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Is Metaculus Slow to Update?

No - I think probability is the thing supposed to be a martingale, but I might be being dumb here.

Thoughts on the SPIES Forecasting Method?

So, what do you think? Does this method seem at all promising? I'm debating with myself whether I should begin using SPIES on Metaculus or elsewhere.

I'm not super impressed tbh. I don't see "give a 90% confidence interval for x" as a question which comes up frequently? (At least in the context of eliciting forecasts and estimates from humans - it comes up quite a bit in data analysis).

For example, I don't really understand how you'd use it as a method on Metaculus. Metaculus has 2 question types - binary and continuous. For binary you have to give the probability an event happens - not sure how you'd use SPIES to help here. For continuous you are effectively doing the first step of SPIES - specifying the full distribution.

If I was to make a positive case for this, it would be - forcing people to give a full distribution results in better forecasts for sub-intervals. This seems an interesting (and plausible claim) but I don't find anything beyond that insight especially valuable.

2022 ACX predictions: market prices

17. Unemployment below five percent in December: 73 (Kalshi said 92% that unemployment never goes above 6%; 49 from Manifold)


I'm not sure exactly how you're converting 92% unemployment < 6% to < 5%, but I'm not entirely convinced by your methodology?

15. The Fed ends up doing more than its currently forecast three interest rate hikes: None (couldn't find any markets)

Looking at the SOFR Dec-22 3M futures 99.25/99.125 put spread on the 14-Feb, I put this probability at ~84%. 


Thanks for doing this, I started doing it before I saw your competition and then decided against since it would have made cheating too easy. (Also why I didn't enter)  

Capturing Uncertainty in Prediction Markets

And one way to accomplish that would be to bet on what percentage of bets are on "uncertainty" vs. a prediction.

How do you plan on incentivising people to bet on "uncertainty"? All the ways I can think of lead to people either gaming the index, or turning uncertainty into a KBC.

Capturing Uncertainty in Prediction Markets

The market and most of the indicators you mentioned would be dominated by the 60 that placed large bets

I disagree with this. Volatility, liquidity, # predictors, spread of forecasts will all be affected by the fact that 20 people aren't willing to get involved. I'm not sure what information you think is being lost by people stepping away? (I guess the difference between "the market is wrong" and "the market is uninteresting"?)

Capturing Uncertainty in Prediction Markets

There are a bunch of different metrics which you could look at on a prediction market / prediction platform to gauge how "uncertain" the forecast is:

  • Volatility - if the forecast is moving around quite a bit, there are two reasons:
    • Lots of new information arriving and people updating efficiently
    • There is little conviction around "fair value" so traders can move the price with little capital
       
  • Liquidity - if the market is 49.9 / 50.1 in millions of dollars, then you can be fairly confident that 50% is the "right" price. If the market is 40 / 60 with $1 on the bid and $0.50 on the offer, I probably wouldn't be confident the probability lies between 40 and 60, let along "50% is the right number". (The equivalent on prediction platforms might be number of forecasters, although CharlesD has done some research on this which suggests there's little addition value being added by large numbers of forecasters)
     
  • "Spread of forecasts" - on Metaculus (for example) you can see a distribution of people's forecasts. If everyone is tightly clustered around 50% that (usually) gives me more confidence that 50% is the right number than if they are widely spread out
Prediction Markets are for Outcomes Beyond Our Control

Prediction markets function best when liquidity is high, but they break completely if the liquidity exceeds the price of influencing the outcome. Prediction markets function only in situations where outcomes are expensive to influence.

 

There are a ton of fun examples of this failing:

Money-generating environments vs. wealth-building environments (or "my thoughts on the stock market")

I don't know enough about how equities trade during earnings, but I do know a little about how some other products trade during data releases and while people are speaking.

In general, the vast, vast, vast majority of liquidity is withdrawn from the market before the release. There will be a few stale orders people have left by accident + a few orders left in at levels deemed ridiculously unlikely. As soon as the data is released, the fastest players will general send quotes making a (fairly wide market) around their estimate of the fair price. Over time (and here we're still talking very fast) more players will come in, firming up that new market.

The absolute level of money which is being made during this period is relatively small. It's not like the first person to see the report gets to trade at the old price, they get to trade with any stale orders - the market just reprices with very little trading volume.

All of the money-making value was redeemed before people like you and me even had a chance to trade. Right?

Correct, you absolutely did not have the chance to be involved in this trade unless you work at one of a handful of firms which have spent 9 figure sums on doing this really, really well.

Use Normal Predictions

I agree identifying model failure is something people can be good at (although I find people often forget to consider it). Pricing it they are usually pretty bad at.

Use Normal Predictions

I'd personally be more interested in asking someone for their 95% CI than their 68% CI, if I had to ask them for exactly one of the two. (Although it might again depend on what exactly I plain to do with this estimate.)

I'm usually much more interested in a 68% CI (or a 50% CI) than a 95% CI because:

  1. People in general arent super calibrated, especially at the tails
  2. You won't find out for a while how good their intervals are anyway
  3. What happens most often is usually the main interest. (Although in some scenarios the tails are all that matters, so again, depends on context - emphasis usually). I would like people to normalise narrower confidence intervals more.
  4. (as you note) the tails are often dominated by model failure, so you're asking a question less about their forecast, and more about their estimate of model failure. I want information about their model of the world rather than their beliefs about where their beliefs breakdown.
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