 NIP (model theory)

In model theory, a branch of mathematical logic, a complete theory T is said to satisfy NIP (or "not the independence property") if none of its formulae satisfy the independence property, that is if none of its formulae can pick out any given subset of an arbitrarily large finite set.
Contents
Definition
Let T be a complete Ltheory. An Lformula φ(x,y) is said to have the independence property (with respect to x, y) if in every model M of T there is, for each n = {0,1,…n − 1} < ω, a family of tuples b_{0},…,b_{n−1} such that for each of the 2^{n} subsets X of n there is a tuple a in M for which
The theory T is said to have the independence property if some formula has the independence property. If no Lformula has the independence property then T is called dependent, or said to satisfy NIP. An Lstructure is said to have the independence property (respectively, NIP) if its theory has the independence theory (respectively, NIP). The terminology comes from the notion of independence in the sense of boolean algebras.
In the nomenclature of Vapnik–Chervonenkis theory, we may say that a collection S of subsets of X shatters a set B ⊆ X if every subset of B is of the form B ∩ S for some S ∈ S. Then T has the independence property if in some model M of T there is a definable family (S_{a}  a∈M^{n}) ⊆ M^{k} that shatters arbitrarily large finite subsets of M^{k}. In other words, (S_{a}  a∈M^{n}) has infinite Vapnik–Chervonenkis dimension.
Examples
Any complete theory T that has the independence property is unstable.^{[1]}
In arithmetic, i.e. the structure (N,+,·), the formula "y divides x" has the independence property.^{[2]} This formula is just
So, for any finite n we take the n 1tuples b_{i} to be the first n prime numbers, and then for any subset X of {0,1,…n − 1} we let a be the product of those b_{i} such that i is in X. Then b_{i} divides a if and only if i ∈ X.
Every ominimal theory satisfies NIP.^{[3]} This fact has had unexpected applications to neural network learning.^{[4]}
Notes
References
 Anthony, Martin; Bartlett, Peter L. (1999). Neural network learning: theoretical foundations. Cambridge University Press. ISBN 052157353X.
 Hodges, Wilfrid (1993). Model theory. Cambridge University Press. ISBN 0521304423.
 Knight, Julia; Pillay, Anand; Steinhorn, Charles (1986). "Definable sets in ordered structures II". Transactions of the American Mathematical Society 295 (2): 593–605. doi:10.2307/2000053.
 Pillay, Anand; Steinhorn, Charles (1986). "Definable sets in ordered structures I". Transactions of the American Mathematical Society 295 (2): 565–592.
 Poizat, Bruno (2000). A Course in Model Theory. Springer. ISBN 0387986553.
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