David_Althaus

Researcher at the Center on Long-Term Risk.

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Incentivizing forecasting via social media

Yes, we have talked with Rebecca about these ideas. 

Incentivizing forecasting via social media

as noted in Nuño's comment this comparison holds little weight when the questions aren't the same or on the same time scales

Right, definitely, I forgot to add this. I wasn't trying to say that Forecast is more accurate than real-money prediction markets (or other forecasting platforms for that matter) but rather that Forecasts' forecasting accuracy is at least clearly above the this-is-silly level.

Incentivizing forecasting via social media

Not sure if I think it would require that many more resources. I was surprised that Metaculus' AI forecasting tournament was featured on Forbes the other day with "only" $50k in prizes. Also, from the point of view of a participant, the EA groups forecasting tournament seemed to go really well and introduced at least 6 people I know of into more serious forecasting (being run by volunteers with prizes in form of $500 donation money).

Yeah, I guess I was thinking about introducing millions of people to forecasting. But yeah, forecasting tournaments are a great idea. 

I agree that a forecasting Coursera course is promising and much more realistic.

Incentivizing forecasting via social media

Thanks, great points!

would excited to hear takes from people working at social media companies.

Yeah, me too. For what it's worth, Forecast mentions our post here.

On (a), I think forecasting accuracy and the qualities it's a proxy for represent a small subset of the space that determines which content I'd like to see promoted

Yeah, as we discuss in this section, forecasting accuracy is surely not the most important thing. If it were up to me, I'd focus on spreading (sophisticated) content on, say, effective altruism, AI safety, and so on. Of course, most people would never agree with this. In contrast, forecasting is perhaps something almost everyone can get behind and is also objectively measurable. 

I agree that the concerns you list under (b) need to be addressed. 

 

Incentivizing forecasting via social media

Thanks for your detailed comment.

but it seems very odd to think of it as a Facebook feature (or other social media platform)

Yeah, maybe all of this is a bit fantastical. :) 

Facebook and social media in general don't really have an intellectual "brand". It seems likely that if you did this as a Facebook feature, it would be more likely to get dismissed as "just another silly Facebook game." Or if most of the people using it weren't putting much effort into it, the predictionslikely wouldn't be that accurate, and that could undermine the effort to convince the public of its value.

That’s certainly possible. For what it’s worth, while Facebook’s Forecast was met with some amount of skepticism, I wouldn’t say it was “dismissed” out of hand. The forecasting accuracy of Forecast’s users was also fairly good: “Forecast's midpoint brier score [...] across all closed Forecasts over the past few months is 0.204, compared to Good Judgement's published result of 0.227 for prediction markets.”

However, it’s true that a greater integration with Facebook would probably make the feature more controversial and also result in a lower forecasting accuracy.

Btw, Facebook is just one example—I write this because you seem to focus exclusively on Facebook in your comment. In some ways, Twitter might be more appropriate for such features.

Am I understanding correctly that each user is given one prediction score that applies to all their content? So that means that if someone is bad (good) at predicting COVID case counts, then if they post something else it gets down- (up-) weighted, even if the something else has nothing to do with COVID

That would be the less complicated option. It might be perceived as being unfair—not sure if this will be a big problem though.

I’m working under the assumption that people who make more correct forecasts in one domain will also tend to have a more accurate model of the world in other domains—on average, of course, there will be (many) exceptions. I’m not saying this is ideal; it’s just an improvement over the status quo where forecasting accuracy practically doesn’t matter all in determining how many people read your content.

Or do you have some system to figure out which forecasting questions count toward the recommender score for which pieces of content?

That would be the other, more complicated alternative. Perhaps this is feasible when using more coarse-grained domains like politics, medicine, technology, entertainment, et cetera, maybe in combination with machine learning. 

Even then it seems weird - if someone made bad predictions about COVID in the past, that doesn't necessarily imply that content they post now is bad.

Well, sure. But across all users there will likely be a positive correlation between past and future accuracy. I think it would be good for the world if people who made more correct forecasts about COVID in the past would receive more “views” than those who made more incorrect forecasts about COVID—even though it’s practically guaranteed that some people in the latter group will improve a lot (though in that case, they will be rewarded by the recommender system in the future for that) and even make better forecasts than people in the former group. 

Presumably the purpose of this is to teach people how to be better forecasters.

I wouldn’t say that’s the main purpose. 

If you have to hide other people's forecasts to prevent abuse, then how are you supposed to learn by watching other forecasters?

My understanding is that’s how other platforms, like e.g. Metaculus, work as well. Of course, people can still write comments about what they forecasted and how they arrived at their conclusions. 

Also, I think one can become better at forecasting on one’s own? (I think most people get better calibrated when they do calibration exercises on their own—they don’t need to watch other people do it.)

All the comparisons between forecasting and traditional fact-checking are weird because they seem to address different issues; forecasting doesn't seem to be a replacement or alternative to fact-checking. 

I didn’t mean to suggest that forecasting should replace fact-checking (though I can now see how our post and appendix conveyed that message). When comparing forecasting to fact-checking, I had in mind whether one should design recommendation algorithms to punish people whose statements were labeled false by fact-checkers. 

In general, this seems to require a lot of editorial judgment on the part of Facebook as to what forecasting questions to use and what resolution criteria. [...] My guess is that this sort of editorial role is not something that social media platforms would be particularly enthusiastic about 

Yeah, they certainly would be reluctant to do that. But given that they already do fact-checking, it doesn’t seem impossible. 

Another way to game the system that you didn't mention here: set up a bunch of accounts, make different predictions on each of them, and then abandon all the ones that got low scores, and start posting the stuff you want on the account that got a high score.

I agree that this is an issue. In practice, it doesn’t seem that concerning though. First, the recommendation algorithm would obviously need to take into account the number of forecasts in addition to their average accuracy in order to minimize rewarding statistical flukes. (Similarly to how Yelp displays restaurants with, say, an average of 4.5 rating but 100 ratings more prominently than restaurants with an average rating of 5.0 but only 5 ratings.) Thus, you would actually need to put in a lot of work to make this worthwhile (and set up, say, hundreds of accounts) or get very lucky (which is of course always possible). 

It would probably also be prudent to put in some sort of decay to the forecasting accuracy boosting (such that a good forecasting accuracy, say, 10 years ago matters less than a good forecasting accuracy in this year) in order to incentivize users to continue making forecasts. Otherwise, people who achieved a very high forecasting accuracy in year 1 would be inclined to stop forecasting in order to avoid a regression to the mean.  

I wonder if it might make more sense to think of this as a feature on a website like FiveThirtyEight that already has an audience that's interested in probabilistic predictions and models. 

Yeah, that’s an interesting idea. On the other hand, FiveThirtyEight is much smaller and it’s readers are presumably already more sophisticated so the potential upside seems smaller. 

That being said, I agree that it might make more sense to focus on platforms with a more sophisticated user base (like, say, Substack). Or focus on news outlets like, say, the Washington Post. That might even be more promising. 

Incentivizing forecasting via social media

Forecasting is such an intellectual exercise, I’d be really surprised if it becomes a popular feature on social media platforms

I'd also be surprised. :) Perhaps I'm not as pessimistic as you though. In a way, forecasting is not that "intellectual". Many people bet on sport games which (implicitly) involves forecasting. Most people are also interested in weather and election forecasts and know how to interpret them (roughly).

Of course, forecasting wouldn't become popular because it's intrinsically enjoyable. People would have to get incentivized to do so (the point of our post). However, people are willing to do pretty complicated things (e.g., search engine optimization) in order to boost their views, so maybe this isn't that implausible.

As we mention in the essay, one could also make forecasting much easier and more intuitive, by e.g. not using those fancy probability distributions like on Metaculus, but maybe just a simple slider ranging from 0% to 100%.

Forecasting also doesn't have to be very popular. Even in our best case scenario, we envision that only a few percent of users make regular forecasts. It doesn't seem highly unrealistic that many of the smartest and most engaged social media users (e.g., journalists) would be open to forecasting, especially if it boosts their views.

But yeah, given that there is no real demand for forecasting features, it would be really difficult to convince social media executives to adopt such features.

I think I‘d approach it more like making math or programming or chess a more widely shared skill

I agree that this approach is more realistic. :) However, it would require many more resources and would take longer.

Incentivizing forecasting via social media

Thanks. :)

Another potential outcome that comes to mind regarding such projects is a self-fulfilling prophecy effect [...]

That's true though this is also an issue for other forecasting platforms—perhaps even more so for prediction markets where you could potentially earn millions by making your prediction come true. From what I can tell, this doesn't seem to be a problem for other forecasting platforms, probably because most forecasted events are very difficult to affect by small groups of individuals. One exception that comes to mind is match fixing.

However, our proposal might be more vulnerable to this problem because there will (ideally) be many more forecasted events, so some of them might be easier to affect by a few individuals wishing to make their forecasts come true.

Incentivizing forecasting via social media

Thanks!

The problems in my view are biggest on the business model and audience demand side.

I agree.

Journalism outlets are possible collaborators but they need the incentive [...]

Yeah, maybe such outlets could receive financial support for their efforts by organizations like OpenPhil or the Rockefeller Foundation—which supported Vox's Future Perfect.

To the extent prediction accuracy correlates with other epistemological skills you could task above average forecasters in the audience with tasks like up- and down-voting content or comments, too.

Interesting idea. More generally, it might be valuable if news outlets adopted more advanced commenting systems, perhaps with Karma and Karma-adjusted voting (e.g., similar to the EA forum). From what I can tell, downvoting isn't even possible on most newspaper websites. However, Karma-adjusted voting and downvotes could also have negative effects, especially if coupled with a less sophisticated user base and less oversight than on the EA forum.

Descriptive Population Ethics and Its Relevance for Cause Prioritization

Thanks! David Moss and others from Rethink Priorities have done some excellent work in this area. 

Lucius Caviola, Geoffrey Goodwin, Andreas Morgensen, and I have been working on an academic paper in this area.

One potential partner for cooperation could be clearerthinking.org

Agree, good idea!

Why I think the EA Community should write more fiction

I mostly agree with Khorton.

A related idea would be to try to convince established authors to write books promoting EA or longtermism (maybe by giving them a grant?). Such authors are already very good writers with a large audience.

I guess the main problem will be the likely lack of high fidelity. Another problem is that this could be seen as a sinister attempt of pushing one's (weird) agenda.

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