Forecasting Newsletter for October 2022
Nuclear probabilities spiked & spooked Musk, Council on Strategic Risk seeking Strategic Forecaster.
Nuclear probability estimates spiked and spooked Elon Musk.
Markov Chain Monte Carlo Without all the Bullshit: Old blog post delivers on its title.
Prediction Markets, Forecasting Platforms &co
Odds and Ends
Prediction Markets and Forecasting Platforms
Kalshi hosted a competition to predict congressional races (a). If someone predicts all races correctly, they get $100k, otherwise the most accurate person will receive $25k. To be clear, this is a marketing gimmick, and participants make Yes/No rather than probabilistic predictions. But I thought I'd report on it given the high amount.
As Metaculus continues to build capacity, they have started to launch several initiatives, namely Forecasting Our World In Data (a), an AI forecasting team (a), a "Red Lines in Ukraine" (a) project, and a "FluSight Challenge 2022/23" (a). They are also hiring (a)
Odds and ends
The US midterm elections were an eagerly awaited event in the prediction market world. Participating so as to make a profit requires a level of commitment, focus and sheer fucking will that I recognize I don't have. For coverage, interested readers might want to look to StarSpangledGamblers (a), or check on the Twitters of various politics bettors, such as Domah (a), Peter Wildeford (a) or iabvek (a).
My forecasting group, Samotsvety, posted an estimate of the likelihood that Russia would use a nuclear weapon, including a calculator so that people could more easily input their own estimates. This was followed by the Swift Institute (a), and both estimates were reported in WIRED magazine. Since then the probability seems much lower, as the strategic situation becomes clearer.
The Council on Strategic Risks (a) is hiring for a full-time Strategic Foresight Senior Fellow (a), and is offering $78,000 to $114,000 per year plus benefits. My impression is that this post would be impactful and policy-relevant.
The $5k challenge to quantify the impact of 80,000 hours' top career paths (a) is still open, until the 1st of December. So far I only know of two applications, and since the pot is split between the participants, participation might have a particularly high expected monetary value.
I came across this really neat explanation of Markov Chain Monte Carlo: Markov Chain Monte Carlo Without all the Bullshit (a). It requires knowledge of linear algebra, but it was otherwise really neat. I would encourage readers who have heard about the method but never learnt how it works to give it a read.
Note to the future: All links are added automatically to the Internet Archive, using this tool (a). "(a)" for archived links was inspired by Milan Griffes (a), Andrew Zuckerman (a), and Alexey Guzey (a).
> In 1646, Magnenus estimated the number of atoms contained in a piece of incense from an argument based on the sense of smell (if a fraction of the grain is burned, the number of particles can be estimated from the volume within which the scent is still perceptible). His estimate for the number of particles in a piece of incense "not larger than a pea" was of the order of 10^18. This estimate is remarkably accurate, within about three orders of magnitude of the true value (based on the number of molecules in the unburned incense) and thus only one order of magnitude off in linear dimension of the molecule. Magnenus was by far the earliest scholar to give a reasonable estimate for the size of a molecule, the first "modern" estimate was given more than 200 years later, in 1865, by Josef Loschmidt
— Wikipedia, on Johann Chrysostom Magnenus.