🔎 DISCOVER MORE QUESTIONS RELEVANT TO YOU WITH METACULUS'S NEW FILTER & SORT TOOLS Where do you disagree with other forecasters? Which community predictions have shifted the most? And what was that nanotech forecast you meant to update? Metaculus has introduced new filter & sort tools that provide more control over the forecast feed so you can find the questions that matter to you. Learn more [https://www.metaculus.com/questions/15386/-discover-more-with-new-filter--sort-tools/]

Theoretical idea that could be implemented into Metaculus tldr; add an option to submit models of how to forecast a question, and also voting on the models.  To be more concrete, when someone submits a question, in addition to forecasting the question, you can submit a squiggle -- or just plain mathematical model -- of your best current guess of how to approach the problem. You define each subcomponent that is important in the final forecast and also how these subcomponents combine into the final forecast. Each subcomponent automatically becomes another forecasting question on the site that people can do the same to (if it is not already one).  Then in addition to a normal forecast, as we do right now, people can also forecast the subcomponents of the models, as well as vote on the models. If a model already includes previously forecasted questions, they automatically populate in the model.  The voting system on models could either just draw attention to the best models and encourage forecasting of the subcomponents, or even weight the models estimates into the overall forecast of the question. No idea if this would improve forecasting but it might make it more transparent and scalable.  I wrote a bit more in this google doc [https://docs.google.com/document/d/1yifAcxXjXtWKndp-kTyNtxwIlxOVWOwik2tU04HEYvc/edit?usp=sharing] if interested.    edit: I think this might just be guesstimate with memoization

Hi everyone, I am Jia, co-founder of Shamiri Health, an affordable mental health start-up in Kenya. I am thinking of writing up something on the DALY cost-effectiveness of investing in our company. I am very new to the community, and I wonder if I can solicit some suggestions on what is a good framework to use to evaluate the cost-effectiveness of impact investment into Healthcare companies. I think there could be two ways to go about this: 1) take an investment amount, and using some cashflow modeling, we can figure out how many users we can reach with that investment and calculate based on the largest user base we can reach, with the investment amount; or 2) we can do a comparative analysis with another more mature company in a different country, and use its % of population reach as our "terminal impact reach". Then, use that terminal user base as the base of the calculation.  The first approach is no doubt more conservative, but the latter, in my opinion, is the true impact counterfactual. Without the investment, we will likely not be able to raise enough funding since our TAM is not particularly attractive for non-impact investors. The challenge to using the latter is the "likelihood of success" of us carrying out the plan to reach our terminal user base. How would you go about this "likelihood number"? I would think it varies case by case, and one should factor in the team, the business model, the user goal, and the market, which is closer to venture capital's model of evaluating companies. What is the average number for impact ventures to succeed?  TLDR:  1. What is the counterfactual of impact investing? The immediate DALY that could be averted or the terminal DALY that could be averted? 2. What is the average success rate of impact healthcare ventures to reach their impact goal?

Metaculus is conducting its first user survey in nearly three years. If you have read analyses, consumed forecasts, or made predictions on Metaculus, we want to hear from you! Your feedback helps us better meet the needs of the forecasting community and is incredibly important to us.  Take the short survey here [https://rutgers.ca1.qualtrics.com/jfe/form/SV_0OhWGuJZg0XpHh4] — we truly appreciate it! (We'll be sure to share what we learn.) 

For a long time I found this surprisingly nonintuitive, so I made a spreadsheet that did it, which then expanded into some other things. * Spreadsheet [https://docs.google.com/spreadsheets/d/1GqdutJuvVDUJVIPLvcwe5a2fuFcgAC2jITV1LlxrjKM/edit#gid=0] here [https://docs.google.com/spreadsheets/d/1GqdutJuvVDUJVIPLvcwe5a2fuFcgAC2jITV1LlxrjKM/edit#gid=0], which has four tabs based on different views on how best to pick the fair place to bet where you and someone else disagree. (The fourth tab I didn't make at all, it was added by someone (Luke Sabor) who was passionate about the standard deviation method!)  * People have different beliefs / intuitions about what's fair! * An alternative to the mean probability would be to use the product of the odds ratios. Then if one person thinks .9 and the other .99, the "fair bet" will have implied probability more than .945. *  The problem with using Geometric mean can be highlighted if player 1 estimates 0.99 and player 2 estimates 0.01. This would actually lead player 2 to contribute ~90% of the bet for an EV of 0.09, while player 1 contributes ~10% for an EV of 0.89. I don't like that bet. In this case, mean prob and Z-score mean both agree at 50% contribution and equal EVs. * "The tradeoff here is that using Mean Prob gives equal expected values (see underlined bit), but I don't feel it accurately reflects "put your money where your mouth is". If you're 100 times more confident than the other player, you should be willing to put up 100 times more money. In the Mean prob case, me being 100 times more confident only leads me to put up 20 times the amount of money, even though expected values are more equal." * Then I ended up making an explainer video [https://www.youtube.com/watch?v=KOQ7OugP-Kc]because I was excited about it   Other spreadsheets I've seen in the space: * Brier score bet

Hi all!  Nice to see that there is now a sub-forum dedicated to Forecasting, this seems like a good place to ask what might be a silly question.   I am doing some work on integrating forecasting with government decision making.  There are several roadblocks to this, but one of them is generating good questions (See Rigor-Relevance trade-off [https://goodjudgment.com/question_clusters/]among other things).   One way to avoid this might be to simple ask questions about the targets the government has already set for itself, a lot of these are formulated in a SMART [1] way and are thus pretty forecastable. Forecasts on whether the government will reach its target also seem like they will be immediately actionable for decision makers.  This seemed like a decent strategy to me, but I think I have not seen them mentioned very often. So my question is simple: Is there some sort of major problem here I am overlooking?  The one major problem I could think of is that there might be an incentive for a sort of circular reasoning: If forecasters in aggregate think that the government might not be on its way to achieve a certain target then the gov might announce new policy to remedy the situation. Smart Forecasters might see this coming and start their initial forecast higher.  I think you can balance this by having forecasters forecast on intermediate targets as well.  For example: Most countries have international obligations to reduce their CO2 emissions by X% by 2030, instead of just forecasting the 2030 target you could forecasts on all the intermediate years as well.    1. ^ SMART stands for: Specific, Measurable, Assignable, Realistic, Time-related - See  https://en.wikipedia.org/wiki/SMART_criteria [https://en.wikipedia.org/wiki/SMART_criteria] 

On January 6, 2022, at 4pm GMT, I am going to host a gather town meetup [https://app.gather.town/app/aPVfK3G76UukgiHx/lesswrong-campus] to go through Scott Alexander's Prediction Competition [https://astralcodexten.substack.com/p/2023-prediction-contest] on Blind Mode which means you only spend max 5 minutes on each question. Because of that, and also possibly because these are the rules (I'm finding out), we likely won't collaborate (though if the rules ok it, maybe we do!), but if you've been wanting to enter and haven't yet made time, come, and we'll set some pomodoros and have a good time! Event link here: https://forum.effectivealtruism.org/events/wENgADx63Cs86b6A2/enter-scott-alexander-s-prediction-competition [https://forum.effectivealtruism.org/events/wENgADx63Cs86b6A2/enter-scott-alexander-s-prediction-competition]

I've heard a variety of takes on this, ranging from "people/decision-makers just don't use forecasting/prediction markets when they should," to "the main issue is that it's hard to come up with (and operationalize) useful questions," to "forecasting methods (including aggregation, etc.) and platforms are just subpar right now; improving them is the main priority." I'd be interested in what people think. Of course, there could also be a meta-take like "this is not the right question" — I'd be interested in discussing that, too.