When AGI arrives and replaces all human work, there won't be human sports.

Instead of watching humans play basketball, we'll watch humanoid robots play basketball; robots will, after all, play better.

Similarly, robot jockeys will ride robot horses at the racetrack.

1/ There won't be humans getting paid to compete in chess tournaments.

MagnusGPT will not only play better than any human plays today, but also make that characteristic smirk and swivel his head around in that weird way.

2/

Played around with OpenAI Deep Research today. Thoughts:

1. Worst: asked it to find the fourth woman ever elected to Harvard's Society of Fellows - simple reasoning was required to assess ambiguous names. Gave wrong person. High school intern would do better.

1/ 2. Asked it to list all economists at top 15 econ departments in a specific subfield w/ their citation counts. It barely figured out the US News ranking, its list of people was incomplete, and it ran into problems accessing Google Scholar so cites were wrong/approximate.

2/

Modern supply chains don't look like trade theory 101!

They involve constant border crossings, each now hit by tariffs.

Tariffs raise prices, but the more important thing they do is disrupt supply relationships.

1/

graphic h/t @scottlincicome . When this choreography unfolds, each firm in the network has contracts with specific partners.

Why does this matter?

Some of those partners are not profitable enough to survive the tariff shock (though of course we shouldn't take a statement like this as impartial).

2/

Most people try to have insurance to protect their biggest assets (e.g., house).

But markets provide very little explicit help if you want to protect yourself against economic change erasing the value of, say, your college degree.

This doesn't have to be so.

1/ Human capital is at least as big an investment for many as their house.

There are very strong economic reasons to want to protect it.

But we spend very little time talking about insurance for things like that.

2/

ALZ got sorta cancelled for proposing using LLMs to parse hard texts

I think part of the aggression from the humanities is feeling threatened: a lot of the value and fun of good undergrad lectures with hard readings is having a

1/

https://twitter.com/alz_zyd_/status/1874863049037033784

smart professor translate the imposing and borderline unintelligible reading into contemporary, easy to follow English.

(Come to think of it, that's also a lot of the value of a good lecture in math, or a good talk on a technical economics paper.)

2/

Free disposal thought for theoretically inclined young economists:

Thinking through what AI means for the economy has a lot of upside.

Our toolbox is very powerful at structuring imaginative thinking about the big changes coming. Few others have these tools.

1/ Imagine a 2055 retrospective on economic thought over the past 30 years.

Seems very likely that a field called (roughly )"the economics of artificial intelligence" is the main character.

2/

This lovely paper by @SNageebAli, Mihm, and Siga — just revised for Econometrica — proposed a really striking theory of when voters rationally, but wrongly, think that policies are bad for them if they're good for others.



1/2 sites.psu.edu/nageebali/file…
The paper has had a long childhood — I remember first seeing it in 2017 and finding the core adverse selection mechanism remarkable and compelling.

(Check out this study in abstract length, @ShengwuLi ! )

Hope it gets the attention it deserves in political economy.

2/2

In the WSJ, Steven Landsburg proudly used this brain teaser to make an argument about where economics teaching is going wrong.

Instead he illustrates what’s wrong with his way of doing price theory: sloppy economic thinking, way more impressed with itself than it deserves to be. It's worth thinking through the "answer" he expects, which you can guess based only on knowing his personality (never great):

marginal cost of the fruit is lower for the monopolist. He writes, "In a competitive industry, prices are a pretty good indicator of resource costs."

Small observation on research trajectory in economic theory.

When you first try to do it, most projects you propose are
(i) ridiculous/uninteresting to experts or
(ii) trivial
or, often, both!

This phase is very difficult and it's a big thrill to escape it.

1/ To escape, you have to sense

(i) what directions other scholars consider substantively interesting/promising;
(ii) what experts consider non-obvious.

Judging (i) and (ii) and doing well by these standards is pragmatically what makes young theorists successful

The notion that amazing papers should not get rejected is an odd one.

Any genuinely important idea is more likely to be strongly disliked. (Some reasons below in a short thread.)

To publish important work, editors have to be bold and overrule some negative experts. Non-exhaustive list of reasons

1. The first technical work in a new paradigm is often crude and simple relative to the sophisticated and elaborate papers written late in a paradigm, when methods are being polished by a large community of experts in those methods.

I generally recommend

1. Constructing an n-by-m matrix whose rows are people and columns are issues (or dimensions of issues).

2. Finding the largest few and smallest few singular values.

3. Looking at the corresponding singular vectors in issue space.

(cont.)

1/

https://twitter.com/besttrousers/status/1727727603803369878

The top few singular vectors in issue space will tell you about "bundles" of issues along which there are considerable distances in the group.

(If these have high singular values, that corresponds to those differences explaining a lot of the group's variation in opinions.)

Talking to GPT4 about the Sylvester-Gallai Theorem and formalizing it

1/ 2/

Some new progress in math makes me hopeful about finally making progress on a big open question about opinion dynamics in social networks.

The question is: in simple models where people update opinions by averaging in friends' opinions, how long can polarization persist?

1/ In a 2012 QJE paper, Matt Jackson and I

(i) studied "time to consensus" in such learning by adapting the standard EIGENVALUE analysis of convergence times for reversible Markov matrices

(ii) showed how to approximate the answer knowing only "GROUP-level" linking data.

2/

For those who (like me) were interested in the "GPT can ace MIT" paper,

Here's a great short writeup by three MIT EECS seniors explaining the many things wrong with analysis.

dub.sh/gptsucksatmit A few quick notes.

Chowdhuri, Deshmukh, and Koplow (from now on CDK) point out some things about the methods that would probably be surprising to those who excitedly retweeted the flashiest claims.

First, GPT-4 was often fed the same problems that it was asked to solve.

What our journals would be like if excellent novelists edited our papers

https://twitter.com/ConBerd/status/1668976398571294721

Among active economists, who writes like this? Ed Glaeser a little bit. Who else?

Do individuals' ideas matter, or is the evolution of social opinion all determined by larger social forces?

Some recent threads by @DAcemogluMIT inspired me to write down one thing we know about this from research in network theory.

A short 🧵

1/ @DAcemogluMIT The question at the start is exactly what @JacksonmMatt and I answered in this paper, though that's not how it's stated in the abstract.

In the model, people get initial opinions from a distribution and talk in a network.

bengolub.net/wpcontent/uplo…

🧵 about a use of ChatGPT with two plugins -- Wolfram and AskYourPDF -- to write a solution to a fairly challenging advanced undergraduate economics/game theory problem.

This is my first try with these tools - I thought it would be interesting to share. The topic of the problem is consumer markets with externalities, as taught in this beautiful chapter of an Easley and Kleinberg textbook.

The first challenge is how to get all this information into ChatGPT's head?

cs.cornell.edu/home/kleinber/…

Had an interesting conversation with an economist friend touching on AI alignment, Derek Parfit, and implementation theory. Brief notes:

A basic fear about AGI concerns the unintended consequences of following instructions.

1/ The core problem is often framed as: "EVEN IF the AI wants to behave well, it's hard to convey all our values to it."

It might violate some of our very important preferences in trying to follow instructions.

E.g. it might kill its master in trying to make paperclips.

2/

The Law of Iterated Expectations is secretly about eigenvectors.

Let q(w) be your prior of state w;
Let P(w,w') be P[w' | Y(w)]: your probability of state w' when w is the state and Y is your info.

Then the LIE is <=>

q P = q

I.e., q is an eigenvector of P w/ eigenvalue 1

https://twitter.com/ProbFact/status/1643346507272429569

This banger due to Dov Samet.

A way GPT-4 changed my teaching:

In an advanced undergrad elective, I used to grade based on problem sets only. This required obfuscating famous exercises to make cheating via Google harder.

AI can often undo obfuscation, so I don't bother and give an exam.

1/ This is *not* because it's hard to obfuscate so that GPT-4 gives bad help: e.g., ask it to prove that in a loopless undirected graph on at least 3 vertices, at least 3 must have the same degree.

But it's made obfuscation costly/uncertain enough that it's not worth it for me.

2/

Reliably amazes me:

Leontief's economics -- about how shocks propagate through the network of firms and industries trading -- is completely outside Ph.D. economists' canon.

This is both a symptom and a cause of economics losing good physics intuition.

Old-person thread

1/ The proximate cause of what happened is pretty clear:

Leontief's analysis made very strong assumptions about production (in fact his production function is the first/main reason most of us see his name).

Micro and macro came to focus on more general & "harder" methods.

2/