I think you are right that there don't have to be collisions (in practice) if the representation space is big enough and has sufficient high dimension. On the other hand there is a metric aspect involved in the way the brain maps its data, which is not present in hash code (as far as I know). This reduces the effective dimension of the brain dramatically and I would guess that it is nowhere near 128 (as in your hash example) for the properties 'good looking', 'honest', etc. It would be an interesting research project to find out.
I think that the cultural aspect you mention might play a significant role. As I wrote in another comment, my goal here was not to give a full explanation of the halo effect... But I don't think that your 'beautiful women are stupid' example undermines the general idea, since for those people 'beauty' doesn't seem like a 'positive' concept and we wouldn't expect it to correlate with intelligence therefore. But I am not defending the 'halo effect' anyway. I chose it as an example to highlight the main idea and I might as well have chosen another bias.
Well, the beauty is positive quality for men who believe prettier women are stupider. One need to be careful not to start redefining positive qualities as those that correlate positively with each other.
So what would be your other example of halo effect? USA tends to elect taller people for presidents, yet I don't think many have trouble with concept that extreme tallness correlates negatively with health. I can't really think of much halo effects, apart from other effects like e.g. if you pick someone based on one quality you rationalize other qualities a...
Introduction
When people on LW want to explain a bias, they often turn to Evolutionary psychology. For example, Lukeprog writes
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.
Brain structure and the halo effect
I want to introduce a simple model, which relates the halo effect to a structural property of the brain. My hope is that this approach will be useful to understand the halo effect more systematically and shows that thinking in evolutionary terms is not always the best way to think about certain biases.
One crucial property of the brain is that it has to map a (essentially infinite) high-dimensional reality onto a finite low-dimensional internal representation. (If you know some Linear Algebra, you can think of this as a projection from a high-dimensional space into a low-dimensional space.) This is done more or less automatically by the limitation of our senses and brain's structure as a neural network.
An immediate consequence of this observation is that there will be many states of the world, which are mapped to an almost identical inner representation. In terms of computational efficiency it makes sense to use overlapping set of neurons with similar activation level to represent similar concepts. (This is also a consequence of how the brain actually builds representations from sense inputs.)
Now compare this to the following passage from here.
This shouldn't be a surprise, since 'positive' ('feels good') seems to be one of the evolutionary hard-wired concepts. Other concepts that we acquire during our life and associate with positive emotions, like kindness and honesty are mapped to 'nearby' neural structures. When one of those mental structures is activated, the 'closed ones' will be activated to a certain degree as well.
Since we differentiate concepts more when we are learning about a subject, the above reasoning should imply that children and people with less education in a certain area should be more influenced by this (generalized) halo effect in that area.
Conclusion
Since evolution can only modify the existing brain structure but cannot get away from the neural network 'design', the halo effect is a necessary by-product of human thinking. But the degree of 'throwing things in one pot' will depend on how much we learn about those things and increase our representation dimensionality.
My hope is that we can relief evolution from the burden of having to explain so many things and focus more on structural explanations, which provide a working model for possible applications and a better understanding.
PS: I am always grateful for feedback!