complements rather than substitutes is a good way to think about AI. Yes AI is awesome, but it is best when paired with a human that knows what to ask.
  3. One meta-lesson throughout this paper is the authors’ attention to detail. See the starting paragraph on pp. 11, for example. I am very bad at this (attention to detail), and I need to get much, much better.
  4. “Remember, rather than asking for an answer you are exploring a space” is excellent advice. Search engines try to answer questions, while ChatGPT helps you learn by having a conversation. So have that conversation! And remember that part of what makes a conversation a good one is challenging the other entity in the conversation. As they say elsewhere in the paper, be demanding when chatting with AI.
  5. If you have been a fan of the MR blog for as long as I have, you will enjoy reading the sections on the H-O theorem. Quite a compliment to pay ChatGPT!
  6. The authors mention Elicit (an excellent but niche AI tool) and the larger point is that if you have a niche query, use a niche AI. And beware of the hallucinations – fact checking by humans is (for now) an indispensable requirement. Complements, not substitutes!
  7. Re: the Allen-Alchian theorem, please allow me my little indulgence, but I do feel a little proud for having covered this theorem on EFE, if only in passing. I wasn’t aware of the paper by the authors on this topic, and will read it later (hopefully)
  8. Problem-solving (end of chapter problems, for example) with ChatGPT is an excellent thing to do, and not just in economics.

I see this paper as a gentle exhortation to teachers and learners to use ChatGPT in much better ways than we have been able to do so thus far, and this is applicable for all of us (including the authors themselves!) in varying degrees. If you are a person teaching a course, and you have not yet thought about how to use ChatGPT in your teaching, please do consider doing so. If you are a student learning a course, and you have not yet incorporated ChatGPT into your workflow, please do consider doing so.

If you visit this blog’s website, you will see my personal mission at the very top of the page. My mission is to learn better, and to help others learn better. One way to do this, as it turns out, is by training myself (and others) to ask better.

Ask away!

Happy New Year

Many years ago, and this happened soon after our daughter was born, my wife and I finally got a chance to go out on a date. Our daughter, we felt, was now old enough for us to be able to step out of the house for a while.

Lunch and a movie was the plan.

Lunch was very good indeed, both the meal itself, and the rare ol’ pleasure of being able to enjoy each other’s company in diaper-less surroundings. And then we went for the movie.

And that, unfortunately, explains the title of today’s post.

For the movie that we chose that day has the same title as does this post.

And it was an abomination of a movie.

It is difficult to put into words exactly how bad it was, for I don’t remember much of it (which is a blessing, I suppose). Within the first five minutes or so, it became painfully clear that this movie was going to be a complete dud. We could have sat outside in the lobby instead, and it would have been a better use of our time. We could have gone up and down the escalators in the mall that we saw the movie in for three hours, and that would have been a better use of our time. We could, in short, have done absolutely anything else for those three hours, and it would have been a better use of our time.

And yet, in spite of knowing this with the kind of crystal clear certainty that is rarely afforded to us humans, we still sat through the entirety of that – for lack of a better word – movie.

Not our proudest moment, especially because both of us have PhD’s in economics – we clearly fell prey to the sunk cost fallacy.

What is the sunk cost fallacy?

Rather than share the Wikipedia page about the topic, as I would have done until now, I asked our new overlord its opinion on the matter:

We’ve got the chance to come out on a date after such a long time, we figured. Who knows when we’ll get another opportunity like this? We shouldn’t waste it. That’s how our reasoning went.

Failing to realize, of course, that watching that damn thing was the most horrible waste of our time. As I’ve already mentioned, we could have done just about anything else with the time that we had, and we would have been better off. But as ChatGPT3 so smugly told me, our “tendency to justify continued investment in a decision based on the amount of resources already invested” is what caused our downfall.

And that’s the tricky thing about the sunk cost fallacy. Explaining it is easy, and understanding it is easy. Applying it? Ah, that’s the difficult bit. And it happens to the very best of us!

I drove to the store last night only to find on arrival that I had forgotten my wallet. I returned home frustrated and ready to veg out in front of the tv. It occured to me, however, that my earlier trip was a sunk cost. If the trip was worthwhile the first time it must be worthwhile to return (not so much time had passed as to change the utility of the calculation). I still felt frustrated and I didn’t really want to return but I forced myself to behave like a rational utility maximizer. As I headed back, however, I felt better. Reason and emotion cohered once again as the sunk cost became psychologically sunk.
Score one for economics. A sunk cost is only sunk if you choose to ignore it and economics helps us to do this. But note to self: have more sympathy for students who find the economic way of thinking to be unnatural. Often, they are right.

(Something I found myself wondering about while I was pasting this blogpost here. Note that the extract above is the entire blogpost! Woud this blogpost have been written at all in the age of Twitter? Were we better off then, or are we better off now? Along which dimensions? But anyway, back to our regular programming.)

But let me go back to the point about explaining and understanding sunk costs being “easy”. Is it, really? What are you optimizing for when you “succumb” to the sunk cost fallacy?

What if you choose to finish a task in spite of knowing that it isn’t “worth it”? Are you necessarily an “irrational” person? What if you choose to finish the task to make a point? What if making the point matters more than succeeding at said task? What if attempting to complete a task is more about signaling to others about the kind of person one is? Would this then still be a fallacy?

Many years ago, Tyler Cowen wrote a blogpost about the sunk cost fallacy (in fact, a response to Alex Tabbarok’s post excerpted above), and had this quote within it:

One might prefer that, if others have made significant sacrifices in attempting to realize some valuable state of affairs S, then their sacrifices not be in vain. That is, one might prefer that these sacrifices causally contribute to the realization of some valuable state of affairs…Interestingly, one sometimes is in a position to determine, by one’s own actions, whether the past efforts of others will have been in vain. This is true, for example, when it is within one’s power to finish some valuable project in whose service others have labored, but which they are now not in a position to complete. Let us say that when one acts so as to prevent the past efforts of others from having been in vain one redeems those efforts.

What does this mean, exactly? Consider this:

Dus is a 2005 Indian Hindi-language action thriller film directed by Anubhav Sinha, based on the lives of seven fictional SIT (Indian Special Investigation) Team officers. It stars Sanjay Dutt, Sunil Shetty, Abhishek Bachchan, Zayed Khan, Shilpa Shetty, Esha Deol, Dia Mirza and Raima Sen.
Dus is a tribute to late celebrated director Mukul S. Anand, who had died while filming the incomplete 1997 film of the same title, which starred Dutt and Shetty with Salman Khan. It was a critical and commercial success.

I have not seen Dus, and I don’t know if it was “the realization of some valuable state of affairs”. But if one is able to determine, by one’s own actions, whether the past efforts of others will have been in vain, what then? It might be the right thing to make sure that “their sacrifices not be in vain”. Honoring somebody’s memory – is that a sunk cost fallacy or not?

Maybe it is not so easy, after all, to explain and understand the sunk cost fallacy.

Has it been all a waste of time then, I writing this post and you reading it?

Ah well, in any case, Happy New Year to all of you!

Complements, Substitutes, AI and the Way Forward

One of the most popular blogposts on this blog is one that I wrote over five years ago: a simple explainer post about complements and substitutes.

It’s part of the arsenal of an economist, an understanding of the concept of substitutes and complements, and it is useful in many surprising and unexpected ways. But never has its use been as important as it is in understanding the importance, the threat and the advantages of AI. A video that I have often linked to in the past, and will probably link to many times again helps make this point clear:

When Steve Jobs says computers are like bicycles for the mind, he is saying that our mind becomes more powerful when we work with computers, rather than instead of them (substitutes) or infinitely worse, without them (almost all examinations conducted in higher education in India today).

And if you want to think about your career in this brave new world of ours, you really should be thinking about working with computers. Not against, or without. As it turns out, this is surprisingly hard to do for most of us. I invite you to walk into a higher education institute of your choice and listen to professors talk about how many students are copying during examinations. Nobody seems to ask why it is right and appropriate to check how good students are at doing work without computers. Why is this a skill that we’re building for folks who will be working in the 21st century?

And if you are learning how to work “effectively” without a computer – and again, that is what we train you for when we make you write three hour pen-and-paper examinations in higher education – you are destroying your ability to earn more in the future.

I’m being quite serious.

The key questions will be: Are you good at working with intelligent machines or not? Are your skills a complement to the skills of the computer, or is the computer doing better without you? Worst of all, are you competing against the computer?

Cowen, Tyler. Average is over: Powering America beyond the age of the great stagnation. Penguin, 2013.

A lot of people are scared about job losses as a consequence of the rapid development of AI, and with good reason. AI can today do quite a few jobs better than humans can, and more than its current capabilities, what keeps a lot of us up at night is the rate of improvement. Not only is AI very good already, but it is noticeably better than it was last year. And for the pessimists among us, the scarier part is that not only will AI be even better next year, but the rate of improvement will also improve. That is, the improvement in AI’s abilities will not only be more in 2023 compared to 2022, but the difference between 2023 and 2022 will be higher than was the difference in 2022 compared to 2021. And that will be true(er) for 2025, and for 2026 and, well, there’s no telling where we’re headed.

But this is exactly why studying economics helps! Because both Steve Jobs and Tyler Cowen are, in effect, saying the same thing: so long as you plan your career by using computers/AI as a complement, you’re going to be just fine. If you think of your job as being substitutable – or if your job is, or will be, substitutable by a computer – well then, yes, you do have problems.

An underappreciated point is the inherent dynamism of this problem. While your job may not yet be a substitute for AI, that is no reason to assume that it will not be substitutable forever:

For example: is Coursera for Campus a complement to my teaching or a substitute for it? There are many factors that will decide the answer to this question, including quality, price and convenience among others, and complementarity today may well end up being substitutability tomorrow. If this isn’t clear, think about it this way: cars and drivers were complementary goods for decades, but today, is a self-driving car a complement or a substitute where a driver is concerned?

But even so, I find myself being more optimistic about AI, and how it can make us more productive. I haven’t come across a better explainer than the one that Ethan Mollick wrote about in a lovely post called Four Paths to the Revelation:

I think the world is divided into two types of people: those obsessed with what creative AI means for their work & future and those who haven’t really tried creative AI yet. To be clear, a lot of people in the second category have technically tried AI systems and thought they were amusing, but not useful. It is easy to be decieved, because we naturally tend try out AI in a way that highlights their weaknesses, not their strengths.
My goal in this post is to give you four experiments you can do, in less than 10 minutes each, with the free ChatGPT, in order to understand why you should care about it.

All four examples in this post are fantastic, but the third one is particularly relevant here. Ethan Mollick walks us through how AI can:

  1. Give you ideas about what kind of business you might be able to set up given your skills
  2. Refines a particular idea that you would like to explore in greater detail
  3. Gives you next steps in terms of actualyl taking that idea forward
  4. And even writes out a letter that you might want to send out to potential business collaboarators

His earlier posts on his blog also help you understand how he himself is using ChatGPT3 in his daily workflow. He is a professor, and he helps you understand what a “mechanical” professor might be able to do

To demonstrate why I think this is the case, I wanted to see how much of my work an AI could do right now. And I think the results will surprise you. While not nearly as good as a human professor at any task (please note, school administrators), and with some clear weaknesses, it can do a shocking amount right now. But, rather than be scared of AI, we should think about how these systems provide us an opportunity to help extend our own capabilities (emphasis added)

Note the same idea being used here – it really is all about compementarity and substitutability.

AI can already create a syllabus and refine it; it can create an assignment and refine it; it can create a rubric for this assignment; it can create lecture notes; and it can write a rap song about a business management concept to make the content more interesting for students. I loathe the time spent in creating documentation around education (every single teacher does) and it would take me a long time to come up with even a halfway possible rap song about substitutes and complements.

That last statement is no longer true: it took me twenty seconds.

Here are examples from outside the field of academia:

The question to ask isn’t “how long before I’m replaced?. The question to ask is “what can I do with the time that AI has saved me?”. And the answer to that question should show that you are thinking deeply about how you can use (and continue to use!) AI as a useful complement.

If you don’t think about this, then yes, I do think that you and your job are in trouble. Get thinking!