I think you’re advocating two things here:
Make a continuous forecast when forecasting a continuous variable
Use a normal distribution to approximate your continuous forecast
I think that 1. is an excellent tip in general for modelling. Here is Andrew Gelman making the same point
However, I don’t think it’s actually always good advice when eliciting forecasts. For example, fairly often people ask whether or not they should make a question on Metaculus continuous or binary. Almost always my answer is “make it binary”. Binary questions get considerable more interest and are much easier to reason about. The additional value of having a more general estimate is almost always offset by:
Fewer predictors ⇒ less valuable forecast
People update less frequently ⇒ Stale forecast
Harder to visualize changes over time ⇒ Less engagement from the general public
I think your point 2. has been well dealt with by gbear605, but let me add my voice to his. Normal approximations are probably especially bad for lots of things we forecast. Metaculus uses a logistic distribution by default because it automatically includes slightly heavier tails than normal distributions.
I feel this is more “limits of PredictIt” rather than “limits of prediction markets”. Doing the equivalent analysis for Betfair for someone in the UK. (2% charge on profits, no fee for withdrawing).
Right now the market for Trump to win is (£1200) 2.78 / 2.80 (£7421)
Those imply probabilities of 35.97% and 35.71%
After fees, 36.44% and 35.00%.
That’s not a bad spread, and you can do this in much more size. (If you’re willing to work a bit, you can comfortably get on £10,000s at prevailing levels).
We pay no taxes on gambling, so there’s none of Section III to worry about.
(Admittedly, once we start talking about even more serious size we run into Betfair’s Premium Charge, but for your average UK based rationalist, I don’t think Betfair is as bad a prediction market as you’re making out).