Natural language / neuro economics

1. Universal economic constraint
a. Learning: a fable
i. Suppose an intelligence confronted with two vital tasks
1. learn
2. Decide in uncertainty
ii. It is subjected to a constraint of 'finitude': to produce models has a cost of complexity
iii. i + ii brings a fundamental compromise: simplicity / generalization
b. The solution to 1.a.iii seems to have been (in our world) universally algorithmical
i. algorithm
ii. Mathematical: (logic of) categories, CF μεταφορά
iii. Natural Language (NL)
c. Here 'universal' undoubtedly has an economic logic: tradeoff tractability-expressiveness, remix of simplicity-generalization: CF SGI, II


2. NL between philosophy and AI
One finds surprisingly little trace of the economic problem that the natural languages ​​(NL) have to solve, where one would expect to find the concept as a preamble to any philosophy of language.
The exit out of the essentialist conception of language took time ... we must wait Frege and Wittgenstein to leave it, CF Bouveresse.
Wittgenstein talks about language games both for NL and math. The notion of rule (convention) exhausts their 'philosophy', 'end point' seems to say W.
The first order logic (FOL) is 'combining the best of formal and NL', dixit AI Russell Norvig: gratuitous and fortunately false claim, CF 'paradox of learning' or μεταφορά, but the applications of FOL are numerous, starting with our 'FOL for mining news'.

3. NL in cognition and evolution theory
a. The language represents a remarkable solution to 1.c: a machine to create models, flexible and constrained
b. The rules of use are limits to linguistic computation
c. They allow compromise in the saying 'anything': between everything and nonsense
d. CF :'Linguistic structure is an evolutionary trade-off between simplicity and expressivity' Kirby, and 'The origins of syntax in visually grounded robotic agents', Steel
e. Note that expressivity ≠ creativity (= inference calculus): Kirby's paper only deals with the first, not the second. Now the discovery of rules, more precisely structures, is what matters to us in L2L: CF SGII, μεταφορά

4. NL ~ Cat?
The equivalence NL ~ FOL is obviously false, the language makes it easy to speak of sets of sets and so on. So we would rather try the guess NL ~ Cat, especially with regard to 'creativity'
(RDF in Cat, in "Category for the sciences", Spivak, 6.2.2)

5. Neuro ~ Cat?
Beyond language, one can even wonder if Cat could not constitute a paradigm for the cognition in neurosciences