> To rise above the simple wisdom of the crowds, you would want to identify the subset of market participants that are constantly beating the market (because they have a more accurate mental model of the world).
Identifying, and then working with, the top traders at Google (including one card-carrying superforecaster) was a great joy.
And yes, they're sitting on some great data, on what the employee crowd tends to get right and wrong, who individually is good at forecasting what. Though one complication is that being a great trader is not the same as being a great forecaster.
My personal blog is defunct (for now!). But some of my recent writings can be found on the research page [1] of my startup, FutureSearch. We're building an AI that can forecast accurately.
We've written some pieces on topics like the problems with using crowds to forecast, and contesting recent papers' claims of good forecasts coming from simple LLMs.
There are some interesting jupyter to blog tools like quarto.org. Or, my Svelte based blogging tool: svekyll.com (I use it to blog about AI/ML because Svelte is the best visualization front end tool).
It's a great time for you to start blogging again!
> To rise above the simple wisdom of the crowds, you would want to identify the subset of market participants that are constantly beating the market (because they have a more accurate mental model of the world).
Identifying, and then working with, the top traders at Google (including one card-carrying superforecaster) was a great joy.
And yes, they're sitting on some great data, on what the employee crowd tends to get right and wrong, who individually is good at forecasting what. Though one complication is that being a great trader is not the same as being a great forecaster.