Meme Generator for Econ Papers

I’m exploring whether the meme generator by Glif could be a way to introduce an econ paper. What if you identify a main character in your research project for GLIF to drag? (BTW, I have learned that the Wojack Meme Generator will re-write the name of the person you put in if your phrase is too long but that does not mean that the phrase is not used for content. So, you can put a longer phrase into the meme generator.)

I’m going to re-print here the prompt I actually used to get the Glif meme. As a warning, this approach is obviously not appropriate for more professional audiences. But sometimes you have a chance to quickly show your paper to a more informal audience either in a presentation or online. Having a way to wake up the audience in that situation could be helpful.

I’m not sharing all of these because I like them. I’m trying to give readers a chance to decide if they’d want to try it themselves. I think some of these prompts don’t work well and the cartoons either aren’t funny or are not true to life. However, I do find them interesting if the assignment is to scrape the internet for the maximally negative sentiment about a certain thing.

The prompt I used: “Pay Transparency Advocate” / “Effort Transparency and Fairness,” with Elif Demiral and Umit Saglam (under review)

Prompt: “Person Who Trusts ChatGPT” / “Do People Trust Humans More Than ChatGPT?” (2024) with William Hickman. Journal of Behavioral and Experimental Economics, 112: 102239. 

Prompt: “Undergraduate Computer Science Major” / “Willingness to be Paid: Who Trains for Tech Jobs?” (2022) Labour Economics, Vol 79, 102267. 

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GLIF Social Media Memes

Wojak Meme Generator from Glif will build you a funny meme from a short phrase or single word prompt. Note that it is built to be derogatory, cruel for sport, and may hallucinate up falsehoods. (see tweet announcement)

I am fascinated by this from the angle of modern anthropology. The AI has learned all of this by studying what we write online. Someone can build an AI to make jokes and call out hypocrisy.

Here are GLIFs of the different social media user stereotypes as of 2024. Most of our current readers probably don’t need any captions to these memes, but I’ll provide a bit of sincere explanation to help everyone understand the jokes.

Twitter user: Person who posts short messages and follows others on the microblogging platform.

Facebook user: Individual with a profile on the social network for connecting with friends and sharing content.

Bluesky user: Early adopter of a decentralized social media platform focused on user control.

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Is the Universe Legible to Intelligence?

I borrowed the following from the posted transcript. Bold emphasis added by me. This starts at about minute 36 of the podcast “Tyler Cowen – Hayek, Keynes, & Smith on AI, Animal Spirits, Anarchy, & Growth” with Dwarkesh Patel from January 2024.

Patel: We are talking about GPT-5 level models. What do you think will happen with GPT-6, GPT-7? Do you still think of it like having a bunch of RAs (research assistants) or does it seem like a different thing at some point?

Cowen: I’m not sure what those numbers going up mean or what a GPT-7 would look like or how much smarter it could get. I think people make too many assumptions there. It could be the real advantages are integrating it into workflows by things that are not better GPTs at all. And once you get to GPT, say 5.5, I’m not sure you can just turn up the dial on smarts and have it, for example, integrate general relativity and quantum mechanics.

Patel: Why not?

Cowen: I don’t think that’s how intelligence works. And this is a Hayekian point. And some of these problems, there just may be no answer. Like maybe the universe isn’t that legible. And if it’s not that legible, the GPT-11 doesn’t really make sense as a creature or whatever.

Patel (37:43) : Isn’t there a Hayekian argument to be made that, listen, you can have billions of copies of these things. Imagine the sort of decentralized order that could result, the amount of decentralized tacit knowledge that billions of copies talking to each other could have. That in and of itself is an argument to be made about the whole thing as an emergent order will be much more powerful than we’re anticipating.

Cowen: Well, I think it will be highly productive. What tacit knowledge means with AIs, I don’t think we understand yet. Is it by definition all non-tacit or does the fact that how GPT-4 works is not legible to us or even its creators so much? Does that mean it’s possessing of tacit knowledge or is it not knowledge? None of those categories are well thought out …

It might be significant that LLMs are no longer legible to their human creators. More significantly, the universe might not be legible to intelligence, at least of the kind that is trained on human writing. I (Joy) gathered a few more notes for myself.

A co-EV-winner has commented on this at Don’t Worry About the Vase

(37:00) Tyler expresses skepticism that GPT-N can scale up its intelligence that far, that beyond 5.5 maybe integration with other systems matters more, and says ‘maybe the universe is not that legible.’ I essentially read this as Tyler engaging in superintelligence denialism, consistent with his idea that humans with very high intelligence are themselves overrated, and saying that there is no meaningful sense in which intelligence can much exceed generally smart human level other than perhaps literal clock speed.

I (Joy) took it more literally. I don’t see “superintelligence denialism.” I took it to mean that the universe is not legible to our brand of intelligence.

There is one other comment I found in response to a short clip posted by @DwarkeshPatel  by youtuber @trucid2

Intelligence isn’t sufficient to solve this problem, but isn’t for the reason he stated. We know that GR and QM are inconsistent–it’s in the math. But the universe has no trouble deciding how to behave. It is consistent. That means a consistent theory that combines both is possible. The reason intelligence alone isn’t enough is that we’re missing data. There may be an infinite number of ways to combine QM and GR. Which is the correct one? You need data for that.

I saved myself a little time by writing the following with ChatGPT. If the GPT got something wrong in here, I’m not qualified to notice:

Newtonian physics gave an impression of a predictable, clockwork universe, leading many to believe that deeper exploration with more powerful microscopes would reveal even greater predictability. Contrary to this expectation, the advent of quantum mechanics revealed a bizarre, unpredictable micro-world. The more we learned, the stranger and less intuitive the universe became. This shift highlighted the limits of classical physics and the necessity of new theories to explain the fundamental nature of reality.
General Relativity (GR) and Quantum Mechanics (QM) are inconsistent because they describe the universe in fundamentally different ways and are based on different underlying principles. GR, formulated by Einstein, describes gravity as the curvature of spacetime caused by mass and energy, providing a deterministic framework for understanding large-scale phenomena like the motion of planets and the structure of galaxies. In contrast, QM governs the behavior of particles at the smallest scales, where probabilities and wave-particle duality dominate, and uncertainty is intrinsic.

The inconsistencies arise because:

  1. Mathematical Frameworks: GR is a classical field theory expressed through smooth, continuous spacetime, while QM relies on discrete probabilities and quantized fields. Integrating the continuous nature of GR with the discrete, probabilistic framework of QM has proven mathematically challenging.
  2. Singularities and Infinities: When applied to extreme conditions like black holes or the Big Bang, GR predicts singularities where physical quantities become infinite, which QM cannot handle. Conversely, when trying to apply quantum principles to gravity, the calculations often lead to non-renormalizable infinities, meaning they cannot be easily tamed or made sense of.
  3. Scales and Forces: GR works exceptionally well on macroscopic scales and with strong gravitational fields, while QM accurately describes subatomic scales and the other three fundamental forces (electromagnetic, weak nuclear, and strong nuclear). Merging these scales and forces into a coherent theory that works universally remains an unresolved problem.

Ultimately, the inconsistency suggests that a more fundamental theory, potentially a theory of quantum gravity like string theory or loop quantum gravity, is needed to reconcile the two frameworks.

P.S. I published “AI Doesn’t Mimic God’s Intelligence” at The Gospel Coalition. For now, at least, there is some higher plane of knowledge that we humans are not on. Will AI get there? Take us there? We don’t know.

Do I Trust Claude 3.5 Sonnet?

For the first time this week, I paid for a subscription to an LLM. I know economists who have been on the paid tier of OpenAI’s ChatGPT since 2023, using it for both research and teaching tasks.

I did publish a paper on the mistakes it makes: ChatGPT Hallucinates Nonexistent Citations: Evidence from Economics In a behavioral paper, I used it as a stand-in for AI: Do People Trust Humans More Than ChatGPT?

I have nothing against ChatGPT. For various reasons, I never paid for it, even though I used it occasionally for routine work or for writing drafts. Perhaps if I were on the paid tier of something else already, I would have resisted paying for Claude.  

Yesterday, I made an account with Claude to try it out for free. Claude and I started working together on a paper I’m revising. Claude was doing excellent work and then I ran out of free credits. I want to finish the revision this week, so I decided to start paying $20/month.

Here’s a little snapshot of our conversation. Claude is writing R code which I run in RStudio to update graphs in my paper.

This coding work is something I used to do myself (with internet searches for help). Have I been 10x-ed? Maybe I’ve been 2x-ed.

I’ll refer to Zuckerberg via Dwarkesh (which I’ve blogged about before):

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Real and Nominal Rigidities Research

This week, I’m doing some review for a macro-related project. In economics, the concepts of real and nominal rigidities help explain why prices and wages do not always adjust quickly in response to shocks. These rigidities create frictions that affect how markets function. A well-known rigidity is downward nominal wage rigidity (I have an experimental paper on that).

“Nominal rigidities” refer to the stickiness of prices and wages in their nominal (monetary) terms. These rigidities prevent immediate adjustment of prices and wages to changes in the overall economic environment.

Examples of Nominal Rigidities

  • Menu Costs: The costs associated with changing prices, such as reprinting menus or reprogramming point-of-sale systems. For instance, a restaurant might avoid changing its menu prices frequently because of the costs involved in printing new menus and the risk of confusing or losing customers.
  • Nominal Wage Contracts: Many workers are employed under contracts that fix their wages for a certain period, such as a year. This means that even if the demand for labor changes, wages cannot adjust immediately. For example, a factory might have a one-year wage contract with its workers, preventing it from lowering wages even during a downturn.
  • Price Stickiness Due to Psychological Factors: Prices may remain rigid because businesses fear that frequent changes might upset customers or erode their trust. A classic example is a retail store keeping prices stable to maintain a reputation for reliability, even when costs fluctuate.

Side note: Lars Christensen predicts less nominal rigidity in our future. Menu costs are getting smaller and customers could become accustomed to, for example, watching the price of milk fluctuate in real time in response to statements by the Fed. Click here for related Twitter joke.

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Trusting ChatGPT at JBEE

You can find my paper with Will Hickman “Do people trust humans more than ChatGPT?” at the Journal of Behavioral and Experimental Economics (JBEE) online, and you can download it free before July 30, 2024 (temporarily ungated*).

*Find a previous ungated draft at SSRN.

Did we find that people trust humans more than the bots? It’s complicated. Or, as we say in the paper, it’s context-dependent.

When participants saw labels informing them (e.g. “The following paragraph was written by a human.”) about authorship, readers were more likely to purchase a fact-check (the orange bar).

Informed subjects were not more trusting of human authors versus ChatGPT (so we couldn’t reject the null hypothesis about trusting humans, in that sense). However, Informed subjects were significantly less likely to trust their own judgement of the factual accuracy of the paragraph in the experiment, relative to readers who saw no authorship labels.

Some regulations would make the internet more like our Informed treatment. The EU may mandate that ChatGPT comply with the obligation of: “Disclosing that the content was generated by AI.” Our results indicate that this policy would affect behavior because people read differently when they are forced to think up front about how the text was generated.

Inspiration for this article on trust began with observing the serious errors that can be produced by LLMS (e.g. make up fake citations). Our hypothesis was that readers are more trusting of human authors, because of these known mistakes by ChatGPT. This graph shows that participants trust (left blue bar = “High Trust”) statements *believed* to have been written by a human (so, in that sense, our main hypothesis has some confirmation).

Conversely, in the Informed treatment, readers are equally uncertain about text written either by humans or bots. Informed readers are suspicious, so they buy a fact-check. “High Trust” (the blue bar) is the option that maximizes expected value if the reader thinks the author has not made factual errors.

So, in conclusion, we find that human readers can be made more suspicious by framing. In this case, we are thinking of being cautious and doing a fact-check as a good thing. The reason is that, increasingly, the new texts of society are being written by LLMs. Evidence of this fact has been presented by Andrew Gray in a 2023 working paper: “ChatGPT “contamination”: estimating the prevalence of LLMs in the scholarly literature” Note that is the scholarly literature, not just the sports blogs or the Harry Potter – Taylor Swift- crossover fanfics.

What about the medical doctors? What is the authority on whether you are getting surgery or not? See: “Delving into PubMed Records: Some Terms in Medical Writing Have Drastically Changed after the Arrival of ChatGPT”

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Latest from Leopold on AGI

When I give talks about AI, I often present my own research on ChatGPT muffing academic references. By the end I make sure that I present some evidence of how good ChatGPT can be, to make sure the audience walks away with the correct overall impression of where technology is heading. On the topic of rapid advances in LLMs, interesting new claims from a person on the inside can by found from Leopold Aschenbrenner in his new article (book?) called “Situational Awareness.”
https://situational-awareness.ai/
PDF: https://situational-awareness.ai/wp-content/uploads/2024/06/situationalawareness.pdf

He argues that AGI is near and LLMs will surpass the smartest humans soon.

AI progress won’t stop at human-level. Hundreds of millions of AGIs could automate AI research, compressing a decade of algorithmic progress (5+ OOMs) into ≤1 year. We would rapidly go from human-level to vastly superhuman AI systems. The power—and the peril—of superintelligence would be dramatic.

Based on this assumption that AIs will surpass humans soon, he draws conclusions for national security and how we should conduct AI research. (No, I have not read all if it.)

I dropped in that question and I’m not sure if anyone has, per se, an answer.

You can also get the talking version of Leopold’s paper in his podcast with Dwarkesh.

I’m also not sure if anyone is going to answer this one:

I might offer to contract out my services in the future based on my human instincts shaped by growing up on internet culture (i.e. I know when they are joking) and having an acute sense of irony. How is Artificial General Irony coming along?

Gear Swaps are Happening

Everyone feels like we throw away too much stuff. One small way to help is to try to find someone who can use the items before you toss them.

I’m happy to say that one of my economic ideas got to the policy implementation stage. I was staring at the Scout gear my son had grown out of and dreading the thought of throwing it away. I could donate it to Good Will, but I thought that the chances it would get to someone who wants it are very low. What parent wants exactly that stuff? So, I emailed our Pack leader and asked if we could start doing a gear swap.

Parents can bring any scout-related items that they do not want anymore to a pack meeting. It is organized on one table with clear information. Anyone can take anything for free if they can use it and store it.  

This works better than posting to internet Buy Nothing groups because the scout parents are right there. No one has to drive across town for a “porch pick up.”

More sports teams or clubs should do this. Seize the moments when like-minded people are already together in one place.

Previously from me on Fast Fashion:

Secondhand for AdamSmithWorks

Is the repair revolution coming?

Joy’s Fashion Globalization Article with Cato

Do Less for Preschool

Today I will write about something I care deeply about: the wellbeing of the moms of young children.

I can remember having a child enrolled in preschool. It was expensive but it was worth it, for us. What follows will be most relevant to readers who are working full-time and have children enrolled in full-time daycare/preschool. That is not the right choice for every family. If it’s the choice you made, then read on.

Do less for preschool. Save your energy and money for the years when your child will actually remember.

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Humanity’s Childhood and Chiefs

I’m going to explore a passage from The Dawn of Everything about whether humans reject Western civilization.

The introductory chapter of The Dawn of Everything is called “Farewell to Humanity’s Childhood.” The authors are idealists wrestling with big questions.

We can take [Steven] Pinker as our quintessential Hobbesian. (page 13)

For instance, if Pinker is correct, then any sane person who had to choose between (a) the violent chaos and abject poverty of the ‘tribal’ stage in human development and (b) the relative security and prosperity of Western civilization would not hesitate to leap for safety. (page 18)

Over the last several centuries, there have been numerous occasions when individuals found themselves in a position to make precisely this choice – and they almost never go the way Pinker would have predicted.

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