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From: briand@infmx.UUCP (brian donat)
Newsgroups: comp.ai
Subject: PROBABLE COMPLEXITY QUOTIENT
Keywords: A step towards quantification of chaos?
Message-ID: <1591@infmx.UUCP>
Date: 19 Jun 89 18:53:26 GMT
Organization: Informix Software Inc., Menlo Park, CA.
Lines: 84


Please feel free to hack away at the following:


Given that the Human Brain is complex, complex to the point that we regard
it now in the terminology of chaos theory, and given that we recognize that
the outer expressions of the brain (as a system) are well ordered and that
this order implies a system of control functions which successively modify
'lower order' variations into large scale composite outcomes, has anybody...


	1.   done any significant investigative work regarding the 
		identification of the brain's control apparatus or functions?

		(this is not to be confused with theoretical rumblings
		 about what intelligence or knowledge, etc. are.)

		I'm sure something's been done here.
		But I'm particularly interested in the identity of 
		composite transistion zones which qualify for the term
		of 'points of bifurcation' in the chaos theory definition
		and also identify 'contact' with specific control functions.


	2.   done any mathematical calculations to estimate a probable   
		complexity figure characteristic for the human brain?

		Is there such an animal as a probable complexity figure?
		Complexity quotient? 


	3.   done any mathematical calculations to estimate a probable
		complexity figure characteristic for any chaotic system? 


	4.   identified any of the 'low order' variations which affect
		outcomes in the human brain? 


	5.   having identified 'low order' variations and having 
		calculated a probable complexity figure for the human brain,
		has anyone begun to analyze the best possible ways to 
		develope a working model which will duplicate any level
		of the human brain's functionality while achieving the 
		a similar probable complexity figure? 


These are just a few thoughts turned into questions.  It occurs to me that
simple binary logics will not suffice to duplicate the complexity figure for
the human brain.   Instead, something analog might do better.          

Also, the brain is filled with minor variations which involve metabolism 
(cell death, protein synthesis, growth, blood/oxygen variations, etc.).
What 'non-living' synthetic model could match the complexity quotient of
all these variations and still reflect the order of control necessary to 
play trivial games such as passing a Turing Machine Test? 

Really, is there such a thing as a complexity quotient? 

I assume that this might somehow allow a calculation for the magnitude
of changes in the outcomes measured at a larger composite definition
for a given system, given that at any one time, a given number of pseudorandom
events will be occuring which will affect that outcome.  I also assume that
the inclusion of control functions at various levels within the organization
of the system will mathematically affect the quotient in either a negative
or positive direction (system destructive or system non-destructive) and that
a series of relative complexity quotients might be calculable between these
varying levels.                 

Is a complexity quotient needed to progress with assimilation of
artificial intelligence? 

I would think so.
 
Any thoughts? 


-- brian

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| Brian L. Donat		Informix Software, Inc.  Menlo Park, CA        |
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