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• My excellent Conversation with Terence Tao
• Experimentally validated digital twins
• Checking the AI audit of Saez and Zucman’s research.

 

2. A new tool for AI-proof homework assignments.  “It’s called https://deb8.tv — students get paired and have live video debates with one another.”

3. “The average French person believes there are 6.4 workers for every retiree. The real number is about 1.8.”

4. Sulfuric acid as a basic medium for life? (NYT)

5. The fascination of underwater archaeology (Atlantic).

6. NYT obituary for Richard Posner.  And Cass Sunstein on Richard Posner.

The post Thursday assorted links appeared first on Marginal REVOLUTION.

       

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• In reply to Dismalist. Dismalist, I'm trying to figure out ... by Kaz Stanczak
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First, they point out that despite the attention given to (some days) trillionaires and billionaires, the combined wealth of the Everywhere Millionaires, the dentists, the car dealers, the mid-size business owners, is far larger.

…it’s important to put the scale of Forbes billionaire growth into perspective with the other rich and powerful actors in American society. Even at the very frothy public market valuations of the current moment, the Forbes list amounts to around $8 trillion or roughly 4.3% of total household wealth. Meanwhile, based on the most recent SCF, Everywhere Millionaires are probably worth more than $63 trillion or 38% of total household wealth.

Second, the power of the billionaires is often exaggerated relative to the power of the millionaires who have money and votes:

…Elon didn’t want the massive tax cuts in the OBBBA extended, which did not go to the tech billionaires. He didn’t benefit from the reduced EV demand, which came when the EV credits were abolished. He benefitted little from the new deduction for car loan interest passed in that bill, since financing car purchases is a small part of Tesla’s business.

Who drove these changes and reaped the rewards? The Everywhere Millionaire car dealers who reside in nearly every Congressional district and represent many of them directly. Elon lost big in the midterm election, despite spending half a billion dollars. Meanwhile, dozens of Everywhere Millionaires will return after the election to the new Congress and to state and local government positions across the country.

That’s all correct, but there is an even more telling and absurd asymmetry. In many states it is illegal for Tesla to sell its cars directly to consumers! Namely, Alabama, Arkansas, Connecticut, Iowa, Kansas, Louisiana, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Carolina, South Dakota, Texas, West Virginia and Wisconsin.

Amazingly, that list includes TEXAS! Tesla builds the Model Y in Austin but they cannot sell directly to Texans who instead must technically buy their car from Tesla in another state. That shows you the power of the everywhere rent seekers.

The post The Everywhere Rent Seekers appeared first on Marginal REVOLUTION.

       

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Federal oil and gas rights are worth about $625 billion on the paper’s own method, down 75% in real terms since 1981. Federal land has gone the other way, to roughly $1.1 trillion to $1.6 trillion. Land, not minerals, is now the larger asset.

Here is the full post.

The post USG assets are worth less than Arnold Kling had thought appeared first on Marginal REVOLUTION.

       

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Tyler opens by asking Terence what it feels like to contemplate Euler’s identity, quantum mechanics, the Riemann hypothesis, and Gödel’s incompleteness theorem, before moving into why mathematical history gets stripped out of classrooms, whether abstract math is a lottery ticket or a unified whole, string theory’s beauty versus its lack of confirmation, why real analysis arrived so late, why zero took until the 7th century to be invented, game theory, the Black-Scholes model, Bostrom’s simulation argument, and Newcomb’s paradox, math education and the case for teaching more statistics, Tao’s view that AI has pushed mathematics from an era of “proof scarcity” into “proof abundance,” what that means for the research paper, why video talks may become more valuable than papers themselves, Tao’s favorite Hong Kong action movies, his unconventional path through an Australian university at age 13, his most unusual work habit, his verdict on the Collatz conjecture, and more.

Excerpt:

COWEN: When you look at mathematical economics over the last two decades, is it interesting to you as math, or is it just a bunch of people doing something different that, to you, is low level?

TAO: Oh, yes, I was always fascinated by economics. I love game theory, and actually often there are pure math problems where it’s actually good to think in game theory terms. In analysis, you have all these complicated statements, like for every ε, there exists a δ, blah, blah, blah. It looks very intimidating, but if you phrase it as a game, your opponent is giving you an ε, you need to counter that ε with a δ of your own. If you view it as a game, then actually you can talk about strategies and who’s ahead, who’s behind. It actually activates really good modes of thinking.

I’ve used economic thinking in my own work. We talk about cost functions and minimizing loss. There are lots of concepts. Ricardo’s law of comparative advantage, I use that to run my life. There are so many things I can do. What do I choose to spend my time on? It’s actually a very useful principle: comparative advantage.

Economics, I find it’s a very good arena to understand the difference between models and reality. Physics, we’re spoiled because we’ve had these great physicists who’ve created these very accurate models of the real world, classical mechanics, quantum mechanics, and they’re very well tested and very accurate. You can just go to town exploring their consequences and still predict reality. Economics is a much more mixed situation where you have models, and sometimes they predict the real world very well, but sometimes they don’t.

In addition to doing the math, you also have to do the modeling, and you have to understand the limits of when a model starts breaking down and what you need to do to adjust it. I find that fascinating. It’s somewhere between the hard sciences and the social sciences. There’s enough hard models that you can do math, but there’s also enough real-world messiness that you have to also grapple with the subjectivity. As an outsider, I find it a really fascinating subject.

COWEN: When you looked at the Black-Scholes model, were you shocked when the probability doesn’t matter? Was that beautiful to you?

TAO: Yes, yes, yes. There is a special feature of Gaussian fluctuations that you can price options just by knowing risk. There is one algebraic miracle that makes this work. Normally, predicting future costs of anything is just an intractable problem, and there just happens to be this miracle. If you have Gaussian fluctuations, then it is beautiful. This is actually a really good example of when you need to know when the models work and when they don’t work. If you don’t have Gaussian fluctuations, then actually Black-Scholes is somewhat dangerous to use, especially if you compound it into derivatives over and over again. Again, that’s an old story. You know what happened in 2009.

Self-recommending, and of interest throughout.  There is some on AI, but he has spoken so much on that topic lately I decided not to emphasize it.  And Terry does have a new book out, namely Six Math Essentials.

The post My excellent Conversation with Terence Tao appeared first on Marginal REVOLUTION.

       

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• Tao has some useful remarks about mathematics as setting ... by Bill Benzon
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• Not to speak ill of the dead, but he was overrated as a jurist. by Ted Craig
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Modern program evaluation prioritizes evidence from randomized controlled trials (RCTs) because prior research found that observational estimates of program impacts often diverge substantially from experimental benchmarks. We revisit this finding — which was based on small survey datasets — in the era of large-scale administrative data. Using anonymized income tax records covering all American adults between 1999 and 2025, we estimate the effects of workforce training programs on earnings by using machine learning models to predict counterfactuals from rich data on prior work histories, demographics, and residential locations. Rather than testing whether our observational estimates are unbiased, we use 37 RCTs to estimate the distribution of their bias. We find that our machine learning estimates closely match the RCT estimates, with root mean squared error (RMSE) comparable to that of the RCTs themselves. Our method reduces RMSE by 60% relative to parametric estimators used in prior work because our larger samples and richer covariates permit more flexible, higher-dimensional models of the counterfactual. Applying our method to a broader set of sites and enrollment years beyond the experimental sample, we find that treatment effects on earnings vary substantially across locations and over time, even within a given program. Programs that place more participants into target-sector jobs generate the largest earnings gains, with impacts that persist even after participants leave their initial jobs. These findings provide guidance for improving job training programs and, more broadly, illustrate a scalable approach to program evaluation that uses observational data to construct experimentally validated “digital twins.”

Work of this sort is going to revolutionize economic method.

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2. “Norway now has more than 2,000 underwater AI laser robots shooting lice off its salmon”

3. Coefficient Giving is looking to support new types of organizations.

4. The future of fermented food in Africa.

5. Shruti and Henry Oliver podcast with transcript on Gulliver’s Travels.

6. Houellebecq probed over comments about Muslims.

The post Wednesday assorted links appeared first on Marginal REVOLUTION.

       

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• In reply to Elad. The quote is considerably older than ... by akshually
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• In reply to Peter. Claude, how many nematodes are in a ... by kwazii
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So what should I ask him?

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UK panel data, 1991-2019. Couples who support different parties separate more often. The effect is as strong as some other mismatches, like education. Disagreement over Brexit mattered even more than party preference.

Here is a longer Nikolaus Haufler thread on European fertility, with a variety of interesting observations about other countries.

The post UK fact of the day appeared first on Marginal REVOLUTION.

       

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It may look like merely the latest story of a wealthy, powerful man making an irresponsible decision. But below the surface, it is also a harbinger of the erosion of privacy today, as well as the acceleration of espionage and the increasing value of secrets. More time and resources are going to be devoted to uncovering secrets, with honeypotting only one part of a larger picture: more spying, more attempted honeypotting, more private surveillance, more attempts to record (and search, using AI) conversations, more hacks of phones and computers, and more attempts to use AI to figure out the details of people’s lives and plans. Life is going to feel much more cloak-and-dagger, especially for the wealthy and influential…

The wealthy and influential are likely to take ever-greater care to minimize public scrutiny, protect their digital privacy, prevent journalists from going through their trash, and travel incognito, and their language in (supposedly) private conversations will have to be more guarded as well. We now live in an era where NDAs for hookups and even dinner dates are a thing.

Worth a ponder, as in the pending world secrets will be more valuable than before.  The super-wealthy and their companies can be decisive actors, there are more prediction markets than before, and AI is equalizing the value of high-quality but publicly available information.

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We study the statistical credibility of randomized controlled trials (RCTs) in development economics and the optimal allocation of resources to replication. Using 745 principal-hypothesis tests from 190 articles in leading economics journals, we find that actual t-statistics peak well below the theoretical benchmark of 2.8 expected under 80 percent power, though above values associated with p-hacking. This suggests many RCTs are underpowered. Estimating an empirical Bayes mixture model that accounts for selective publication, we find that approximately 30 percent of published rejections are false discoveries, with average directional power at only 40 percent. At conventional significance thresholds, the posterior probability of a false discovery reaches 66 percent. Doubling original sample sizes lowers the average false discovery rate by five percentage points, whereas tightening the significance threshold to 0.005 yields a larger reduction at the cost of total discoveries. Finally, we characterize a funder’s optimal replication policy under a fixed budget and an evidence standard. Under standard regularity conditions, replications concentrate in an intermediate interval of original test statistics, and the optimal replication sample size is single-peaked in the original t-statistic. Illustrative calibrations show that successful replications reduce aggregate false discovery rates to 1.8–7.8 percent, depending on the evidence standard.

That is from a new paper by Qingyuan Chai, Kevin Lang & Liang Zhong.

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2. Hayek for our time, by Cass Sunstein, free download for two weeks.

3. Works in Progress is looking for business and finance writers.

4. Matthew Lilley on Harvard grad students.  I believe they teach the permanent income hypothesis there?

5. Ethan Mollick on Fable 5.2.

6. John Cochrane on Brazil finances.

7. “The latest Godzilla release has become the first Japanese film to simultaneously top the box office in both North America and Japan on its opening weekend…” (FT)

The post Tuesday assorted links appeared first on Marginal REVOLUTION.

       

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We checked the anonymous AI audit of Emmanuel Saez and Gabriel Zucman’s research that Marginal Revolution featured on 28 Oct: all 147 problems it calls serious enough to change a headline number, against the papers, their sources and the authors’ own data. The audit’s facts hold up: for 131 of the 147, every fact we could check was right, and none had its core facts wrong. 21 of the 147 change a headline number; 98 depend on a disputed choice of method; 20 change no number; 8 we could not check on that question. A second pass graded the 99 flags we first counted as depending on a disputed choice of method: 11 standard, 70 defensible, 11 questionable, 6 we could not grade, and 1 contested. It found one, No. 54, that does change a headline number, which is why the counts are now 21 and 98. Saez and Zucman were sent our findings on 29 Oct and did not reply by 6 Nov.

Here is the audit of the audit, open code and data, kudos to the authors.  Here was the original AI audit.  Here is the October 30 revision of that audit.

Addendum: And a revised version with less jargon.

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