On bottom-up/top-down development of concepts

For many years now, beginning with some questions that were part of my doctoral dissertation research, I have been curious about multi-level models that describe phenomena and strategies. A fundamental question that arises in this setting is regarding which direction (top-down/bottom-up) takes primacy.

A particular sense in which this directly touches upon my work is in the ability of unsupervised and semi-supervised learning methods to model “everything of interest” in a complex domain (e.g., robotics) so that any detailed analysis of the domain is rendered unnecessary. A claim that is often made is that the entire hierarchy will just emerge from the bottom-up. My own experience with difficult problems such as synthesizing complex humanoid robot behaviours makes me sceptical of the breadth of this claim. I find that, often, the easily available observables do not suffice and one needs to work hard to get the true description. However, I am equally sceptical of the chauvinistic view that the only way to solve problems is to model everything in the domain and dream up a clever strategy or the defeatist view that the only way to solve the problem is to look at pre-existing solutions somewhere else and copy them. Instead, in my own work, I have searched for a middle ground where one seeks general principles on both ends of the spectrum and tries to tie it together efficiently.

Recently, while searching google scholar for some technical papers on multi-level control and learning, I came across an interesting philosophical paper (R.C. Bishop and H. Atmanspacher, Contextual emergence in the description of properties, Foundations of Physics 36(12):1753-1777, 2006.) that makes the case that extracting deep organizational principles for a higher level from a purely bottom-up approach is, in a certain sense, a fundamentally ill-posed problem. Even in “basic” areas like theoretical physics one needs more context. Yet, all is not lost. What this really means is that there are some top-down contextual constraints (much weaker than arbitrary rigid prescriptions) that are necessary to make the two mesh together. You will probably have to at least skim the paper to get a better idea but I think this speaks to the same issue I raise above and says something quite insightful.


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