Measure is unceasing

An estimate of the value of Metaculus questions

tl;dr: Metaculus is an EA-adjacent forecasting platform. By my estimation, most Metaculus questions fail to directly influence decisions, but a few fall at the sweet spot between large scope, high decision relevance, and good fit for forecasters. That said, perhaps only a fraction of Metaculus’ impact is captured by the impact of their questions. In any case, the EA community could perhaps use evaluations of the value of Metaculus questions to incentivize it to produce more valuable questions.

Overall impressions

The holy grail for Metaculus would be questions on important topics which the kind of person who encounters Metaculus is in a position to do something about.

However, there is a tension between questions being decision-relevant and having a large scope because smaller entities might be more amenable than larger ones to being influenced. So it could turn out that the impact sweet spot is asking intimately decision-relevant questions that small organizations are willing to listen to. Conversely, as Metaculus grows its audience, perhaps questions with a large scope which change small decisions for many people might be more valuable.

But a large number of Metaculus questions fall in neither of those categories. On the one hand, we have very narrow questions which do not affect any decisions, such as What will the women’s winning 100m time in the 2024 Olympic final be? On the other hand, we also have questions such as “Will Israel recognize Palestine by 2070?” or “When will Hong Kong stop being a Special Administrative Region of China?”. These events seem so large as to essentially be non-influenceable, and thus I’d tend to think that their Metaculus questions are not valuable [3].

For Metaculus, another constraint is to have questions that interest forecasters. Interestingness is necessary to build a community around forecasting that may later have a large instrumental value.

Below, I outline a simple rubric that I think captures an important part of how Metaculus questions lead to value in the world. I look at questions’ decision-relevance, forecasting fit, and scope. 

Perhaps predictably, Metaculus is very good at making questions that are a good fit for being forecastable and suitable for forecasters instead of things like financial markets. Now that a forecasting community already exists and is known to be accurate and calibrated, it seems to me that the next bottleneck is to make forecasts action-guiding, perhaps by tweaking the scope of questions that Metaculus asks, or by reaching out to specific organizations.

Overall, the driving motivation behind this post is the perspective that:


I took a random sample of 200 questions and rated them according to:

  1. how far removed I think they are from influencing decisions
  2. how good of a fit they are for forecasters, and
  3. the scope of the matters they ask about

My results can be found here [1]. This work is rough and not meant to be definitive. In particular, I think that some of the rankings might be subject to some degree of idiosyncrasy. Nonetheless,  I hope that this might still be informative, and lead to some reflection about if and how Metaculus questions can be valuable.


How likely is this to change actual decisions?

Indirect effects, such as finding out if forecasters are calibrated on some domain, or improving one’s models of the world, are difficult to capture in a simple rubric. I’ve tried to capture this in “degrees away from being decision-relevant”, but this might be a bad approximation.

Forecasting fit

How valuable is it to generate insight on this topic from a forecasting perspective? Is anybody else trying?

For example, if other groups are looking at similar questions, I would rate the forecasting fit lower. Other groups might be liquid financial markets, sports betting markets, politics prediction markets, Nate Silver’s group at FiveThirtyEight, or experts I deem to be reliable.

Otherwise, I would use my intuition about what makes a question more forecastable. For instance, binary events are more straightforward to forecast (and to construct base rates about) than distributions. I also rated questions as more forecastable if they were about areas where forecasters live (Europe, US and UK.)


I categorized Metaculus questions as one of:

Given Metaculus’ readership, local questions most likely end up being more decision-relevant than global ones. For instance, California wildfires could influence Metaculus users in California, or questions about Virginia could influence their health department. As expanded below, I attempt to consider this by multiplying scope, decision-relevance and forecasting fit, but this might be too crude a system.

Considerations regarding scope could be made more robust by considering how many people the event under consideration affects (e.g., 1k, 10k, 100k, 1M, 10M, 100M, 1B+), and a measure of how much it affects them.

Overall value

The three elements (sort of) map to: scale, tractability and neglectedness, but not completely. One could also create more elaborate and robust rubrics.

In any case, I get a measure of the overall value by multiplying the numeric values of the three factors. For instance, if decision-relevance is zero, the overall value is zero as well. I am aware that this is not very methodologically elegant [2]. Nonetheless, I still wanted to have some measure of aggregate impact.


Given Metaculus’ historic roots as a general-purpose forecasting platform [4], it’s not surprising that their questions don’t have that much of an impact from the narrow perspective considered in this post: legibly changing decisions. In particular, many of Metaculus’ questions seem optimized for being fairly interesting to forecasters rather than directly valuable. However, given that Metaculus does appeal to the EA community for funding—see the 2019 and 2020 grants—it still feels fair game to evaluate them based on their expected impact.

That said, I can imagine other pathways to impact besides the impact of their questions. Two I can think of are:

It’s also possible that Metaculus is most valuable at the onset of emergencies, like the COVID pandemic, and less useful now that there are fewer unknown unknowns in the immediate horizon [5]. Because of the absence of these and other considerations or possible pathways to impact, this post does feel somewhat rough.

But suppose one determined that most of Metaculus’s impact came from the effects of their questions. In that case, the EA community could try to directly estimate its willingness to pay for Metaculus questions and just pay Metaculus and Metaculus forecasters that amount as a reward and an incentive. 

For instance, the highest-scoring questions [6] in my dataset—those with a score of 80—were:

If one estimates, arguendo, that good forecasts for each of those questions are worth something on the order of $2000 per year, one could get an estimate of $2000 * (10 questions) * (1818 questions with more than 10 predictions on Metaculus) / (200 questions in my sample) / (0.8 as a Pareto coefficient [7]) ~ $225,000 / year.

I’d be interested in getting pushback and other perspectives on any of these points.

Thanks to Ozzie Gooen and Kelsey Rodríguez for comments and editing help.


[1]: The code to extract these questions from Metaculus—using Metaforecast as a middle-point—can be found here. Note that by design, Metaforecast excludes questions with less than ten forecasts. The code to produce the R plots can be found here.

[2]: For instance—given equal forecasting fit—a question with a decision relevance of 3 and a scope of 1 might be more valuable than a question with a decision relevance of 1 and a scope of 3. 

[3]: One could make the case that these questions could be valuable if they influenced people to emigrate away from unstable regions. Still, I don’t expect prospective emigrant Metaculus readership in Hong Kong to be very high, nor Metaculus readers in Israel to have high property ownership rates in places that would be given back to Palestine.

[4]: I am aware that Metaculus has always aimed to produce useful probabilities, particularly around topics of scientific interest and AI. But the aim of being directly useful to the EA community in particular feels relatively recent.

[5]: Or, are there?

[6]: Because of the limitations of my methodology, these might not ultimately be the most valuable questions in my 200 question dataset. Conversely, some of the lowest-scoring questions (those with a value of 5 or lower) in my dataset were:

I know that there is an argument to be made that the oddly specific AI arxiv questions are valuable because they help inform how accurate other AI predictions might be, but I don’t buy it.

[7]: Assuming that 80% of the value of Metaculus questions comes from the most valuable ones, per something akin to the Pareto principle.