What are the top frictions preventing wider adoption of forecasting best practices?

2kṀ15k

resolved Jun 14

11%11%

Good questions require a lot of back and forth

10%10%

Hard to put as part of an org

10%10%

People don’t know about forecasting

8%8%

The average person does not think in probabilities

8%8%

People don’t want to share private information

7%7%

Lack of awareness

7%7%

not enough liquidity/dumb money to make it worthwhile for good forecasters

5%5%Other

5%5%

Existing solutions have poor UX

5%5%

Forecasts are hard to interpret without rationales

5%5%

Whalebait

5%5%

Sub-optimal market design and/or resolution criteria

3%3%

Questions aren’t relevant enough to what I care about

2%2%

Conditional forecasts are more useful but less frequent

2%2%

Forecasting disrupts existing hierarchies

2%1.6%

People don’t think forecasting is accurate

1%1.1%

There are alternatives with higher ROI

1%1.1%

Making good forecasts is hard

1%1.1%

People don’t know how forecasts should affect their decisions

1%1.0%

Operationalizing questions is hard

Imagine 2 different worlds:

We reduce all the frictions listed here by p%
We reduce friction X by p%

The price of X should be the ratio of adoption in world (b) / adoption in world (a).

Resolution

This is a self-resolving market. I’ll resolve all options to PROB at some random time the week after Manifest.

NB: This market is part of a live session at Manifest 2024. If you are/were not at the session, you’re probably missing some important context. I’ll link to the session video once it’s up, but it’s likely I’ll have resolved the market by then.

Meta-Forecasting

Manifest 2024

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