1) If you were certain about your source code, i.e. if you knew your source code, uploading your mind should be immediately feasible, subject to resource constraints. Since you do not know how would go about immediately uploading your mind, you aren't certain about your source code. Because the answer is binary (tertium non datur), it follows you're uncertain about your own source code. (No, I don't count vague constraints such as "I know it's Turing computable" as "certainty about my own source code", just as you wouldn't say you know a program's source code just because you know it's implemented on a JVM.)

2) The uncertainty falls in several categories, because there are many ways to partition "uncertainty". For example, the uncertainty is mostly epistemic (lack of knowledge of the exact parameters), rather than aleatoric. Using a different partitioning, the uncertainty is structural (we don't know how to correctly model your source code). There are many more true attributes of the relevant uncertainty.

3) I don't understand the question. Handle to what end?

3) It seems unlikely that subjective bayesian probability would work for this kind of uncertainty. In particular, I would expect the correct theory to violate Cox's assumption of consistency. To illustrate, we can normally calculate P(A,B|X) by either P(A|X)P(B|A,X) or P(B|X)P(A|B,X). But what if A is the proposition that we calculate the probability P(A,B|X) by using P(A|X)*P(B|A,X)? Then we will get different answers depending on how we do the calculation.

1) Yes, presumably; your brain is a vast store of (evolved)(wetware)(non-serial)(ad-hoc)(etc.)algorithms that has so far been difficult for neuroscientists to document.

2) Just plain empirical? There's nothing stopping you from learning your own source code, in principle, it's just that we don't AFAIK have scanners that can view "many" nearby neurons, in real time, individually (as opposed an fMRI).

3) Well that's much more difficult. Not sure why Mark_Friedenbach's comment was downvoted though, except maybe snarkiness; heuristics and biases is a small step towards understanding some of the algorithms you are (and correcting for their systematic errors in a principled way).

I think this is a distinct type of uncertainty which I call "introspective". It is closely related to the expectation value over T in the definition of the updateless intelligence metric.

2) The most obvious obstacle for a human is, that I don't have the power to precisely observe and remember everything that I do, and I absolutely don't have an ability to reason about which specific source code could cause me to do exactly those things. Even the information that in theory is there, I can't process it. I guess this is logical uncertainty. It's like being unable to calculate the millionth digit of pi, especially if I couldn't even count to ten correctly.

But even if I had the super-ability to correctly determine which kinds of source code could produce my behavior and which couldn't, there would still be multiple solutions. I could limit the set of possible source codes to a subset, but I couldn't limit it to exactly one source code. Not even to a group of behaviorally identical source codes, because there are always realistic situations that I have never experienced, and some of the remaining source codes could do different things there. So within the remaining set, this seems like indexical uncertainty. I could be any of them, meaning that different copies of "me" in different possible worlds could have different algorithms within this set, and so far the same experience.

There is a problem with the second part -- if I have an information about maximum possible size of my source code, it means there are only finitely many options, so I could hypothetically gradually reduce it to exactly one, which means removing the indexical uncertainty. On the other hand, this would work for "normal" scenarios, but not for the "brain in the jar" scenarios: if I am in a Matrix, my assumption that my human source code is limited by the size of my body could be wrong.

Interesting!

I would say that you (as a real human in the present time) are uncertain about your source code in the traditional sense of the word "uncertain". Once we have brain scans and ems and such, if you get scanned and have access to the scan, you're probably uncertain in something more like a logical uncertainty sense: you have access, and the ability to answer some questions, but you don't "know" everything that is implied by that knowledge.

Indexical uncertainty can apply to a perfect Bayesian reasoner. (Right? I mean, given that those can't exist in the real world,...) So it doesn't feel like it's indexical.

Does it make sense to talk about a "computationally-limited but otherwise perfect Bayesian reasoner"? Because that reasoner can exhibit logical uncertainty, but I don't think it exhibits source code uncertainty in the sense that you do, namely that you have trouble predicting your own future actions or running yourself in simulation.