SimonM

Just in case anyone is struggling to find the relevant bits of the the codebase, my best guess is the link for the collections folder in github is now here.

You are looking in "views.ts" eg .../collections/comments/views.ts

The best thing to search for (I found) was ".addView(" and see what fits your requirements

I feel in all these contexts odds are better than log-odds.

Log-odds simplifies Bayesian calculations: so does odds. (The addition becomes multiplication)

Every number is meaningful: every *positive* number is meaningful and the numbers are clearer. I can tell you intuitively what 4:1 or 1:4 means. I can't tell you what -2.4 means quickly, especially if I have to keep specifying a base.

Certainty is infinite: same is true for odds

Negation is the complement and 0 is neutral: Inverse is the complement and 1 is neutral. 1:1 means "I don't know" and 1:x is the inverse of x:1. Both ot these are intuitive to me.

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

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.

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)

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.

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"?)

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 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:

- Libor
- "Chicken Libor"
- Every sport, all the time
- Option expiries (I don't have a good single link for this)

I agree, as I said here