We built Ergo (a Python library for integrating model-based and judgmental forecasting) as part of our work on forecasting. In the course of this work we realized that for many forecasting questions the bottleneck isn’t forecasting infrastructure per se, but the high-quality research and reasoning that goes into creating good forecasts, so we decided to focus on that aspect.
I’m still excited about Ergo-like projects (including Squiggle!). Developing it further would be a valuable contribution to epistemic infrastructure. Ergo is an MIT-licensed open-source project so you can basically do whatever you want with it. As a small team we have to focus on our core project, but if there are signs of life from an Ergo successor (5+ regular users, say) I’d be happy to talk for a few hours about what we learned from Ergo.
Speaker here. I haven’t reviewed this transcript yet, but shortly after the talk I wrote up these notes (slides + annotations) which I probably endorse more than what I said at the time.