Unnamed

Comments

Covid 3/4: Declare Victory and Leave Home

It appears Operation Warp Speed had to be funded by raiding other sources because Congress couldn’t be bothered to fund it. As MR points out, this is a scandal because it was necessary, rather than because it was done. It’s scary, because it implies that under a different administration Operation Warp Speed could easily have not happened at all.

There are gaps in the reporting on Operation Warp Speed funding, because apparently a bunch of the money that Congress did allocate for vaccines hasn't been spent yet. I don't understand why the White House spent other money but not that money.

Avoid Contentious Terms

There are advantages to this style of writing even when the general term isn't contentious.

These kinds of concrete descriptions encourage readers to look at the world and see what's there, rather than engaging primarily with you and your concepts.

This can be good for people who know less about the topic, since looking at the world has fewer prerequisites. And it can be good for people who know more about the topic, since they can gain texture and depth by looking at new examples.

Though with non-contentious topics it's easier to add a general term at the end as a label to remember, or to tie the post into a larger conversation, without overshadowing the rest of the post.

Incentive Problems With Current Forecasting Competitions.

The full-blown process of in-depth contract negotiations, etc., is presumably beyond the scope of the current competitive forecasting arena. 

One of the main things that I get out of the sports comparison is that it points to a different way of using (and thinking of) metrics. The obvious default, with forecasting, is to think of metrics as possible scoring rules, where the person with the highest score wins the prize (or appears first on the leaderboard). In that case, it's very important to pick a good metric, which provides good incentives. An alternative is to treat human judgment as primary, whether that means a committee using its judgment to pick which forecasters win prizes, or forecasters voting on an all-star team, or an employer trying to decide who to hire to do some forecasting for them, or just who has street cred in the forecasting community. And metrics are a way to try to help those people be more informed about forecasters' abilities & performance, so that they'll make better judgment. In that case, the standards for what is a good metric to include are very different. (There's also a third use case for metrics, where the forecaster uses metrics about their own performance to try to get better at forecasting.)

Sports also provide an example of what this looks like in action, what sorts of stats exist, how they're presented, who came up with them, what sort of work went into creating them, how they evaluate different stats and decide which ones to emphasize, etc.  And it seems plausible that similar work could be done with forecasting, since much of that work was done by sports fans who are nerds rather than by the teams; forecasting has fewer fans but a higher nerd density. I did some brainstorming in another comment on some potential forecasting stats which draws a lot of inspiration from that; not sure how much of it is retreading familiar ground.

Incentive Problems With Current Forecasting Competitions.

Here' s a brainstorm of some possible forecasting metrics which might go in those tables (probably I'm reinventing some wheels here; I know more about existing metrics for sports than for forecasting):

  • Leading Indicator: get credit for making predictions if the consensus then moves in the same direction over the next hours / days / n predictions (alternate version: only if that movement winds up being towards the true outcome)
  • Points Relative to Your Expectation: each forecast has an expected score according to that forecast (e.g., if the consensus is 60% and you say 80%, you think there's a 0.8 chance you'll gain points for doing better than the consensus and a 0.2 chance you'll lose points for doing worse than consensus). Report expected score alongside actual score, or report the ratio actual/expected. If that ratio is > 1, that means you've been underconfident or (more likely) lucky. Also, expected score is similar to "total number of forecasts", weighted by boldness of forecasts. You could also have a column for the consensus expected score (in the example: your expected score if there was only a 0.6 chance you'd gain points and a 0.4 chance you'd lose points).
  • Marginal Contribution to Collective Forecast: have some way of calculating the overall collective forecast on each question (which could be just a simple average, or could involve fancier stuff to try to make it more accurate including putting more weight on some people's forecasts than others). Also calculate what the overall collective forecast would have been if you'd been absent from that question. You get credit for the size of the difference between those two numbers. (Alternative versions: you only get credit if you moved the collective forecast in the right direction, or you get negative credit if you moved it in the wrong direction.)
  • Trailblazer Score: use whichever forecasting accuracy metric (e.g. brier score relative to consensus) while only including cases where a person's forecast differed from the consensus at the time by at least X amount. Relevant in part because there might be different skillsets to noticing that the consensus seems off and adjusting a bit in the right direction vs. coming up with your own forecast and trusting it even if it's not close to consensus. (And the latter skillset might be relevant if you're making forecasts on your own without the benefit of having a platform consensus to start from.)
  • Market Mover: find some way to track which comments lead to people changing their forecasts. Credit those commenters based on how much they moved the market. (alternative version: only if it moved towards truth)
  • Pseudoprofit: find some way to transform people's predictions into hypothetical bets against each other (or against the house), track each person's total profit & total amount "bet". (I'm not sure if this to different calculations or if it's just a different gloss on the same calculations.)
  • Splits: tag each question, and each forecast, with various features. Tags by topic (coronavirus, elections, technology, etc.), by what sort of event it's about (e.g. will people accomplish a thing they're trying to do), by amount of activity on the question, by time till event (short term vs. medium term vs. long term markets), by whether the question is binary or continuous, by whether the forecast was placed early vs. middle vs. late in the duration of the question, etc. Be able to show each scoring table only for the subset of forecasts that fit a particular tag. 
  • Predicted Future Rating: On any metric, you can set up formulas to predict what people will score on that metric over the next (period of time / set of markets). A simple way to do that is to just predict future scores on that metric based on past scores on the same metric, with some regression towards the mean, using historical data to estimate the relationship. But there are also more complicated things using past performance on some metrics (especially less noisy ones) to help predict future performance on other metrics. And also analyses to check whether patterns in past data are mostly signal or noise (e.g. if a person appears to have improved over time, or if they have interesting splits). (Finding a way to predict future scores is a good way to come up with a comprehensive metric, since it involves finding an underlying skill from among the noise. And the analyses can also provide information about how important different metrics are, which ones to include in the big table, which ones to make more prominent.)
Covid 2/11: As Expected

The thing that I was more surprised by, looking at the scoring system, is that Metaculus is set up as a platform for maintaining a forecast rather than as a place where you make a forecast at a particular time. (If I'm understanding the scoring correctly.) 

Metaculus scores your current forecast at each moment, from the moment you first enter a forecast on the question until the moment the question closes. Where "your current forecast" at each moment is the most recent number that you entered, and the only thing that happens when you enter an updated prediction is that for the rest of the moments (until you update it again) "your current forecast" will be a different number. Every moment gets equal weight regardless of whether you last entered a number just now or three weeks ago (except that the very last moment when the question closes gets extra weight).

So it's not like a literal betting market where you're buying at the current market price at the moment that you make your forecast. If you don't keep updating your forecast, then you-at-that-moment is going up against the future consensus forecast.

So the scoring system rewards the activity of entering more questions, and also the activity of updating your forecasts on each of those questions again and again to keep them up-to-date.

2019 Review: Voting Results!

And neither of you voted for it!

Load More