The computer simulation within the Global Model should generate artificial intelligence quotients that should behave like those observed

Cover of the book the EDI Study. Dusk over the sea with clouds, Galicia.




Author: José Tiberius

q050 The Global Model should generate artificial intelligence quotients


The title of each graph of the statistical study indicates the parents' variables (R or M & F) to which the correlations relate. Each point of the colored lines represents the correlations with the observational C variables of the children.

Likewise, the variables of unknown order, formed by the different groups of 1 to 10 values from the 70 IQ values of each parent and children variables, appear on the left-hand side of the graph. The criteria order of the groups of 1 to 10 values located on the right-hand side is the variable mentioned at the bottom of the graph.

Indeed, there is an almost instantaneous perception of the exactitude of the particular specification of the statistical study; each graph shows sixty coefficients of determination (r²) highlighting the global and underlying relations of the involved data set.

See the methodology of the statistical abstract for more details



1. General statistical significance

The considerable increase of the correlation for the estimation of homogenous groups is not due to the reduction of 68 to 5 or 4 degrees of freedom, since the estimation with non-homogenous groups, without previous rearrangement, has the same degrees of freedom and the correlation even lowers concerning the sample without grouping.

In general, the model of the genetic evolution of intelligence (Mendelian geneticsConditional intelligenceGlobal Cognitive Theory) adjusts perfectly, showing an superior to 0.9 in several cases. Bearing in mind the tendency to increase the goodness of fit with the size of rearranged groups, we could assume it would be over 0,9 in almost all the cases for grander groups within a more significant sample.

2. Development of the evolution with artificial IQ

The Social Model resolved the debate heredity vs. environment. It shows that there is not much margin left to deny the hereditary nature of intelligence, not even to try to reduce it to less than 80%. Of course, one could always argue that there is a problem with the concept of intelligence, and why not? With the definition of the environment.

Due to the accuracy of the Social Model, the researchers tried to carry out a computer simulation with artificial IQ, which could allow performing a sensibility analysis. The introduction of the ability to generate quantitative variables with disturbances close to real ones will imply a new model.

The task was much more complicated than thought, forcing the team to eliminate all the simplifications introduced in the original design.

Finally, after introducing the functional limitations, the Global Model works satisfactorily. The graphs speak for themselves.

Nonetheless, before currying out with Global Model the same analysis done with the Social Model, it is necessary to know the rate of evolution of the intelligence giving the best good-of-fit, which will fulfill the development of the Global model.

3. Significant comments on this particular graph

The typical result of the generated variable W before eliminating the simplifications can be seen in this graph. Considering that W is a random variable, the graph represents the average of ten estimates for the corresponding correlations.

The MCI of the artificial intelligence quotients vector W, which has been multiplied by 3 for comparative reasons, is over 25 and far above the G-MCI for the observed C variables.