There's a certain issue.

A revision control system I use (GIT) uses 128 bit hashes to identify a much longer piece of program code. There are very many pieces of program code that correspond to same hash.

I don't expect it to ever encounter collision, though. Just because something maps large space to a smaller space, doesn't mean any collisions will actually happen. Even a couple hundred bits is enough to define space so vast, that you can map anything you encounter in your life to it, and never see a collision. Not that our brains necessarily work like this. But they can in principle avoid collisions.

For the halo effect you're speaking of, it is the case that positive qualities weakly correlate - at least the good looks and intelligence do, then the intelligence generally correlates with niceness in so much as intelligence prevents grossly un-nice behaviour that hurts everyone including that person. Still could usually be a fallacy, of course, some sort of signal leakage between 'good looks' and 'good somethingelse'.

I'd say, people just tend to assume by default there's some correlation between two things - either they assume it is positive (so pretty, must be nice), or they assume it is negative (so pretty, must be evil or spoiled or the like), and just a few people assume it is zero by default.

I think that ''evolved faulty thinking processes'' is the wrong way to look at it and I will argue that some biases are the consequence of structural properties of the brain, which 'cannot' be affected by evolution.

The structure can be affected by evolution, it's just too hard (takes too many coordinated mutations) to get to a structure that actually works better. I think you recognize this by your use of scare quotes, but you would be better off stating it explicitly. This is the flip side of the arguments I think you're alluding to, that the faulty thinking was actually beneficial in the EEA.

There must be an evolutionary explanation for the properties of the brain, but that doesn't mean we need to actually figure out that evolutionary explanation to understand the current behavior. Just like there must be an explanation in terms of physics, but trying to analyze every particle will clearly get us nowhere.

In fact, if you can find an explanation of a phenomenon in terms of current brain structure, I think that screens off evolutionary explanations as mere history (as long as you've really verified that the structure exists and explains the phenomenon).

I do think we're getting sidetracked by your halo effect example, though -- it might be useful to give three or four examples to avoid this (although if each one has a different explanation, that might substantially increase the effort of presenting your idea).

I think that formulating this in terms of linear algebra is not always as illuminating as explaining it in terms of structure.

The way neural nets work, related concepts get wired together, and therefore cross-activate each other. To re-use your example, because we often activate various positive things alongside the more general notion of positiveness, you'd expect some coupling even between unrelated positive concepts.

This is perhaps an example of when understanding a formal cause, in this case logical truths about certain machine learning architectures, is more enlightening than understanding an efficient cause, in this case contingent facts about evolutionary dynamics. It is generally the case that formal-causal explanations are more enlightening than efficient-causal explanations, but efficient-causal explanations are generally easier to discover, which is why the sciences are so specialized for understanding efficient causes. There are sometimes trends towards a more form-oriented approach, e.g. cybernetics, complexity sciences, aspects of evo-devo, and so on, but they're always on the edge of what is possible with traditional scientific methods and thus their particular findings are unfortunately often afflicted with an aura of unrigor.

Of note is that the only difference between "causal" decision theory and "timeless" decision theory is that the latter's description emphasizes the taking-into-account of formal causes, which is only implicit in any technically-well-founded causal decision theory and is for some unfathomable reason completely ignored by academic decision theorists. (If you get down to the level of an actually formalized decision theory then you're working with Markovian causality, where as far as I can discern CDT and TDT are no different.)