Shitong Qiao

🚨 Last week, I sat with Profs. @hartzog and @JSilbey to discuss their excellent new paper, "How AI Destroys Institutions." The paper has been downloaded 30,000+ times, and it discusses some of AI's most important negative consequences. Watch our 56-minute conversation below:

Google just mass-published how 34 researchers actually use Gemini to solve open math and CS problems. not benchmarks. not demos. real unsolved problems across cryptography, physics, graph theory, and economics. 145 pages of case studies. here's what actually matters:

🚨 Forthcoming in Econometrica! How does trade liberalization affect developing countries with large informal sectors? Informality fundamentally changes how we think about the gains from trade. (1/5)

🎉 Congratulations to @AnnaFruhstorfer on her new book "Constitutional Change Under Autocracy." 🤩 We are proud to publish it in our Oxford Series in Comparative Constitutionalism. It is a model for comparative constitutional studies, and certain to become an invaluable resource for scholars of constitutional change. 📚 Check out the book here: global.oup.com/academic/produ…. It is now available for pre-order.

The Difference Between "Significant" and "Not Significant" is not Itself Statistically Significant

My article with Hein Goemans and Michael Weintraub is now out at @The_JOP ! See how prospect theory does (and doesn’t) apply to territorial disputes

My latest, with @scottjshapiro: "A world in which the powerful no longer feel the need to justify themselves is not merely unjust. It is barbaric . . . . That world does not have a legal order at all. It has only force, guided by one man’s whims." foreignaffairs.com/united-states/…

It is with great sadness that we share that David M. Webb MBE passed away peacefully in Hong Kong on Tuesday January 13th, 2026 from metastatic prostate cancer. David will be missed by family, many friends, and supporters. The family request privacy at this difficult time.

Did you know you can calculate the exact sample size you need before you even start your study? A sample size calculation — also called a power analysis — helps you determine the optimal number of observations for your statistical analysis. It ensures your study is large enough to detect meaningful effects, but not so large that you waste resources. Key advantages of performing a power analysis: ✔️ Avoid underpowered studies that might miss real effects ✔️ Save time and costs by avoiding unnecessarily large samples ✔️ Tailor your sample size to the effect size you care about detecting ✔️ Choose your desired confidence level and statistical power for robust results ✔️ Works for a wide range of statistical tests, from t‑tests to ANOVA and regression ✔️ Supported by many free R packages, such as pwr The image shows on the left side how the required sample size changes depending on the expected effect size — smaller effects require much larger samples. On the right side, you see an example of a calculated sample size for comparing two groups using a t-test, showing exactly how many participants are needed per group for the desired confidence level and statistical power. Sign up for my newsletter to get more practical tips on statistics, data science, R, and Python. Check out this link for more details: eepurl.com/gH6myT #pythonprogramming #programmer #DataAnalytics #RStats #Python #datastructure #DataAnalytics

CFP: Oxford Studies in Private Law Theory, Volume V. We're seeking contributions in contract, property, tort, fiduciary, equity, unjust enrichment, and remedies law. Submissions are 12,000 words, and you get a trip to Cape Town! Deadline August 1, 2026! philevents.org/event/show/142…

This paper from Harvard and MIT quietly answers the most important AI question nobody benchmarks properly: Can LLMs actually discover science, or are they just good at talking about it? The paper is called “Evaluating Large Language Models in Scientific Discovery”, and instead of asking models trivia questions, it tests something much harder: Can models form hypotheses, design experiments, interpret results, and update beliefs like real scientists? Here’s what the authors did differently 👇 • They evaluate LLMs across the full discovery loop hypothesis → experiment → observation → revision • Tasks span biology, chemistry, and physics, not toy puzzles • Models must work with incomplete data, noisy results, and false leads • Success is measured by scientific progress, not fluency or confidence What they found is sobering. LLMs are decent at suggesting hypotheses, but brittle at everything that follows. ✓ They overfit to surface patterns ✓ They struggle to abandon bad hypotheses even when evidence contradicts them ✓ They confuse correlation for causation ✓ They hallucinate explanations when experiments fail ✓ They optimize for plausibility, not truth Most striking result: `High benchmark scores do not correlate with scientific discovery ability.` Some top models that dominate standard reasoning tests completely fail when forced to run iterative experiments and update theories. Why this matters: Real science is not one-shot reasoning. It’s feedback, failure, revision, and restraint. LLMs today: • Talk like scientists • Write like scientists • But don’t think like scientists yet The paper’s core takeaway: Scientific intelligence is not language intelligence. It requires memory, hypothesis tracking, causal reasoning, and the ability to say “I was wrong.” Until models can reliably do that, claims about “AI scientists” are mostly premature. This paper doesn’t hype AI. It defines the gap we still need to close. And that’s exactly why it’s important.

This is one of the best short articles I've read on P-values, CIs, and significance tests

Does market integration foster a psychology inclined toward impersonal prosociality (cooperation with strangers)? Impressive new evidence from @_MaxPosch_ and Raz maxposch.com/s/Posch_Raz_Do… @JonHaidt @sapinker @DrNathanNunn @DAcemogluMIT

My old causal inference reading lists Intro to Causal Inference (undergrad stats, Yale, 2021): stat.berkeley.edu/~winston/causa… Causal Inference & Research Design (grad political science seminar, Yale, 2019): stat.berkeley.edu/~winston/causa…

Honored to host global experts at NYU Shanghai for a deep conversation on sovereign debt, development finance, and the future of global economic governance. cga.shanghai.nyu.edu/workshop-on-so…

📢 New USF Funding: Urban Urgencies The Urban Studies Foundation is launching a major new grant to support rapid-response collaborative research on the world’s most pressing urban challenges. Deadline: 23rd March 2026 Read more & share: urbanstudiesfoundation.org/urban-urgencie…

"While the Chinese state endeavors to institute the Party’s leadership in all aspects of the society, it has failed in its urban neighborhoods where hundreds of millions of middle-class homeowners, who are backbone of the regime, live." papers.ssrn.com/sol3/cf_dev/Ab…

This week, I had the privilege of presenting my forthcoming @CambridgeUP book “The Political Economy of Public Bureaucracy” at the University of Gothenburg’s Quality of Government Institute. Thanks to @VictorLapuente & F. Boräng for the kind invitation and the great discussion!

A webinar about LLM admission in elite U.S. law schools. Needless to say, Duke @DukeLaw is one of the very best!

"In the case that the primary purpose was to support the local car manufacturing industry, the compensation took the form of subsidizing taxi drivers to replace their old cars with electronic vehicles (“EVs”) manufactured locally." papers.ssrn.com/sol3/papers.cf…