I thought I'd try to reconstruct Bryan's discussion with a 13-year-old about the minimum wage based on the below tweet: (@bryan_caplan let me know if anything is inaccurate)

And further validation in many field settings: theory says that prices should not differ by more than transport costs, provided price info is available. Here is what happened to fish prices when mobile phones were introduced in Kerala:

Many people cannot accept that they know too little to have an opinion about issues they care about, so the only way to rationalize disagreement with economists is to assume that economists are morally or philosophically naive.

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Letting more people spend more time on their comparative advantage is one of the key ways that AI could accelerate scientific progress, even when AI can't solve problems outright.

LLMs probably could not reliably estimate P(success), but an auxiliary regression model trained on historical outcomes and using inputs generated by the LLM could do so.

For example, let's say you have a tough differential equation problem. The LLM scans the work of everyone in its database to try to predict who can solve the problem. You are then given a list of people with their cost/hr, predicted number of hours to solve and P(success).

I’ll be the first to say it. I think that AI will be the Beast (antichrist) from Revelation in the Bible. It will be the last god that humans create before it rules us and controls us without us even realizing it. The world in 10 years is going to be very different.

Suppose, you feed an LLM a problem and, if it can't solve it, it matches you with all the people capable of solving it (based on their past work), and you can then pay them or offer them co-authorship.

Many researchers spend huge amounts of time working on problems that others could solve easily and inexpensively. This problem is fixable with the right matchmaking tool.

If you think the positive conclusions of economists are elaborate justifications stemming from their ideological commitments, your view is empirically false. Ideology explains only a small share of variation in positive estimates: https://t.co/lxgKj7trdl

Even if you think economists are in thrall to "neoliberal ideology", you cannot use this to dismiss the positive estimates they produce, which is the bulk of the work published in economics journals.

The pharmaceutical fallacy lies with the idea that everyone claims price controls will eliminate profitability and decrease the profit motive that fuels the growth of medicines. This is only partially true because a profit motive is required, but how much? This contributes to the

They are correct that one cannot draw policy conclusions without normative assumptions. However, this claim is often conflated with the false claim that causal conclusions require normative assumptions.

A virtue of modern economics is the time spent being careful about models and data before normative reasoning is conducted. Many left-wing critics of economics reason too much from first principles, spending too little time on the positive questions economists obsess over.

This is an "inside econtwitter" tweet if you don't know what I'm talking about.

To unify general relativity and quantum mechanics, remember that the physical laws we observe are the equilibrium of a low-dimensional game on the boundary of the universe in which particles randomize their location to avoid detection by other particles.

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Seems a tad suspicious that the guy who calls you from Stockholm to interview you about winning a Nobel Prize in Economics is named "Adam Smith"

The mean is not a sufficient statistic for the distribution. Scientific innovation comes from the far right tail. If you don’t admit the best foreign students, you get vastly fewer students in the far right tail.

A lot of fashion choices have arms race dynamics. We should have higher taxes on women’s pants that lack adequate pockets, uncomfortable shoes, or men’s shirts that button tightly around the neck.

The return to papers saying, "Paper X, by famous people, is badly done and wrong" is, regretably, low. The return to papers saying, "Paper X says Y. It has shortcomings. My analysis that doesn't have these shortcomings says not Y" remains high (at least in econ).

Global liquidity is shifting again. AI is pulling capital into new corners of the world. And policy credibility is making a comeback in emerging markets. Here’s what the @StoneX_Official Strategy team has been seeing from São Paulo to Buenos Aires to New York.

In my experience, this is the single biggest failure mode for PhD students. They only care about the approval of more senior people, so they imitate or go through the expected motions rather than using the tools they’ve learned to try to solve a real problem.

Formal math and statistical testing force you to admit frequently that you are mistaken. Trying to think about social issues without this safety net is extremely perilous.

It is correct in principle that people can wrongly dismiss concepts for being too informal (e.g. scientists who dismissed Wegener's theory of continental drift), but in practice, most of the time, the establishment scientists who did this were completely correct.