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NunoSempere  6mo ago (forecasting.substack.com)   1177 thread views

The State of Forecasting: Dynamics, Challenges, Hopes

forecasting.substack.com

In short…

  • Forecasting platforms and prediction markets are partially making the pie bigger together, and partially undercutting each other.
  • The forecasting ecosystem adjusted after the loss of plentiful FTX money.
  • Dustin Moskovitz’s foundation (Open Philanthropy) is increasing their presence in the forecasting space, but my sense is that chasing its funding can sometimes be a bad move.
  • As AI systems improve, they become more relevant for judgmental forecasting practice.
  • Betting with real money is still frowned upon by the US powers that be–but the US isn’t willing to institute the oversight regime that would keep people from making bets over the internet in practice.
  • Forecasting hasn’t taken over the world yet, but I’m hoping that as people try out different iterations, someone will find a formula to produce lots of value in a way that scales.
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Thanks for the comment. Some points:

actual large stakes betting

To quantify this, there are some markets in which you can bet >100k, particularly around US elections. Kalshi is also trying to change this in the US. But yeah.

actual financial, governmental and business markets

Different niche, though. One important difference is that in normal markets "the market can stay irrational longer than you can stay solvent". Not so in prediction markets/forecasting questions: there is a definite date.

how would one extract any value from it

Not all goods are rival, not all games are zero sum. E.g., people can and do get value from weather forecasting.

alter the very things it is trying to predict

Sure. You do have fixed point problems. You can also make predictions conditional on a level of investment. It's still a consideration, though.

You can make large stakes bets, but as I said they are handicapped to the point where your odds are less than 50% of winning because of spreads.

Is the market irrational? Or are you?

Fair point there are a lot of positives to be had from certain predictive algorithms.

I don't know what this last one means.

The last one is: I agree that sometimes predictions influence what happens. A few cases people have studied is alarmist Ebola predictions making Ebola spread less because people invested more early on, and optimistic predictions about Hillary Clinton leading to lower turnout.

You can solve these problems in various ways. For the Ebola one, instead of giving one probability, you could give a probability for every "level of effort" to prevent it early on. For the Hillary Clinton one, you could find the fixed point, the probability which takes into account that it lowers turnout a little bit (https://en.wikipedia.org/wiki/Fixed_point_(mathematics)).

Unfortunately, the level of effort option can still lead people to think it'll be fine if they just take the measures, then not get concerned and don't take the measures.

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