Evolution of intelligence:Direct modeling of temporal effects of environment on a global absolute scale vs statistics

H Mark Hubey

Research output: Contribution to journalArticleResearchpeer-review

Abstract

The social sciences are really the “hard sciences” and the physical sciences are the “easy” sciences. One of the great contributors to making the job of the social scientist very difficult is the lack of fundamental dimensions on the basis of which absolute (i.e. ratio) scales can be formulated and in which relationships could be realized as the [allegedly] coveted equations of physics. This deficiency leads directly to the uses of statistical methods of various types. However it is possible, as shown, to formulate equations and to use them to obtain ratio/absolute scales and relationships based on them. This paper uses differential/integral equations, fundamental ideas from the processing view of the brainmind, multiple scale approximation via Taylor series, and basic reasoning some of which may be formulated as infinitevalued logic, and which is related to probability theory (the theoretical basis of statistics) to resolve some of the basic issues relating to learning theory, the roles of nature and nurture in intelligence, the measurement of intelligence itself, and leads to the correct formulation of the potentialactual type behaviors (specifically intelligence) and dynamicaltemporal model of intelligence development. Specifically, it is shown that the: (1) basic model for intelligence in terms of genetics and environment has to be multiplicative, which corresponds to a logicalAND, and is not additive; (2) related concept of “genetics” creating its own environment is simply another way of saying that the interaction of genetics and environment is multiplicative as in (1); (3) timing of environmental richness is critical and must be modeled dynamically, e.g. in the form of a differential equation; (4) path functions, not point functions, must be used to model such phenomena; (5) integral equation formulation shows that intelligence at any time t, is a a sum over time of the past interaction of intelligence with environmental and genetic factors; (6) intelligence is about 100 per cent inherited on a global absolute (ratio) scale which is the natural (dimensionless) scale for measuring variables in social science; (7) nature of the approximation assumptions implicit in statistical methods leads to “heritability” calculations in the neighborhood of 0.5. and that short of having controlled randomized experiments such as in animal studies these are expected sheerely due to the methods used; (8) concepts from AI, psychology, epistemology and physics coincide in many respects except for the terminology used, and these concepts can be modeled nonlinearly.

Original languageEnglish
Pages (from-to)361-431
Number of pages71
JournalKybernetes
Volume31
DOIs
StatePublished - 1 Apr 2002

Fingerprint

Social sciences
Statistics
Integral equations
Statistical methods
Physics
Modeling
Taylor series
Terminology
Animals
Social Sciences
Differential equations
Statistical method
Multiplicative
Randomized Experiments
Processing
Heritability
Integral-differential Equation
Epistemology
Learning Theory
Genetics

Keywords

  • Brain
  • Computers
  • Cybernetics
  • Intelligence

Cite this

@article{0a06e818aa16491ca4427e622e0b67c1,
title = "Evolution of intelligence:Direct modeling of temporal effects of environment on a global absolute scale vs statistics",
abstract = "The social sciences are really the “hard sciences” and the physical sciences are the “easy” sciences. One of the great contributors to making the job of the social scientist very difficult is the lack of fundamental dimensions on the basis of which absolute (i.e. ratio) scales can be formulated and in which relationships could be realized as the [allegedly] coveted equations of physics. This deficiency leads directly to the uses of statistical methods of various types. However it is possible, as shown, to formulate equations and to use them to obtain ratio/absolute scales and relationships based on them. This paper uses differential/integral equations, fundamental ideas from the processing view of the brainmind, multiple scale approximation via Taylor series, and basic reasoning some of which may be formulated as infinitevalued logic, and which is related to probability theory (the theoretical basis of statistics) to resolve some of the basic issues relating to learning theory, the roles of nature and nurture in intelligence, the measurement of intelligence itself, and leads to the correct formulation of the potentialactual type behaviors (specifically intelligence) and dynamicaltemporal model of intelligence development. Specifically, it is shown that the: (1) basic model for intelligence in terms of genetics and environment has to be multiplicative, which corresponds to a logicalAND, and is not additive; (2) related concept of “genetics” creating its own environment is simply another way of saying that the interaction of genetics and environment is multiplicative as in (1); (3) timing of environmental richness is critical and must be modeled dynamically, e.g. in the form of a differential equation; (4) path functions, not point functions, must be used to model such phenomena; (5) integral equation formulation shows that intelligence at any time t, is a a sum over time of the past interaction of intelligence with environmental and genetic factors; (6) intelligence is about 100 per cent inherited on a global absolute (ratio) scale which is the natural (dimensionless) scale for measuring variables in social science; (7) nature of the approximation assumptions implicit in statistical methods leads to “heritability” calculations in the neighborhood of 0.5. and that short of having controlled randomized experiments such as in animal studies these are expected sheerely due to the methods used; (8) concepts from AI, psychology, epistemology and physics coincide in many respects except for the terminology used, and these concepts can be modeled nonlinearly.",
keywords = "Brain, Computers, Cybernetics, Intelligence",
author = "Hubey, {H Mark}",
year = "2002",
month = "4",
day = "1",
doi = "10.1108/03684920210422557",
language = "English",
volume = "31",
pages = "361--431",
journal = "Kybernetes",
issn = "0368-492X",
publisher = "Emerald Group Publishing Ltd.",

}

Evolution of intelligence:Direct modeling of temporal effects of environment on a global absolute scale vs statistics. / Hubey, H Mark.

In: Kybernetes, Vol. 31, 01.04.2002, p. 361-431.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Evolution of intelligence:Direct modeling of temporal effects of environment on a global absolute scale vs statistics

AU - Hubey, H Mark

PY - 2002/4/1

Y1 - 2002/4/1

N2 - The social sciences are really the “hard sciences” and the physical sciences are the “easy” sciences. One of the great contributors to making the job of the social scientist very difficult is the lack of fundamental dimensions on the basis of which absolute (i.e. ratio) scales can be formulated and in which relationships could be realized as the [allegedly] coveted equations of physics. This deficiency leads directly to the uses of statistical methods of various types. However it is possible, as shown, to formulate equations and to use them to obtain ratio/absolute scales and relationships based on them. This paper uses differential/integral equations, fundamental ideas from the processing view of the brainmind, multiple scale approximation via Taylor series, and basic reasoning some of which may be formulated as infinitevalued logic, and which is related to probability theory (the theoretical basis of statistics) to resolve some of the basic issues relating to learning theory, the roles of nature and nurture in intelligence, the measurement of intelligence itself, and leads to the correct formulation of the potentialactual type behaviors (specifically intelligence) and dynamicaltemporal model of intelligence development. Specifically, it is shown that the: (1) basic model for intelligence in terms of genetics and environment has to be multiplicative, which corresponds to a logicalAND, and is not additive; (2) related concept of “genetics” creating its own environment is simply another way of saying that the interaction of genetics and environment is multiplicative as in (1); (3) timing of environmental richness is critical and must be modeled dynamically, e.g. in the form of a differential equation; (4) path functions, not point functions, must be used to model such phenomena; (5) integral equation formulation shows that intelligence at any time t, is a a sum over time of the past interaction of intelligence with environmental and genetic factors; (6) intelligence is about 100 per cent inherited on a global absolute (ratio) scale which is the natural (dimensionless) scale for measuring variables in social science; (7) nature of the approximation assumptions implicit in statistical methods leads to “heritability” calculations in the neighborhood of 0.5. and that short of having controlled randomized experiments such as in animal studies these are expected sheerely due to the methods used; (8) concepts from AI, psychology, epistemology and physics coincide in many respects except for the terminology used, and these concepts can be modeled nonlinearly.

AB - The social sciences are really the “hard sciences” and the physical sciences are the “easy” sciences. One of the great contributors to making the job of the social scientist very difficult is the lack of fundamental dimensions on the basis of which absolute (i.e. ratio) scales can be formulated and in which relationships could be realized as the [allegedly] coveted equations of physics. This deficiency leads directly to the uses of statistical methods of various types. However it is possible, as shown, to formulate equations and to use them to obtain ratio/absolute scales and relationships based on them. This paper uses differential/integral equations, fundamental ideas from the processing view of the brainmind, multiple scale approximation via Taylor series, and basic reasoning some of which may be formulated as infinitevalued logic, and which is related to probability theory (the theoretical basis of statistics) to resolve some of the basic issues relating to learning theory, the roles of nature and nurture in intelligence, the measurement of intelligence itself, and leads to the correct formulation of the potentialactual type behaviors (specifically intelligence) and dynamicaltemporal model of intelligence development. Specifically, it is shown that the: (1) basic model for intelligence in terms of genetics and environment has to be multiplicative, which corresponds to a logicalAND, and is not additive; (2) related concept of “genetics” creating its own environment is simply another way of saying that the interaction of genetics and environment is multiplicative as in (1); (3) timing of environmental richness is critical and must be modeled dynamically, e.g. in the form of a differential equation; (4) path functions, not point functions, must be used to model such phenomena; (5) integral equation formulation shows that intelligence at any time t, is a a sum over time of the past interaction of intelligence with environmental and genetic factors; (6) intelligence is about 100 per cent inherited on a global absolute (ratio) scale which is the natural (dimensionless) scale for measuring variables in social science; (7) nature of the approximation assumptions implicit in statistical methods leads to “heritability” calculations in the neighborhood of 0.5. and that short of having controlled randomized experiments such as in animal studies these are expected sheerely due to the methods used; (8) concepts from AI, psychology, epistemology and physics coincide in many respects except for the terminology used, and these concepts can be modeled nonlinearly.

KW - Brain

KW - Computers

KW - Cybernetics

KW - Intelligence

UR - http://www.scopus.com/inward/record.url?scp=0036018166&partnerID=8YFLogxK

U2 - 10.1108/03684920210422557

DO - 10.1108/03684920210422557

M3 - Article

VL - 31

SP - 361

EP - 431

JO - Kybernetes

JF - Kybernetes

SN - 0368-492X

ER -