If the brain avoided collisions in the way you describe, it would utterly fail at its function. The brain must be able to access the information it has about similar situations to make judgments and decisions about the current one. Looking up that information must make use of some data in common between the current situation and whatever representation the brain has of other similar situations, or there would be no way to locate or identify that information.
So at the description level of "the brain is a computing device", this seems plausible, but considering what the brain actually does, I don't see how it could work. It could use a hybrid of hash functions and structural similarities at different levels, and maybe it does. But the fact that we can confuse two different people who have some attributes in common, or even whose names are similar but not the same, seems like evidence against that to me.
The point is not that it necessarily happens, the point is that if the larger space is mapped to a smaller space, that's by itself doesn't mean there will be [unwanted] collisions. The very same software could do lower-case string matching which 'confuses' lower and upper case, using the hashes.
The collisions between multiple good qualities - well that does not even happen for every person on the earth in the way that is outlined in the article - there definitely are people who think that e.g. pretty people must be stupid, which is btw more wrong than pret...
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!