Phenotype screens off genotype, as the genetic saying goes. Infection reduces Openness? Well, do you have some reason to want to increase your Openness? (Are you also looking into acquiring some psilocybin, which might actually be cheaper than treatment if Morendil is generalizable about it taking 'months' ?)

Slower reactions - would that significantly improve your life? I'm a cat person so I may be infected, but I can't say I often think (outside of taekwondo) 'I wish I had 10% faster reflexes!'

The greater jealousy thing may be an issue.

Not 13 out of every 100 people in jail, but still 13 out of every 100 people sentenced by the jury as guilty in the case of a probability estimate of only ~87%. ....The argument still works to show that the probability of guilt at 87% is too low to vote guilty.

Hopefully normalizing them so they sum to 1 again and using them to draw an outcome :)

I assume the intent was to say that in normal games, if the goal is to choose the "smartest" action (highest EV, or whatever the objective fn is) under uncertainty, and the player makes the optimal choice, they should always on average be noticeably higher rewarded (not just slightly more rewarded).

It's fine (maybe more addictive?) for right decisions to sometimes not result in a win, so long as there enough chances for a masterful player to recover from bad luck.

The argument is that simple numbers like 3^^^3 should be considered much more likely than random numbers with a similar size, since they have short descriptions and so the mechanisms by which that many people (or whatever) hang in the balance are less complex.

Consider the options A = "a proposed action affects 3^^^3 people" and B = "the number 3^^^3 was made up to make a point". Given my knowledge about the mechanisms that affect people in the real world and about the mechanisms people use to make points in arguments, I would say that the likelihood of A versus B is hugely in favor of B. This is because the relevant probabilities for calculating the likelihood scale (for large values and up to a first order approximation) with the size of the number in question for option A and the complexity of the number for option B. I didn't read de Blanc's paper further than the abstract, but from that and your description of the paper it seems that its setting is far more abstract and uninformative than the setting of Pascal's mugging, in which we also have the background knowledge of our usual life experience.

One of the later scripts talks about him inventing that android-body type machine, or at least helping develop it.

But they've already been invented.

What would have come up with them instead?

I don't understand what point you're making; could you expand?

We have ... preferences in terms of "our next experience." If we become convinced by the data that this model of a unique thread of experience is false, we then have problems in translating preferences defined in terms of that false model.

In what sense would I want to translate these preferences? Why wouldn't I just discard the preferences, and use the mind that came up with them to generate entirely new preferences in the light of its new, improved world-model? If I'm asking myself, as if for the first time, the question, "if there are going to be a lot of me-like things, how many me-like things with how good lives would be how valuable?", then the answer my brain gives is that it wants to use empathy and population ethics-type reasoning to answer that question, and that it feels no need to ever refer to "unique next experience" thinking. Is it making a mistake?

I'm also interested in repurposing machine learning algorithms used for finding plausible hypotheses about data distributions into algorithms for finding action policies with high expected utility.

While I'm not in a position to hire you, I think that this is an extremely important problem. In the case where the utility function is known, I think there is lots of low-hanging fruit that will lead to progress in more "physical" application areas of machine learning like computer vision and robotics. In the case where the utility function is unknown, I think the problem is harder (at the level say of a PhD thesis), but would be a crucial step towards making progress on FAI.

If you're interested in talking to me about either of these then I'd be happy to, assuming you have enough of a statistical background for me to get my thoughts across without too much of an explanatory burden. Assuming you haven't already decided on a specific set of algorithms, I have some ideas here that I don't currently have time to pursue myself that I think could lead to a publication in a good machine learning journal if done well.

I've moved in the direction of predicting AGI soonish (5-20 years)

This timeline is much sooner than I would predict. Could you perhaps point me to a few sources that you think would cause me to update my estimate towards yours?