Maxwell

The tradeoff presented is excellent and the results are intriguing. The central result-- that penalty size doesn't really seem to matter relative to p(getting caught)-- is a bit confusing and makes me want to ask the authors more details about how their model works.

To be very simple about it, there is definitely a penalty that is large enough such that the difference between a 10% and 30% chance of getting caught would meaningfully change behavior. There are other simple "gut checks"//"sanity checks" like this that call into question some of the modeling assumptions.

Nonetheless, it's an interesting challenge/tradeoff and the authors deserve credit for attempting to model it quantitatively in such a short timeframe.

The "verification valley" is the headline here, and it's a genuinely useful concept for policy people to have in their vocabulary. But once you hear it, it's kind of obvious: half-assed monitoring catches some cheaters (who then get punished) without actually deterring anyone. You end up paying for enforcement that doesn't work. The model tells you this formally, which has value, but it's not exactly a surprise.

The bigger issue is that everything here lives in math-land. Every parameter is made up. No attempt to calibrate against, say, the IAEA's actual detection rates for nuclear material accounting, or carbon market compliance data, or anything real. Even one grounded parameter would change this from a toy model into something a policy person could point to.

The actors in the model also have no memory. They optimize one round at a time. In real international relations, the whole game is reputation: "we cooperated last time, so you should too." Stripping that out probably changes the results in ways that matter. And the distributional result (big countries lose, small countries win) is buried when it should be front and center: if the US or China face net costs from a verification regime, the regime doesn't happen. That's not a footnote, that's the whole ballgame.