Quick takes

Current takeaways from the 2024 US election <> forecasting community. First section in Forecasting newsletter: US elections, posting here because it has some overlap with EA. 1. Polymarket beat legacy institutions at processing information, in real time and in general. It was just much faster at calling states, and more confident earlier on the correct outcome. 2. The OG prediction markets community, the community which has been betting on politics and increasing their bankroll since PredictIt, was on the wrong side of 50%—1, 2, 3, 4, 5. It was the democratic, open-to-all nature of it, the Frenchman who was convinced that mainstream polls were pretty tortured and bet ~$45M, what moved Polymarket to the right side of 50/50. 3. Polls seem like a garbage in garbage out kind of situation these days. How do you get a representative sample? The answer is maybe that you don't. 4. Polymarket will live. They were useful to the Trump campaign, which has a much warmer perspective on crypto. The federal government isn't going to prosecute them, nor bettors. Regulatory agencies, like the CFTC and the SEC, which have taken such a prominent role in recent editions of this newsletter, don't really matter now, as they will be aligned with financial innovation rather than opposed to it. 5. NYT/Siena really fucked up with their last poll and the coverage of it. So did Ann Selzer. Some prediction market bettors might have thought that you could do the bounded distrust, but in hindsight it turns out that you can't. Looking back, to the extent you trust these institutions, they can ratchet their deceptiveness (from misleading headlines, incomplete stories, incomplete quotes out of context, not reporting on important stories, etc.) for clicks and hopium, to shape the information landscape for a managerial class that... will no longer be in power in America. 6. Elon Musk and Peter Thiel look like geniuses. In contrast Dustin Moskovitz couldn't get SB 1047 passed despite being the s

@Austin and I made a nice visualization for election-conditioned prediction markets! https://policypredictions.com/ -- see how the markets think the world will look under Trump vs Harris There's an AI market on there just for EAs :)

‘Five Years After AGI’ Focus Week happening over at Metaculus. Inspired in part by the EA Forum’s recent debate week, Metaculus is running a “focus week” this week, aimed at trying to make intellectual progress on the issue of “What will the world look like five years after AGI (assuming that humans are not extinct)[1]?” Leaders of AGI companies, while vocal about some things they anticipate in a post-AGI world (for example, bullishness in AGI making scientific advances), seem deliberately vague about other aspects. For example, power (will AGI companies have a lot of it? all of it?), whether some of the scientific advances might backfire (e.g., a vulnerable world scenario or a race-to-the-bottom digital minds takeoff), and how exactly AGI will be used for “the benefit of all.” Forecasting questions for the week range from “Percentage living in poverty?” to “Nuclear deterrence undermined?” to “‘Long reflection’ underway?” Those interested: head over here. You can participate by: * Forecasting * Commenting * Comments are especially valuable on long-term questions, because the forecasting community has less of a track record at these time scales.[2][3] * Writing questions * There may well be some gaps in the admin-created question set.[4] We welcome question contributions from users. The focus week will likely be followed by an essay contest, since a large part of the value in this initiative, we believe, lies in generating concrete stories for how the future might play out (and for what the inflection points might be). More details to come.[5] 1. ^ This is not to say that we firmly believe extinction won’t happen. I personally put p(doom) at around 60%. At the same time, however, as I have previously written, I believe that more important trajectory changes lie ahead if humanity does manage to avoid extinction, and that it is worth planning for these things now. 2. ^ Moreover, I personally take Nuño Sempere’s “Hurdles of using f

Not that we can do much about it, but I find the idea of Trump being president in a time that we're getting closer and closer to AGI pretty terrifying. A second Trump term is going to have a lot more craziness and far fewer checks on his power, and I expect it would have significant effects on the global trajectory of AI.

Metaculus introduces 'Changed my mind' button This short take is a linkpost for this Discussion Post by Metaculus's Technical Product Manager * Do you sometimes read a comment so good that you revise your whole world model and start predicting the opposite of what you believed before? * Do you ever read a comment and think “Huh. Hadn’t thought of that.” and then tweak your prediction by a few percentage points? * Do you ever read a comment so clearly wrong that you update in the opposite direction? * Do you ever wish you could easily tell other forecasters that what they share is valuable to you? * Do you ever want to update you prediction right after reading a comment, without getting RSI in your scrolling finger? Did a comment change your mind? Give Metaculus's new 'Changed my mind' button a click!  And for binary questions, clicking the button lets you update your prediction directly from the comment. 

This December is the last month unlimited Manifold Markets currency redemptions for donations are assured: https://manifoldmarkets.notion.site/The-New-Deal-for-Manifold-s-Charity-Program-1527421b89224370a30dc1c7820c23ec Highly recommend redeeming donations this month since there are orders of magnitude more currency outstanding than can be donated in future months

As someone predisposed to like modeling, the key takeaway I got from Justin Sandefur's Asterisk essay PEPFAR and the Costs of Cost-Benefit Analysis was this corrective reminder – emphasis mine, focusing on what changed my mind: More detail: Tangentially, I suspect this sort of attitude (Iraq invasion notwithstanding) would naturally arise out of a definite optimism mindset (that essay by Dan Wang is incidentally a great read; his follow-up is more comprehensive and clearly argued, but I prefer the original for inspiration). It seems to me that Justin has this mindset as well, cf. his analogy to climate change in comparing economists' carbon taxes and cap-and-trade schemes vs progressive activists pushing for green tech investment to bend the cost curve. He concludes:  Aside from his climate change example above, I'd be curious to know what other domains economists are making analytical mistakes in w.r.t. cost-benefit modeling, since I'm probably predisposed to making the same kinds of mistakes. 

Simple Forecasting Metrics? I've been thinking about the simplicity of explaining certain forecasting concepts versus the complexity of others. Take calibration, for instance: it's simple to explain. If someone says something is 80% likely, it should happen about 80% of the time. But other metrics, like the Brier score, are harder to convey: What exactly does it measure? How well does it reflect a forecaster's accuracy? How do you interpret it? All of this requires a lot of explanation for anyone not interested in the science of Forecasting.  What if we had an easily interpretable metric that could tell you, at a glance, whether a forecaster is accurate? A metric so simple that it could fit within a tweet or catch the attention of someone skimming a report—someone who might be interested in platforms like Metaculus. Imagine if we could say, "When Metaculus predicts something with 80% certainty, it happens between X and Y% of the time," or "On average, Metaculus forecasts are off by X%". This kind of clarity could make comparing forecasting sources and platforms far easier.  I'm curious whether anyone has explored creating such a concise metric—one that simplifies these ideas for newcomers while still being informative. It could be a valuable way to persuade others to trust and use forecasting platforms or prediction markets as reliable sources. I'm interested in hearing any thoughts or seeing any work that has been done in this direction.