TL;DR
NOVAH (No Violence At Home) was incubated by Charity Entrepreneurship (now Ambitious Impact) in 2024 to test a promising idea: preventing intimate partner violence through edutainment, in our case a serialised radio drama. Over the past two years we have produced and aired two seasons in Rwanda.
We are currently evaluating our second season through a randomized controlled trial with 2,400 couples in Rwanda in partnership wi...
TL;DR
* The Long-Term Future Fund is closing down, and EA Funds is launching the Transformative AI Fund with a new full-time team.
* The fund's primary focus is technical AI safety and AI governance (including post-AGI governance), as well as supporting fields such as field-building and forecasting. We'll also consider non-GCR implications of transformative AI such as flourishing futures and digital...
The current Long Term Future Fund (LTFF) fund managers and I have decided to step back from our work on the LTFF. Because we believe LTFF donors trusted the fund managers to ensure that the funds would be used in line with the purposes of their donation, we've decided the right move is to close the fund.
While LTFF is closing, note that EA Funds has launched a new fund...
Not sure I agree, but then again, there's no clear nailed-down target to disagree with :p
For particular people's behaviour in a social environment, there's a high prior that the true explanation is complex. That doesn't nail down which complex story we should update towards, so there's still more probability mass in any individual simpler story than in individual complex stories. But what it does mean is that if someone gives you a complex story, you shouldn't be surprised that the story is complex and therefore reduce your trust in them--at least not by much.
(Actually, I guess sometimes, if someone gives you a simple story, and the prior on complex true stories is really high, you should distrust them more. )
To be clear, if someone has a complex story for why they did what they did, you can penalise that particular story for its complexity, but you should already be expecting whatever story they produce to be complex. In other words, if your prior distribution over how complex their story will be is nearly equal to your posterior distribution (the complexity of their story roughly fits your expectations), then however much you think complexity should update your trust in people, you should already have been distrusting them approximately that much based on your prior. Conservation of expected evidence!