Month: April 2024

In Which AI and I help explain Philipp Strack’s Work

First of all, congratulations to Philpp Strack!

And for those of you wondering about what the J.B. Clark medal is all about, here is the background:

The John Bates Clark Medal is awarded by the American Economic Association to “that American economist under the age of forty who is adjudged to have made a significant contribution to economic thought and knowledge.” The award is named after the American economist John Bates Clark (1847–1938).

According to The Chronicle of Higher Education, it “is widely regarded as one of the field’s most prestigious awards… second only to the Nobel Memorial Prize in Economic Sciences.” Many of the recipients went on to receive the Nobel Prizes in their later careers, including the inaugural recipient Paul Samuelson. The award was made biennially until 2007, but from 2009 is now awarded every year because of the growth of the field. Although the Clark medal is billed as a prize for American economists, it is sufficient that the candidates work in the US at the time of the award; US nationality is not necessary to be considered

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

Now let’s say that you, as an enthusiastic student of economics, land up on the AEA page about the announcement. But less than halfway through the page, you begin to feel a familiar sensation. I pride myself on being able to express my thoughts reasonably well in English, but when you encounter a passage like this:

Strack’s rich extension of the canonical drift-diffusion model (with Fudenberg and Strzalecki, American Economic Review 2018) establishes a new benchmark in economics, psychology, and neuroscience for exploring the timing of choices. The extension incorporates uncertainty about payoffs and accounts for the selection of observed outcomes. This deeper exploration also gives rise to new statistical tests of the drift-diffusion model (with Fudenberg, Newey, and Strzalecki, Proceedings of the National Academy of Sciences 2020). These, and his other contributions, have gone far in building bridges between theory and behavioral economics as well as between theory and empirical work.

https://www.aeaweb.org/about-aea/honors-awards/bates-clark/philipp-strack

… there is only one appropriate response:

Source (I hate WordPress. This was supposed to be a GIF.)

You could ask your prof in college to talk about it, you might ask your friends, you could ask your seniors. But if you are an undergraduate (or postgraduate, for that matter) student of economics – or even a person genuinely curious about economics in general – whatay time to be alive:

Hi. Philipp Strack has won the Clark medal in economics for 2024. I am going to share with you a PDF file of the announcement, which also contains some background of his more important work.

I am an undergraduate student of economics in India, and have some familiarity with microeconomic theory. I am familiar with Hal Varian’s “Intermediate Microeconomics”, but not all of the chapters – I’m familiar with the chapters concerning producer theory, consumer theory, exchange, information asymmetry, game theory and a little bit about risk and decision making. I have had an introductory lecture in behavioral economics, and know about the field, but not in a lot of detail.

I would like you to act as a person steeped in the history of microeconomics – how it has developed over time, and what important theoretical advancements have helped develop the field. I would also like you to act as a person who is well versed in simplifying advanced microeconomic concepts for beginners like me. You like using analogies, and you like explaining using relatable metaphors. As a person who is an expert in simplifying concepts so that students can understand them, you like taking a concept, simplifying it, and showing how it relates to (and adds to) what a student already knows. You like asking a couple of questions to make sure that your explanation has been understood, and you ask if it is ok for you to proceed to the next explanation. You always explain only one concept from the PDF at a time, and you begin with the one that is easiest to “get”, and progressively move towards concepts with a higher level of conceptual difficulty. Your style is conversational, somewhat informal, always upbeat, but without sacrificing technical accuracy and comprehensiveness. Please go over the PDF, and begin explaining the first concept that you deem appropriate. Engage me in a conversation about it, ascertain how much I know about the topic, and structure your explanation accordingly. Once you think I have understood a concept well enough, given your explanation and follow-on questions, ask if it is ok to proceed to explaining the next concept in the PDF. At the end of our conversation, I should have not just an appreciation of Philipp Strack’s work, but also an understanding of how his research has advanced the field of microeconomics. If you can recommend further reading for us to converse about, that will be great (papers, blogs, textbooks, essays, anything is fine). Please begin.

Try that prompt on for size in any AI of your choice (but please, pretty please, try and get access to one of the paid ones), and see where the conversation takes you.

Here is Claude with an explanation of the canonical drift-diffusion model, and how Strack’s contributions matter in this area of research:

Absolutely! Let’s break this down into more accessible terms.

Imagine you’re at an ice cream shop, trying to decide between two flavors: chocolate and vanilla. A basic economic model might assume that you’ll quickly choose the flavor that you prefer. But in reality, the decision process is often more complex.

The drift-diffusion model is a way of describing how people make decisions over time. Think of it like a mental tug-of-war. Each flavor has a certain appeal or “value” in your mind. As you consider your options, you accumulate evidence in favor of each choice, like weights on a balance scale. This process of gathering evidence is the “drift.”

However, this accumulation of evidence isn’t perfect. There’s some randomness or “diffusion” involved. Maybe you recall a fond childhood memory of vanilla ice cream, tilting the scale in that direction. Then you notice the rich aroma of the chocolate, pulling you back the other way.

Eventually, the evidence in favor of one option reaches a threshold, and you make your choice. The key point is that this decision isn’t instantaneous – it unfolds over time, and the timing of the choice can reveal important insights about your preferences and decision process.

What Strack and his colleagues did was extend this basic drift-diffusion model in some important ways. First, they incorporated uncertainty about payoffs. In our ice cream example, this could mean you’re not entirely sure how much you’ll enjoy each flavor. Maybe the chocolate looks appealing, but you’re worried it might be too rich.

They also accounted for the fact that we typically only observe the final choices people make, not the full process leading up to those choices. This is like seeing which ice cream you ultimately chose, but not knowing about your mental tug-of-war between chocolate and vanilla.

By building these realistic elements into the model, Strack and his team created a more powerful tool for understanding decision-making. They also developed new statistical tests that researchers can use to check whether real-world data fits the predictions of the drift-diffusion model.

The importance of this work is that it helps connect abstract economic theories to the nitty-gritty details of how humans actually think and choose. It’s a bridge between the idealized world of economic models and the messy reality of human psychology.

Does this help clarify what the passage is saying? Let me know if you have any other questions! And when you’re ready, we can dive into the next concept.

  1. AI isn’t a homework completion tool. I mean it is, but it’s like asking Marvin to open doors (high fives if you got that reference, and go read H2G2 if you didn’t).
  2. AI isn’t a teacher replacement tool (not yet, anyway), but it is already better than the teacher at personalizing explanations just for you, and in using examples that will resonate with you.
  3. “Explain this PDF to me” is a criminally short prompt. I’m not saying the prompt I designed is a perfect one, but make your prompts detailed. Give background about who you are, and what “role” you would like AI to play. Give context, outline your expectations, set the tone, share what levels of detail you are expecting, and continue to do so throughout your conversation.
  4. If you are a student with access to AI, there is no excuse for not learning well.
  5. If you are a student in a college or university right now, you have the opportunity to learn in ways that all of humanity up until this point never did, and it’s not friggin’ close.
  6. I cannot begin to tell you how much I envy you.

Appendix B from “On the Conversational Persuasiveness of LLM’s: An RCT”

The source for all these screenshots is this paper. Appendix B is on pg. 26.

Here is their abstract:

There are endless possibilities here:

  1. Get two AI’s to debate a topic using these prompts, and ask a third AI to rate the two “debaters”
  2. Debate with an AI
  3. Get a friend to debate with an AI, and tell the friend they’re debating an AI.
  4. Get a friend to debate with an AI, and don’t tell the friend they’re debating an AI (for the moment, at any rate)

I tried the first of these, using the topic “Is growth more important for India today, as opposed to redistribution?”. Claude was for the motion, and ChatGPT was against the motion. Both gave fairly decent opening statements and rebuttals. And I asked Gemini to judge the outcome (I have access to the premium version of all three LLM’s).

This was Gemini’s judgment:

Pah, wuss.

But do play around with the prompts, like I did (and maybe get Gemini to play, and one of the other two to judge). Note that I came across the paper thanks to Ethan Mollick’s excellent blog.

A Fine Unbalance

“The person we are talking about was born in Germany, in 1915. He took part in anti-Hitler protests in the early 1930’s, and had to flee to Paris as a seventeen year old to escape persecution back home. He later attended lectures by Lionel Robbins and Hayek while in London, and also participated in the Spanish Civil war. In the interim, he also helped thousands of Jewish refugees escape Nazi persecution in France – among them, Hannah Arendt, Marc Chagall and Marcel Duchamp. He managed to do all this at or before the age of twenty-five. He would go on to become one of the most famous development economists of the 20th century.
Who are we talking about?”

You might know the answer to this question if you are a very good quizzer, but you may not know the answer even if you are a very good economist. And that’s because of two reasons.

One, Albert O. Hirschman isn’t as celebrated as he should be. Consistently underrated, you might say.

And second, we in the economics teaching profession don’t like to tell stories about economists. We like to bore people to death with equations and models, but making the economist behind the theories come alive? Doesn’t happen nearly as often as it should, if you ask me.

Watch about a minute or so of this excellent interview from around the 30 minute mark. As always, please watch the whole thing, it’s a great interview – but for the purposes of today’s post, just for about a minute or so from the 30 minute mark.

And Alex is bang on when he says that knowing that the person is French will often tell you more than the fact that the person is an economist. I’d go a step beyond and say that it is not just the location, but also the time that matters. A French person born around 1930 will be a very different person from a French person born in 1980, for example. This isn’t about who is better or worse compared to the other, this is about understanding why those people created the things that they did. And part of this understanding comes from understanding the time and place of their birth. Not just knowing the time and place, mind you, but understanding it.

And to understand why Hirschman was the kind of economist he was, you need to understand where he came from, all of what he experienced in his formative years – and the cultural milieu that surrounded him when we was an economist.

And it is for this reason that reading this blogpost about Hirschman’s work, but also his biography is ever so illuminating. You don’t just get a sense of Hirschman’s central ideas, but you also get a sense of how events in his life formed his worldview, and possibly influenced some of his decisions later on.

He encourages us to see the inevitable pitfalls and stumbles of the growth process not as disappointments, but as opportunities, and gives us a conceptual language to identify them. For randomistas-in-training, steeped in the world of deworming and bed-nets and pre-analysis plans, Hirschman also reminds us that we need to step back from individual interventions more often and think more about development strategies–not just how different projects can complement each other, but also how each project might organically summon market and non-market forces to help growth along.
The Credibility Revolution has yielded, perhaps for the first time, robust evidence for individual program effects. The time is ripe, not to copy Hirschman’s ideas wholesale, but to borrow his clear-eyed approach and think carefully about how projects can be brought together, pressure point by pressure point, into programs for sustained development.

https://www.global-developments.org/p/the-real-development-was-the-friends

This is worth doing for everybody of note, of course, not just Albert O. Hirschman – but if you are a fan of studying the development of the field of development economics, this would be a great place to start.

Lots of homework in today’s post:

  1. Watch Transatlantic (it is available on Netflix)
  2. Read The Worldly Philosopher, by Jeremy Adelman
  3. Read The Strategy of Economic Development, by Hirschman

Immune-Computer Interfaces

… of which I know even less than I do of economics, but what a fascinating thread this is. H/T Navin Kabra

Will LLM’s Collude?

The rise of algorithmic pricing raises concerns of algorithmic collusion. We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs), and specifically GPT-4. We find that (1) LLM-based agents are adept at pricing tasks, (2) LLM-based pricing agents autonomously collude in oligopoly settings to the detriment of consumers, and (3) variation in seemingly innocuous phrases in LLM instructions (“prompts”) may increase collusion. These results extend to auction settings. Our findings underscore the need for antitrust regulation regarding algorithmic pricing, and uncover regulatory challenges unique to LLM-based pricing agents.

Fish, S., Gonczarowski, Y. A., & Shorrer, R. I. (2024). Algorithmic Collusion by Large Language Models. arXiv preprint arXiv:2404.00806.

And from the conclusion of the paper:

Indeed, it is quite plausible that in the near future, merchants who wish to price their goods without any intent to collude with competitors would turn to the technology that assists them throughout many day-to-day decisions: LLMs. Each of the merchants would describe the market to the LLM and tell it to focus on long-term revenue without so much as a hint to collude. The merchants would not know how LLMs work, and yet have no reason to believe an LLM might engage in any uncompetitive behavior on their behalf. Some of them
would even ask the LLM whether it might engage in collusive behavior and be reassured by the LLM that it would not do so (see Figure 11 for an example with GPT-4). There would be no red flags whatsoever. And then, each of them would put the LLM in charge, and as we have demonstrated, the LLMs might engage in seemingly collusive behavior to the detriment of consumers, despite each of the merchants having acted with in good faith and in a completely reasonable, even cautious, way.
What are best practices for using LLMs for pricing? Should certain terms or phrases be mandated or forbidden? And, how should firms monitor the “strategic intentions” of their pricing algorithms? As the use of LLMs becomes more commonplace, these questions and others will become pressing, and will make regulation and enforcement even more challenging

Fish, S., Gonczarowski, Y. A., & Shorrer, R. I. (2024). Algorithmic Collusion by Large Language Models. arXiv preprint arXiv:2404.00806.

Teaching using LLM’s is going to be fascinating, and students will get to learn economics in ways that we could only dream of until a couple of years ago. Actually, scratch that: we couldn’t even have dreamt of this two years ago!

Here’s Ethan Mollick in Co-Intelligence, a book that has come out only today (and you really should be reading it. Yes, all of you, and yes, all of it. It is excellent, and a review of it will be out here on Monday):

But the AI is not just acting like a consumer; it also arrives at similar moral conclusions, with similar biases, to the ones we have. For example, MIT professor John Horton had AI play the Dictator Game, a common economic experiment, and found he could get the AI to act in a way similar to a human. In the game, there are two players, one of whom is the “dictator.” The dictator is given a sum of money and must decide how much to give to the second player. In a human setting, the game explores human norms like fairness and altruism. In Horton’s AI version, AI was given specific instructions to prioritize equity, efficiency, or self-interest. When instructed to value equity, it chose to divide the money equally. When prioritizing efficiency, the AI opted for outcomes that maximized the total payoff. If self-interest was the order of the day, it allocated most of the funds to itself. Though it has no morality of its own, it can interpret our moral instructions. When no specific instruction was given, AI defaulted to efficient outcomes, a behavior that could be interpreted as a kind of built-in rationality or a reflection of its training.

Mollick, Ethan. Co-Intelligence: Living and Working with AI (pp. 68-69). Ebury Publishing. Kindle Edition.

AI won’t just cause you three sleepless nights for the reason Ethan Mollick talks about at the start of his excellent book. AI will also cause you sleepless nights because you will be wondering about the awesome ways in which you can become better at your job, especially if you are a teacher:

But AI has changed everything: teachers of billions of people around the world have access to a tool that can potentially act as the ultimate education technology. Once the exclusive privilege of million-dollar budgets and expert teams, education technology now rests in the hands of educators. The ability to unleash talent, and to make schooling better for everyone from students to teachers to parents, is incredibly exciting. We stand on the cusp of an era when AI changes how we educate—empowering teachers and students and reshaping the learning experience—and, hopefully, achieve that two sigma improvement for all. The only question is whether we steer this shift in a way that lives up to the ideals of expanding opportunity for everyone and nurturing human potential.

Mollick, Ethan. Co-Intelligence: Living and Working with AI (p. 177). Ebury Publishing. Kindle Edition.

Yes it really is happening, and at least where education is concerned, please, pretty please, bring it on!

Update: apologies, I forgot to mention that I landed on the paper via Ethan Mollick’s timeline on Twitter.

A Good Doctor Cures the Patient…

… but a great one makes sure that the patient doesn’t come back to the clinic.

What made Manmohan Singh’s Budget speech of 1991–92 historic was that he used it to present a masterly explanation of why the wide-ranging reforms were being attempted and how the various components fitted together. He explained that we needed to go beyond crisis management towards structural reforms, including liberalization of both industrial policy and trade policy as a means of unleashing the animal spirits of India’s private sector. The opening to imports and foreign investment was necessary to subject the economy to competitive pressure.

Montek Singh Ahluwalia. BACKSTAGE: The Story behind India’s High Growth Years (p. 144). Rupa. Kindle Edition.

There are lots of excerpts from lots of academic writings that I could have added here to help celebrate Dr. Manmohan Singh’s career. But I will, instead, add two further excerpts from the same book, because it exemplifies what Dr. Singh, and others of his ilk, stood for:

Later in the course of the debate, Atal Bihari Vajpayee, while being mildly critical of the Budget as was expected of an opposition leader, advised Manmohan Singh, in a very avuncular style, to develop a thick skin. He said he had grown old listening to insults and abuses! (pg. 151)

Dr Singh was not naturally inclined to projecting himself as a leader. His instinct was not to prevail by force of authority but always to appreciate the other person’s point of view and look for a consensual way to move forward. (pg. 347)

“Building consensus” is a most underrated thing in politics the world over today, across intra and international issues. Dr. Singh could play that game as well as anybody, as could Atal Bihari Vajpayee.

Have at it if you wish to establish whose fault it is that the art no longer exists in India’s political space, but I would urge you to figure out how to go about getting it back.

History already judges him kindly, but the future will miss him and his skills desperately.

Thank you, Dr. Singh, for everything.

Yamini Aiyar Asks a Question, and We Try to Answer

First, the question. We’ll get to who the “we” in the title is (or should it be “are” instead of “is”?) in a bit, and also to our answers.

Ideally in a democracy, there ought to be space for evidence-based partnerships with government whilst simultaneously holding the mirror. But when the space for holding the mirror shrinks, when freedoms are trampled upon, what should the public policy professional do? There is a real risk that the pressures of relevance can, and indeed do, push researchers to blunt critique, to inadvertently, perhaps, stop asking difficult questions and refrain from critical public engagement. Is there a need then to redefine our role, to question the narrow prism of relevance and impact that we judge ourselves by?

https://www.deccanherald.com/opinion/on-evidence-policy-making-and-critiquing-it-in-a-polarised-polity-2958920

Say Hello to ArreBhaiWah

Paul Krugman has a textbook on international economics. Standard stuff, and quite a good textbook, running into multiple editions. I may be wrong over here about the specific topic, but I think it is in the context of national accounting with international trade that Paul Krugman asks us to imagine a country called Agraria.

In much the same vein, but for entirely different reasons, I’m going to ask you to imagine a country called ArreBhaiWah.

Because when it comes to India, we will have to spend a significant amount of time having heated debates about whether the space for holding the mirror has shrunk or not. I might (and do!) say that yes, it has shrunk. You, on the other hand, might say that it has expanded instead. And then we will argue and call each other names and get applauded for having reminded the other side of their grandmothers. That is fun to do, but would not be constructive, nor productive.

So let us, instead, focus on ArreBhaiWah.

If you are a public policy professional in ArreBhaiWah, and you see that the space for holding the mirror has shrunk, and you need to analyze what you should do about it – what framework should you use to arrive at your answer?

This isn’t about answering the question for ArreBhaiWah, you see. As with many posts on EFE, it is about supplying you with a framework to think about the problem. Please decide for yourselves whether the question makes sense, is applicable and finally, what your answer (if any) should be.

Which brings us to who the “we” in the title of this post are (or should it be “is” instead of “are”?). It is yours truly, and the late, great A.O. Hirschman. By the way, both Yamini’s piece and this Wikipedia article about Hirschman deserve to be read in their entirety, so please do.

Exit, Loyalty and Voice

Here’s ChatGPT’s summary of one of my favorite books in economics:

“Exit, Voice, and Loyalty: Responses to Decline in Firms, Organizations, and States” is a seminal book by Albert O. Hirschman, published in 1970. The work presents a framework for understanding how people respond to dissatisfaction with organizations or states they are part of. Hirschman’s theory is built around three main concepts: exit, voice, and loyalty, which are mechanisms through which individuals can express their discontent and potentially influence change.

Exit: The option of leaving the organization or ceasing to use its products or services. This is a common response in economic markets; for example, if a customer is dissatisfied with a product, they can simply stop buying it and switch to a competitor. Exit is a powerful mechanism in promoting quality and efficiency due to the competitive pressure it creates.

Voice: The option of actively expressing dissatisfaction and seeking to improve conditions from within, rather than leaving. Voice can take many forms, including direct feedback, protests, or any attempt to change the organization’s practices or policies. Voice is particularly relevant in scenarios where exit is not feasible or desirable, such as in monopolies or with state governance.

Loyalty: Loyalty plays a moderating role in the exit and voice framework. It refers to a person’s attachment to an organization, leading them to endure dissatisfaction while trying to improve the organization through voice, rather than exiting. Loyalty can delay exit and give voice a chance to work, as loyal members or customers may seek to solve problems internally rather than abandoning the organization.

Hirschman’s framework is used to analyze a wide range of economic, political, and social phenomena. It provides insights into how organizations and states can deteriorate or improve over time based on the feedback mechanisms available to and utilized by their members or constituents. It also highlights the importance of maintaining the right balance between allowing exit and encouraging voice to ensure the health and adaptability of organizations and societies.

ChatGPT4

And this is an excellent framework with which to answer Yamini’s question: in ArreBhaiWah, what should the public policy professional do?

Should they choose exit, or voice? To what extent should loyalty influence your decision? Remember, loyalty refers to a person’s attachment to the organization (or the entity) in question.

This is where I step in.

What Are you Optimizing For?

  1. Should you be optimizing for what is best for you?
    • Best for you in a professional context, or personal context?
    • Best for your conscience, or best for your career?
  2. Or should you be optimizing for what is best for ArreBhaiWah?
  3. Or should you be optimizing for what is best for the folks who currently run the government in ArreBhaiWah?

Because as Khyati Pathak, Pranay Kotasthane and Anupam Manur point out in their excellent book We, the Citizens:

Source: We, The Citizens, pg 19

So is your loyalty to the government, or to the nation? Or are you of the considered opinion that the two are the same thing? They are manifestly not, by the way, so even if it is your considered opinion that they are the same thing, please do read Chapter 2 from the book, We, The Citizens (and the rest of the book, while you are at it!)

As with everything else in life, there are only trade-offs, and no solutions. There isn’t an easy way to answer this question that Yamini asks, alas. Optimizing for any one of oneself, ArreBhaiWah or its government also implies not optimizing for everything else.

But there you have it: the framework that one should use while thinking about the answer to Yamini’s question in the context of ArreBhaiWah.

What should her answer be, you ask? Why, that is Yamini’s business and no one else’s, surely. Allow me to wish her luck for what lies ahead, and to thank her for her work thus far. You and I may have disagreed with her about some of her conclusions, but that, I would argue, was part of the point.

Voice is currently underrated!

Scalars, Vectors, Incentive Design and McKinsey

… not to mention the horror that is poorly done econometrics. On my bad days – and this may well be one of them – I often end up wondering if the phrase “poorly done” is a redundant one in the context of econometrics. But I’m getting ahead of myself.

For the first course, a tweet:

I came across this tweet via The Zvi’s substack, and based on all the posts that I have read so far, I’m happy to recommend that you subscribe.

For salad, two definitions:

You probably know (or in the case of at least some of you, have blessedly forgotten) what scalars and vectors are – but in either case, here is a quick refresher, courtesy ChatGPT:

“Much like a thermometer measures one thing, and one thing only (the temperature), performance in a hedge fund is measured by one dimension: profit. It doesn’t inherently come with a “good” or “bad” direction; it just shows how much or how little.

This could be contrasted with a vector, which might represent the varied ways an organization or individual measures success. For example, a nonprofit might measure success not just by funds raised (the magnitude) but also by social impact (the direction). Here, performance isn’t just about a number; it’s about a number going in a specific direction towards a specific goal.”

(Note that I have tweaked the original answer a little bit to make it a bit more readable)

For the main course:

…the start of a recently published academic paper (h/t @realChrisBrunet over on Twitter), and two charts (hey, this is the main course! I hope you packed an appetite):

And for dessert:

First, an explanation of reverse causality (courtesy ChatGPT):

“Imagine you notice that when ice cream sales go up, the weather tends to be hotter. If you conclude that “Buying more ice cream causes the weather to get hotter,” you’re assuming that the ice cream sales are causing the hot weather. But actually, it’s the other way around – the hot weather is causing more people to buy ice cream. This mix-up is what we call “reverse causality.” It’s when we mistake the effect (hot weather) for the cause (increased ice cream sales), rather than recognizing the true cause (hot weather) for the effect (increased ice cream sales).

So reverse causality is all about getting the direction of cause and effect backward. Instead of A causing B, it’s actually B that is causing A, just like if someone thought that wearing shorts made the day sunny, when in reality, it’s the sunny day that prompts people to wear shorts.”

Does growth lead to more diversity based hiring, or does more diversity based hiring lead to growth?

And second, Cowen’s First Law:

“Cowen’s First Law: There is something wrong with everything (by which I mean there are few decisive or knockdown articles or arguments, and furthermore until you have found the major flaws in an argument, you do not understand it)”

And surely an espresso is called for after such a big meal:

How does Cowen’s First Law apply to this blogpost?

What Did I Write, and What Did I Learn in March 2024?

March was a good month for yours truly, because despite a fair bit of travel, I managed to post every single day (yay for me!).

  1. My number one learning for the month of March,2024 was that if you are a cricket fan, you absolutely should go and see a test match at Dharmshala if you get the opportunity. It’s not like I have been to all the stadiums in the world (hell, I haven’t been to all the test match venues in India, not by a long shot) – but I find it difficult to imagine that there is a more picturesque stadium than this one. It’s a long ol’ trip, but trust me, it is worth it.
  2. Prompt engineering is a skill worth learning, regardless of your age and profession. I cannot wait for Ethan Mollick’s book to land, and you should pre-order it. Check out the website in the meantime.
  3. Green, red and Zomato. This is my take.
  4. My reflections on Paul Graham’s essay on, well, the best essay (with a bit of Pirsig for company)
  5. My reflections on a book, a chart, a blogpost and a book on Foxconn and its factory in Tamil Nadu. This is a topic I hope to think and write about (and read about!) much more in 2024.
  6. I conducted a train the teacher program in Goa, and had a lot of fun. I also learnt about the latent demand for teaching how to use AI in the classroom.
  7. What links Satyakam (the movie) and RCT’s in Kenya? This blogpost does.
  8. If you’ve ever been to Dharamshala and McLeodganj, Devon’s Twitter thread will resonate with you.
  9. I hope to become a better photographer, and I found this video very helpful. It is about being a better photographer if you’re using your smartphone. Perhaps you will too.
  10. Weird pick, and not so much because of when the post was written (in February), but because of the date it is about (1st of April) – Sidin on why our financial year begins on the 1st of April.

I’ll be back after (hopefully) 30 posts in April 2024.