Category: Current Affairs

Prediction Market Details

The Guardian has an interesting article on prediction markets. There are the usual worries about betting on death, as if insurance markets don’t already exist and about insider trading, which public markets have long dealt with. But there is also interesting material on who decides what happened when resolving bets about events made in language (as opposed to more objectively verified numbers).

On Monday, anonymous user “Harshad” asked in a Discord channel if there was “any chance” that he could still win his bet about whether US forces would enter Iran by the end of April. His money was on “no”.

But Polymarket appeared to be resolving the market to “yes”, after the US conducted an operation to rescue a crew member shot down on a mission over Isfahan over the weekend.

…At the moment, when there is a dispute, markets on Polymarket are settled by an anonymous group of people who hold a crypto token called UMA.

It’s an unusual way to decide what has happened. Some longtime users suggest it opens the platform to corruption. Different individuals hold different amounts of UMA, and therefore have different voting power.

It isn’t known who the largest UMA holders are, or what might affect how they vote. It is entirely possible that the people who finally settle a bet on UMA have large amounts of money staked on it.

There was also this bit about Prediction Hunt (I am an advisor) which is focused on cross-market arbitrage opportunities:

“I love to gamble,” said Joseph Francia.

Now in his early 30s, Francia counted cards in casinos while studying economics at Berkeley, and spent weekends in Reno, Nevada, playing blackjack. He’s not a thrill-seeking “Yolo” (you only live once) gambler, he said: he likes to bet when he has an edge on the house.

At university, he and a friend decided to collect data from a number of offshore sportsbooks, and start placing arbitrage bets: playing on the discrepancies in odds given by different betting sites.

“If the odds on the Lakers are really good on one site, and the odds on the Pacers are really good on another site, you could bet on basically both teams on different sportsbooks and make guaranteed profit,” he said.

That project was a student lark in 2017. But in 2025, he remembered it when he was suddenly laid off from his full-time job, just as prediction markets were taking off.

“I’m a spiritual, religious person,” he said. “The more secular people would say, this opportunity is coincidence. But in my head, I was like, this is a sign of something to some extent. Let me lean into this.”

So Francia started Prediction Hunt, a Discord channel and online community where thousands of people gather to trade tips and ideas for how to make money – and bet smart – on Polymarket. The Guardian spent roughly three weeks in this Discord channel.

There are alerts to track “fade” bets, where you try to follow the smart money: profitable wallets were betting “yes” on the Iranian regime falling by 30 April, for example, while unprofitable wallets were betting “no”.

There are alerts to track potential insiders, so you can copy their bets: one of these appears to have an inside line on interest rate decisions by the US Federal Reserve.

Getting these details right will be important but overall I am pleased that the news now regularly reports prediction market data when reporting stories–this is disciplining news from noise, something I predicted long ago in Entrepreneurial Economics.

AI, Unemployment and Work

Imagine I told you that AI was going to create a 40% unemployment rate. Sounds bad, right? Catastrophic even. Now imagine I told you that AI was going to create a 3-day working week. Sounds great, right? Wonderful even. Yet to a first approximation these are the same thing. 60% of people employed and 40% unemployed is the same number of working hours as 100% employed at 60% of the hours.

So even if you think AI is going to have a tremendous effect on work, the difference between catastrophe and wonderland boils down to distribution. It’s not impossible that AI renders some people unemployable, but that proposition is harder to defend than the idea that AI will be broadly productive. AI is a very general purpose technology, one likely to make many people more productive, including many people with fewer skills. Moreover, we have more policy control over the distribution of work than over the pure AI effect on work. Declare an AI dividend and create some more holidays, for example.

Nor is this argument purely theoretical. Between 1870 and today, hours of work in the United States fell by about 40% — from nearly 3,000 hours per year to about 1,800. Hours fells but unemployment did not increase. Moreover, not only did work hours fall, but childhood, retirement, and life expectancy all increased. In fact in 1870, about 30% of a person’s entire life was spent working — people worked, slept, and died. Today it’s closer to 10%. Thus in the past 100+ years or so the amount of work in a person’s lifetime has fallen by about 2/3rds and the amount of leisure, including retirement has increased. We have already sustained a massive increase in leisure. There’s no reason we cannot do it again.

LDS fact of the day

The Church of Jesus Christ of Latter-day Saints has grown 66% this century, fueled in part by a record-breaking number of convert baptisms in 2025.

The church had 10,752,986 members at the end of 1999. The church had 17,887,212 at the end of 2025, according to an annual statistical report released Saturday during the church’s 196th Annual General Conference.

Furthermore the growth is coming in every part of the world (as a qualifier I am not sure what the outflow is).  Here is the full article, via Tyler Ransom.

AI Risks

Two new papers/initiatives indicate severe risks from AI, interestingly in opposite directions. The first is that the most advanced frontier models are now capable of finding and exploiting software in ways that could be used to crash or control pretty much all the world’s major systems.

Anthropic: We formed Project Glasswing because of capabilities we’ve observed in a new frontier model trained by Anthropic that we believe could reshape cybersecurity. Claude Mythos2 Preview is a general-purpose, unreleased frontier model that reveals a stark fact: AI models have reached a level of coding capability where they can surpass all but the most skilled humans at finding and exploiting software vulnerabilities.

Mythos Preview has already found thousands of high-severity vulnerabilities, including some in every major operating system and web browser. Given the rate of AI progress, it will not be long before such capabilities proliferate, potentially beyond actors who are committed to deploying them safely. The fallout—for economies, public safety, and national security—could be severe. Project Glasswing is an urgent attempt to put these capabilities to work for defensive purposes.

That’s from Anthropic. The irony is that the company that has developed a frontier model capable of infiltrating and undermining more or less any computer system in the world is the one that has been forbidden from working with the US government. It’s as if a private firm developed nuclear weapons and the American government refused to work with them because they were too woke. Okey dokey.

The second paper on AI risks is AI Agent Traps from Google DeepMind. They point out that AI agents on the web are vulnerable to all kinds of attacks from things like text in html never read by humans, hidden commands in pdfs, commands encoded in the pixels of images using steganography and so forth.

Putting this together we have the worrying combination that very powerful AI’s are very vulnerable. Will AI solve the problems of AI? Eventually the software will be made secure but weird things happen in arms races and its going to be a bump ride.

The President(s) Fought the Law and the Law Won

In our textbook, Modern Principles, Tyler and I emphasize that Congress and the President are subject to a higher law, the law of supply and demand. In an excellent column, Jason Furman gives a clear example of how difficult it is to fight the law of inelastic demand:

…Today a given number of autoworkers can make, according to my calculations, three times as many cars in a year as they could 50 years ago.

The problem is that consumers do not want three times as many cars. Even as people get richer, they increase their spending on manufactured goods only modestly, preferring instead to spend more on services like travel, health care and dining out. There are only so many cars a family can own, but that’s not the case for expensive vacations or fancy meals. As a result we have fewer people working in auto factories and more people working in luxury resorts and the like.

These forces — rising productivity but steady demand — explain why the United States was losing manufacturing job share as far back as the 1950s and 1960s, long before trade became a major factor.

The Happiness Crash of 2020

From the still-active Sam Peltzman:

I document a sudden, sharp and historically unprecedented decline in self-reported happiness in the US population. It occurred during 2020, the year of the Covid pandemic, and mainly persists through 2024. This happiness crash spread across nearly all typical demographics and geographies. The happiest groups pre-Covid (e.g., whites, high income, well-educated and politically/ideologically right-leaning) tend to show the largest happiness reductions. The glaring exception is marital status, which has consistently been an important marker for happiness. The already wide happiness premium for marriage has, if anything, become slightly wider. With both married and unmarried reporting large declines in happiness the country has become segregated: slightly over half-the married adults-remain happy on balance; the unmarried, nearly half, are now distinctly unhappy. I also show that across a number of aspects of personal and social capital post-Covid deterioration is the norm, including a collapse of belief in the fairness of others and of trust in the US Supreme Court.

Here is the paper, via the excellent Kevin Lewis.

Grade Caps are Not a Good Solution to Grade Inflation

It’s well known that grade inflation has “degraded” the informational content of grades at many colleges. At Harvard, two-thirds of all undergraduate grades are now A’s—up from about a quarter two decades ago. In response, a Harvard faculty committee has proposed capping A grades at 20 percent of each class (plus a cushion for small courses). That may give professors some cover to resist further inflation, but it doesn’t solve the real problem.

The real problem is not inflation per se. It’s that students are penalized for taking harder courses with stronger peers. A grade cap leaves that distortion intact—and can even amplify it. As Harvard economist Scott Kominers argues:

A grade cap systematically penalizes ambitious students for surrounding themselves with strong classmates. Perverse course-shopping incentives ensue as a result. A student who is prepared for an advanced course but concerned about landing in the bottom 80 percent may choose to drop down preemptively—seeking out a pond where they are a relatively bigger fish. As strong students move into lower-level courses, competition for A grades increases there while harder courses continue to shrink—reducing their A allocation further and driving more students away.

The underlying issue is informational. A grade tries to capture two things—student ability and course difficulty—with a single number. Gans and Kominers show that in general this is impossible: if some students take math and earn B’s while others take political science and earn A’s, there is no way, from grades alone, to tell whether the difference reflects ability or course difficulty.

There is, however, a solution in some cases. Clearly, if every student takes some math and political science courses, informative patterns can emerge. If math students tend to get B’s in math but A’s in political science, while political science students get A’s in their own field but C’s in math, you can begin to separate course difficulty from student ability.

Students don’t all overlap the same classes. But full overlap isn’t necessary—you just need a connected network. If Alice just takes math courses, Joe takes math and political science courses, and Bob just takes political science courses, then Alice and Bob can be compared through Joe. With enough of these links, the entire system can be stitched together. The more overlap, the more precise the estimates.

Valen Johnson proposed a practical method along these lines in 1997. Gans and Kominers embed the same intuition in a much more general framework, showing exactly what can and cannot be inferred, and under what conditions.

The great thing about achievement indexes based on relative comparisons is that they are robust to grade inflation and do not penalize students for taking hard classes or subjects. A political science student who chooses to take a tough math class instead of an easy-A intro to sociology course won’t be penalized because their low math grade will, in effect, by boosted by the difficulty of the course/quality of the students. That’s good for the student and also good for disciplines that have lost students over the years because they held the line on grade inflation.

One final point. Harvard’s cap proposal appears to have been developed with little engagement with researchers who have studied problems like these for decades in the mechanism and market design literature—people like Kominers, Gans, Budish, Roth, Maskin, and Sönmez, some of them at Harvard! Moreover, this isn’t a case of ignoring high-theory for practice. The high-theory of mechanism design has produced real-world systems including kidney exchanges, school choice mechanisms, physician-resident matching, even the assignment of students to courses at Harvard, as well as many other mechanisms. Mechanism design is practical.

Grade inflation is a mechanism design problem—and we know a lot about how to solve it, if we want to solve it.

The Candidates’ tournament

Caruana and Sindharov have won today, obviously boosting Caruana’s chances as favorite (he beat Nakamura, the number two rated player in the tournament).  Yet what the chess world needs right now is not a winner, but rather a greater sense of legitimacy for the world title.  Ideally the same person should win a championship match two or three times in a row, and with a decisive margin.  They do not have to be as good as Carlsen, just clearly better than everyone else.  Nepo never quite made it, Ding has retreated from the chess world, and Caruana has yet to win a first title.  Is he young enough to win a few in a row?  Or are we waiting for Nordirbek Abdusattorov (or someone else) to enter the cycle?  I fear decisiveness is not soon on the way.  There are several (relatively) weak players in this tournament, so a variety of players can win just by beating up on the weakies, rather than by demonstrating mastery over their strongest peers.  Legitimacy is likely to remain uncertain, to the detriment of the chess world.  But soon we will know more.

Shruti interviews V. Anantha Nageswaran on the Indian economy

He is currently serving as the Chief Economic Advisor to the Government of India, and also is the co-author of the books Economics of Derivatives and The Rise of Finance: Causes, Consequences and Cures.  The podcast covers import substitution and strategic resilience, futures and options market, gross fixed capital formation, crypto markets, India’s growth trajectory, and much more.

Here is the audio and video on YouTube.  Here is a linked transcript.  Excerpt:

RAJAGOPALAN: The policy response to this has come in a couple of different ways. One has come through SEBI. It has started raising contract sizes and limiting weekly expiration,and so on. Another instrument has come through taxation. There have been STT [Securities Transactions Tax] hikes in consecutive budgets,but there is one thing about STT that I want to understand a little bit better from someone like you who has thought about this deeply.

Now, STT on futures is being levied on the notional value of the contract, which is the full traded price, whereas the STT on the options is levied on the premium, which is a small fraction of the overall underlying value of the notional exposure. The effective tax that is imposed is much more on the futures trade, manyfold more actually, than it is on the options trade, whereas the speculation is mostly happening on the options side, which is also where most of the retail investors are losing money because the futures side is much better capitalized, larger firms, and so on.

NAGESWARAN: No, also the futures side is probably used more by institutions, and therefore, they are able to put up the margin requirement, etc., better than the options trades, where the individuals are being sold almost like the₹10 sachet-type options, and the options…

RAJAGOPALAN: Exactly, sachetization options, absolutely.

NAGESWARAN: Yes. Go ahead.

RAJAGOPALAN: Now with each successive hike in the STT,we’re seeing the gap widen. It’s on the margin, making futures relatively more expensive than options just because it’s taxing each trade. It’s like a toll fee that’s paid almost on every transaction. Your book was precisely about understanding these kinds of policy instruments. Given that now we have a tax instrument which inadvertently favors the more speculative instrument. Is that a good way of thinking about it, or how would you think about this problem?

NAGESWARAN: No, I think you have given me a lot to think about on this. I probably haven’t applied my mind as much to the mechanics of the STT being levied on the premium when it comes to options, but on the notional value of the contract when it comes to futures. Actually, you have given me something to think about. As you said, it could be having the unintended consequence of reducing the hedging role of futures, which probably is playing a better role there and encouraging the speculative element. Let me think about it and also probably take back this aspect of the conversation back to my colleagues in the revenue department, in the Ministry of Finance. Thank you for that, yes.

Of great importance for the world’s most populous country.

Is Tinder actually OK?

Online dating apps have transformed the dating market, yet their broader effects remain unclear. We study Tinder’s impact on college students using its initial marketing focus on Greek organizations for identification. We show that the full-scale launch of Tinder led to a sharp, persistent increase in sexual activity, but with little corresponding impact on the formation of long-term relationships or relationship quality. Dating outcome inequality, especially among men, rose, alongside rates of sexual assault and STDs. However, despite these changes, Tinder’s introduction did not worsen students’ mental health on average and may have even led to improvements for female students.

That is from a new paper published in AEJ: Applied Economics, by Berkeren Büyükeren, Alexey Makarin, and Heyu Xiong.