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The timing evidence is thus hostile evidence and updating on it correctly requires superintelligence.

What do you mean by this? It seems trivially false that updating on hostile evidence requires superintelligence; for example poker players will still use their opponent's bets as evidence about their cards, even though these bets are frequently trying to mislead them in some way.

The evidence being from someone who went against the collective desire does mean that confidently taking it at face value is incorrect, but not that we can't update on it.

The LW staff are necessary to take down the site. If we assume that there are multiple users that are willing to press the button, then the (shapely-attributed) blame for taking the site down mostly falls on the LW staff, rather than whoever happens to press the button first.

According to http://shapleyvalue.com/?example=8 if there were 6 people who were willing to push the button, the LW team would deserve 85% of the blame. (Here I am considering the people who take actions that act to facilitate bringing down the site as part of the coalition.)

I am not quite sure how to take into account all the people who choose not to take down the website and thus delay, and there is some value in running the Petrov day event, so the above does not take everything into account.

Tweaking some values in the website to model this, where value = 7 if either LW and/or all the other users refuse to shut down the site, and 7-i where i is the highest numbered player that shuts down the site (higher meaning they shut things down sooner), I get these values:

The Shapley value of player 1(Low Karma button pusher) is: -0.023809523809524
The Shapley value of player 2 is: -0.057142857142857
The Shapley value of player 3 is: -0.10714285714286
The Shapley value of player 4 is: -0.19047619047619
The Shapley value of player 5 is: -0.35714285714286
The Shapley value of player 6(High karma button pusher) is: -0.85714285714286
The Shapley value of player 7(LW team) is: -4.4071428571429

(All the values are negative, since this assigns no value to running the experiment or to keeping the site online despite running the experiment and for simplicity's sake measures things in site uptime, and not shutting down the site achieves that.)

Here is an example of something that comes close from "The Selfish Gene":

One of the best-known segregation distorters is the so-called t gene in mice. When a mouse has two t genes it either dies young or is sterile, t is therefore said to be lethal in the homozygous state. If a male mouse has only one t gene it will be a normal, healthy mouse except in one remarkable respect. If you examine such a male's sperms you will find that up to 95 per cent of them contain the t gene, only 5 per cent the normal allele. This is obviously a gross distortion of the 50 per cent ratio that we expect. Whenever, in a wild population, a t allele happens to arise by mutation, it immediately spreads like a brushfire. How could it not, when it has such a huge unfair advantage in the meiotic lottery? It spreads so fast that, pretty soon, large numbers of individuals in the population inherit the t gene in double dose (that is, from both their parents). These individuals die or are sterile, and before long the whole local population is likely to be driven extinct. There is some evidence that wild populations of mice have, in the past, gone extinct through epidemics of t genes.

Not all segregation distorters have such destructive side-effects as t. Nevertheless, most of them have at least some adverse consequences.

From the discussion of human-engineered gene drives, they would only cause sterility in one sex, which would help avoid the gene dying off as quickly.

I had not thought of self-play as a form of recursive self-improvement, but now that you point it out, it seems like a great fit. Thank you.

I had been assuming (without articulating the assumption) that any recursive self improvement would be improving things at an architectural level, and rather complex (I had pondered improvement of modular components, but the idea was still to improve the whole model). After your example, this assumption seems obviously incorrect.

Alpha-go was improving its training environment, but not any other part of the training process.

The left hand side of the example is deliberately making the mistake described in your article, as a way to build intuition on why it is a mistake. 

(Adding instead of averaging in the update summaries was an unintended mistake)

Thanks for explaining how to summarize updates, it took me a bit to see why averaging works.

Seeing the equations, it was hard to intuitively grasp why updates work this way. This example made things more intuitive for me:

If an event can have 3 outcomes, and we encounter strong evidence against outcomes B and C, then the update looks like this:

The information about what hypotheses are in the running is important, and pooling the updates can make the evidence look much weaker than it is.

I found the postmortem over-focuses on what went wrong or was sub-optimal. I would like to point out that I found the event fun, despite being a lurker with no code.

There were some reports of people seeing a frozen countdown on the button, that disappeared when the page was refreshed. Was this an intentional false alarm? I had assumed that was the case, as a false alarm with some evidence that it was false echoes some parts of Petrov's situation nicely.

I had not noticed my own Gel-Mann amnesia when reading that bit, and therefore find your response quite convincing. I had thought that Ziv's answer to (D) made sense due to the FDA being over-cautious about approving things, but both the scope of the precedent and the kinds/directions of errors had not registered with me.

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