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A useful learning concept for social analysis

Following the madness that I mentioned in a previous post (on axiomatic set theory), and having as one of the basic skills of the actors covered by this theory is that they can learn, it follows then it is necessary to develop a concept of learning. So here's the concept.

Learning: The ability of an actor to develop partnerships, all while learning is cognitive and practical elements

Comment

The definition explicitly

learning does not pose as an acquisition of associations or beliefs 'correct'. And this for the simple fact that we can not determine what knowledge is 'right': what we know at any given time can be shown to be incorrect later.

What interests us more is that makes a learner: And what does is order the world to establish partnerships: Partnerships in behavior (ie if I do X to Y) or associations between distinctions in the world (ie the objects are X and quality). This is a system because, then, anything can happen (if I do X to Y and not Z). What it does is to allow learning passing from one world and non-report one with distinction. The fact distinctions and create partnerships among these distinctions is the crucial fact of learning. Put another way, learning is a way to 'create' (find out) order from chaos.

These associations should not be thought of as clearly defined rules and that can lead to a simple formula. Rule if I do X to Y is a short form to refer to the association, but is compatible with exceptions and ambiguities. As to guide the actor in general if I do X to Y using distinctions which generally operate to define X and Y, that's enough for the actor.

Learning, in this sense has a very clear relationship with the notions of informational complexity, with the idea of \u200b\u200bKolmogorov complexity. Recall that a string of maximum complexity is one that can only be described by a string of equal length. A simple chain is one that can be described by a much smaller chain (is compressed without losing information.) Learning is precisely the discovery process in the chain of lesser extent.

There is a certain paradox in any case, above. This simplification for the actor may occur as a complication. This is because random objects in the traditional view are maximally complex, but a fully stochastic system can be described in a statistical way so limited: It can also replicate the chain, but very similar. In this sense, it is structurally complex (Crutchfield, 1994) . Adding rules, and therefore possibility of compression involves complicate this brief description. Learning, then I can go from a world without distinctions and informationally complex, but structurally simple in a world with distinction, informationally simpler but structurally more complex (ie a world whose best description is longer). Is moving from a world I can not describe in detail and just in general to a world I can describe (and act) specifically using the rules that I discover.