It's worth noting that in the case of logical induction, there's a more fleshed-out story where the LI eventually has self-trust and can also come to believe probabilities produced by other LI processes. And, logical induction can come to trust outputs of other processes too. For LI, a "virtuous process" is basically one that satisfies the LI criterion, though of course it wouldn't switch to the new set of beliefs unless they were known products of a longer amount of thought, or had proven themselves superior in some other way.
It's easy to list flaws; for example the first paragraph admits a major flaw; and technically, if trust itself is a big part of what you value, then it could be crucially important to learn to "trust and think at the same time".
Are either of those the flaw he found?
What we have to go on are "fairly inexcusable" and "affects one of the conclusions". I'm not sure how to filter the claims into a set of more than one conclusion, since they circle around an idea which is supposed to be hard to put into words. Here's ...
I think there’s some looseness in the Mind Illuminated ontology around this point, but I would say: thinking involves attention on an abstract concept. When attention and/or awareness are on a thought, that’s metacognitive attention and/or awareness. For example, if I’m trying to work on an intellectual task but start thinking about food, my attention has moved from the task to food. Specifically my attention might be on a specific possibility for dinner, or on a set of possibilities. If I have no metacognitive awareness, then I’m lost in the thought; my attention is not on the thought, it’s on the food.
The definition may not be principled, but there's something that feels a little bit right about it in context. There are various ways to "stay in the logical past" which seem similar in spirit to migueltorrescosta's remark, like calculating your opponent's exact behavior but refusing to look at certain aspects of it. The proposal, it seems, is to iterate already-iterated games by passing more limited information of some sort between the possibly-infinite sessions. (Both your and the opponent's memory gets limited.) But if we admit that Miguel's "iterated p...
I think it's worth mentioning that part of the original appeal of the term (which made us initially wary) was the way it matches intuitively with the experience of signaling behavior. Here's the original motivating example. Imagine that you are in the Parfit's Hitchhiker scenario and Paul Ekman has already noticed that you're lying. What do you do? You try to get a second chance. But it won't be enough to simply re-state that you'll pay him. Even if he doesn't detect the lie this time around, you're the same person who had to lie only a moment ago. What ch...
What does it look like to rotate and then renormalize?
There seem to be two answers. The first answer is that the highest probability event is the one farthest to the right. This event must be the entire . All we do to renormalize is scale until this event is probability 1.
If we rotate until some probabilities are negative, and then renormalize in this way, the negative probabilities stay negative, but rescale.
The second way to renormalize is to choose a separating line, and use its normal vector as probability. This keeps probability positive. Then we fin...
Your assessment makes the assumption that the knowledge that we are missing is "not that important".
Better to call it a rational estimate than an assumption.
It is perfectly rational to say to onesself "but if I refuse to look into anything which takes a lot of effort to get any evidence for, then I will probably miss out." We can put math to that sentiment and use it to help decide how much time to spend investigating unlikely claims. Solutions along these lines are sometimes called "taking the outside view".
To my eyes yo
It doesn't seem quite right to say that the sensor readings are identical when the thief has full knowledge of the diamond. The sensor readings after tampering can be identical. But some sensor readings have caused the predictor to believe that the sensors would be tampered with by the thief. The problem is just that the predictor knows what signs to look for, and humans do not.