6.a) Computer simulation of the evolution of intelligence

  • Actual values and observed values!

The properly reformulated Individual model or Social model of evolution of intelligence has been useful to determine that the significant gene is the one of less intellectual powers.

Due to the accuracy of the Social model of evolution of intelligence, and the fact that I had all the elements to simulate the proposed model by the General Theory of Conditional Evolution of Live (GTCEL), I thought it would be a good idea to make a computer simulation of it to confirm the results. The statistical simulation should generate artificial intelligence quotients that should behave like those observed.

Statistical study
Computer simulation of evolution
Intelligence Artificial intelligence quotients
Graphics Subject Observations
q050 MCIW Too high
q060 MCIW Similar to GMCI

The second big surprise, for me, was the initial failure of the simplified Social model of evolution of intelligence to obtain this objective of statistical simulation of processes and mechanisms of biological inheritance.

This task was much more complicated than I thought, forcing me to eliminate all the simplifications that I had introduced in the Social model of evolution of intelligence.

The typical result of the generated variable W can be seen in the q050 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 of the children.

Evolution of intelligence Complex model with random deviations
Evolution of intelligence with VIG method

Earlier, we commented that the differences in IQ measurements of the same children were very high. Also, that this was surely due to the manifestation of the child's capacity at any given moment, and even more so, over the years.

Other factors causing the same type of deviations are the particular intelligence test used and each specific test session within a standard test.

Consequently, we can introduce an additional combinatorial algorithm of the statistical simulation models to represent a factor of randomness based on these causes of the evolution of intelligence. Although the observed differences are superior to 10% in respect to the average in some cases, I will introduce a mean deviation of 3% upward and 3% downward.

For the same reason we introduce elements of error in children variables C, we should set an error pattern in M and F variables.

Nevertheless, the high correlation of W in computer simulation of the evolution of intelligence does not decrease substantially and do not behave as the original IQ vectors, like those from Stanford Binet test o Wechsler intelligent test.


6.b) Statistical simulation model: complexity and optimization

It is necessary to introduce more combinatorial algorithm to represent other error patterns or the complexity of the statistical simulation model of evolution of intelligence to be able to qualify the model as acceptable. Nevertheless, it is not so easy, since it must lower the correlation in the unordered groups, mainly in the small groupings.

At the same time, in the previously rearranged groups the correlations should decrease in the small groups and increase or remain the same in the big ones. Once, we achieve a good model specification for the evolution of intelligence we could begin its optimization.

6.b.1) Genetic affinity

First of all, we should try to eliminate the simplifications carried out in the model's theoretical argumentation to avoid its complexity.

In order to continue lowering the multidimensional correlation index of W, we may include the interesting filter effect mentioned by the General Theory of Conditional Evolution of Life (GTCEL) in the statistical simulation model of evolution of intelligence regarding the resulting intellectual power of the genetic combination. It will be equal to the intersection of the potentials and not to the potential of the smaller gene.

Of course, this decrease due to the lack of genetic affinity will not definitely be fixed in all cases and, for that reason, we will treat it in the statistical simulation as a random pattern; another margin of 3% upwards or downwards can be introduced bearing in mind the possible drag effect of ancestors.

After considering this filter effect, or affinity, the correlation has lowered again, but not much. And the complexity of the statistical simulation model continues to increase.