But collectively we are all better off if everyone stops holding protests for now.
Who is the 'we' here and by whose yardstick the benefit measured?
Animal rights activists are not turning out in large numbers to get tear gassed and beaten for the cause. This is pretty good evidence that they are not in the set of 'everyone else who thinks their reason is as good as I think this one is'.
As usual, there are better alternatives being neglected here. Those who want more lockdown have, in this situation, two options to get it: more violence or more concessions.
Negotiation is certainly possible. So, a consequentialist might lay additional covid deaths at the step of a government which failed to negotiate.
Add to this the obvious virtue of the demand to end police brutality and recognize that black lives matter. That being an option now, it seems particularly bizarre, and wrong, to delay granting the wish.
Now that is a big philosophical question.
One answer is that there is no difference between 'orders' of random variables in Bayesian statistics. You've either observed something or you haven't. If you haven't, then you figure out what distribution the variable has.
The relationship between that distribution and the real world is a matter of your assiduousness to scientific method in constructing the model.
Lack of a reported distribution on a probability, e.g. p=0.42, isn't the same as a lack of one. It could be taken as the assertion that the distribution on the probability is a delta function at 0.42. Which is to say the reporter is claiming to be perfectly certain what the probability is.
There is no end to how meta we could go, but the utility of going one order up here is to see that it can actually flip our preferences.
One of the topics I hope to return to here is the importance of histograms. They're not a universal solvent. However they are easily accessible without background knowledge. And as a summary of results, they require fewer parametric assumptions.
I very much agree about the reporting of means and standard deviations, and how much a paper can sweep under the rug by that method.
Nice example, I see where you're going with that.
I share the intuition that the second case would be easier to get people motivated for, as it represents more of a confirmed loss.
However, as your example shows actually the first case could lead to an 'in it together' effect on co-ordination. Assuming the information is taken seriously. Which is hard as, in advance, this kind of situation could encourage a 'roll the dice' mentality.
I also think it would be a lot more helpful to walk through how this mistake could happen in some real scenarios in the context of EA
Hopefully, we'll get there! It'll be mostly Bayesian though :)
Thanks - that last link was one I'd come across and liked when looking for previous coverage. My sole previous blog post was about Pascal's Wager. I'd found though when speaking about it that I was assuming too much for some of the audience I wanted to bring along; notwithstanding my sloppy writing :D So, I'm going to attempt to stay focused and incremental.
As long as the core focuses on unusual priorities – which using neglectedness as a heuristic for prioritization will mean is likely – there’s a risk that new members get surprised when they find out about these unusual priorities
Perhaps there are also some good reasons that people with different life experience both a) don't make it to 'core' and b) prioritize more near term issues.
There's an assumption here that weirdness alone is off-putting. But, for example, technologists are used to seeing weird startup ideas and considering the contents.
This suggests a next thing to find out is: who disengages and why.
TL;DR's for the EA Forum/Welcome: ”Effective altruists are trying to figure out how to build a more effective AI, using paperclips, but we're not really sure how it's possible to do so.
Perhaps EA's roots in philosophy lead it more readily to this failure mode?
Take the diminishing marginal returns framework above. Total benefit is not likely to be a function of a single variable 'invested resources'. If we break 'invested resources' out into constituent parts we'll hit the buffers OP identifies.
Breaking into constituent parts would mean envisaging the scenario in which the intervention was effective and adding up the concrete things one spent money on to get there: does it need new PhDs minted? There's a related operational analysis about time lines: how many years for the message to sink in?
Also, for concrete functions, it is entirely possible that the sigmoid curve is almost flat up to an extraordinarily large total investment (and regardless of any subsequent heights it may reach). This is related to why ReLU functions are popular in neural networks: because zero gradients prevent learning.