6.b.3) Sensitivity analysis - Globus Model

Another oversimplification refers to evolution itself. The General Theory of Conditional Evolution of Life (GTCEL) indicates that genetic modifications indeed exists, that intelligence increases throughout life by means of internal work, and, that it is transmitted to descendants. So, I will introduce this improvement in the simulation model. Complexity will increase while introducing its correspondent combinatorial algorithms of error patterns.

Internal evolution Genetic evolution of intelligence
Genetic evolution of intelligence

We also have the possibility of introducing asymmetric combinatorial algorithms to help the statistical simulation models of evolution of intelligence achieve its goal of decreasing the MCI. Complexity increases again. Internal evolution will only take place in the male genes; they are the ones that are renewed constantly during a lifetime. I am sorry, but the General Theory of Conditional Evolution of Life (GTCEL), according to what I was taught in biology when I was little, reminds me that the ovules are fixed from a very early age in girls.

Also, by following the model of evolution of intelligence, we can distinguish between direct and indirect internal evolution; in the former, the capacity will be increased in a percentage of its own value while in the latter, the increment will be a percentage of the capacity of the other gene or, better said, chromosome. This will imply an additional asymmetry and will make the correlation drop a little more than only the internal evolution.

The computer will make all the calculations of the necessary combinatorial algorithms in statistical simulation model. At least, math complexity will not be a problem.

A logical factor of minimum internal evolution was also checked; it was discarded due to the bad adjustments obtained.

Statistical study
Globus parametrized model


Internal Evo.°
T1-d, X3 y X6 and arrangement criterion M1F1°
Objective function
Direct Indirect M & F
Mothers Graphs GMCI r² max. Graphs GMCI r² max.
5 5 q071° 14,14 0,72 q072° 14,46 0,72
3 3   14,21 0,82   14,81 0,82
1 1   13,49 0,80   13,89 0,80
0 0 q023 14,98 0,92 q024 16,07 0,92
1 1   14,06 0,83   16,10 0,87
2 3   14,79 0,87   16,10 0,87
3 3   15,33 0,84   16,47 0,84
4 4   15,09 0,84   16,73 0,84
5 5 q063° 15,61 0,89 q064° 17,77 0,89
6 6   14,30 0,95   16,74 0,95
7 7   13,25 0,83   15,56 0,83
° Internal evolution parameters affect the objective function R and M1F1 order

Considering that internal evolution parameters will affect the objective function and variable M1F1° of the sample's previous arrangement, the effect on the correlations of changes in these parameters would allow us to see changes in the goodness-of-fit of this model's specifications. Using sensitivity analysis of these parameters of the model, not of society, it will allow the optimization of this magnitude.

The optimization with original variables is not as conclusive as with centered variables, these ones generate more precise results.

Evolution of intelligence
Evolution of intelligence graph

The graph shows the optimization done and that the best adjustment is obtained for a value of 5% for each of the parameters of internal evolution, direct and indirect. This means 10% in each generation of male genes. It would be a good idea to emphasize that in the initial General Theory of Conditional Evolution of Life (GTCEL) description, 10 years ago, I did mention a figure of 10% while talking about internal evolution.

Although more studies with more data are strongly recommended due to the complexity of the model of evolution of intelligence and all the combinatorial algorithms of error patterns, the difference in the MCI-Gs is, in my opinion, sufficiently noteworthy. I would also like to comment that each point of this graph represents 30 determination coefficients, r², between variables M & F and the average of variables C, and those deviations are compensated not only for the centered children variables C but also for the rearranged grouping.

Algoritmos of optimizatation
Globus model

Given the high degree of social sensitivity in these scientific areas, I would like to stress that I checked within the statistical simulation model whether the opposite assumption of male-female evolution would work in the same fashion; in other words, supposing that only females changed genes. In the same graph the results of the optimization are shown: as I expected the adjustments are even worse than in a no-evolution situation.

It is interesting to examine the X3 and X6 variables separately. Doubtlessly, X6 should present better results as the deviations of the natural combinatorial algorithms are more compensated.

The observed peak for the null evolution or the equivalent, that both sexes would contribute the same percentage to internal evolution has a really difficult explanation. I think we need to use complexity science within a quantitative approach; among others, I can think of a precarious idea: the possibility that not all men carry out the improvement of their genes due to a lack of confidence in Nature when determined indicators are present.

In this assumption, given the model sensitivity and the standardized variables, the first evolutionary increase of one percent would shrink the correlations, whereas when we approached the optimal value, the effect of a correct percentage of internal evolution would surpass the previous one.

Anyway, the optimal amount of 5% of direct internal evolution and the 5% of indirect internal evolution, of the capacity transmitted by men's genes is fairly clear.

The subject is not as serious as it seems socially if one knows or remembers what the GTCEL says about the meaning of sexual differentiation, specialization, etc. Women have the important and difficult task of the initial development of children that implies a biological specialization in technology of materials.

For that reason the parameter of endogenous external evolution is included in the statistical simulation model: it gathers the evolutionary effect generated by women, and in particular, it could imply an average of increase of 5% with random distribution. This, however, cannot be verified at the moment since its variation affects neither the objective function R nor the criteria of arrangement M1F1.

Another logical point is that the increase generated by men also comes from certain changes due to the improvement of available materials thanks to the amelioration in the quality of their formation when they were in the womb.

On the other hand, it is very possible that women's genes have a backup function to maximize the guarantee of the viability of the new being. On the contrary, Nature would be the first good programmer who would not make copies of her marvelous codes or programs once they have acquired a certain degree of complexity and accumulated work.

In fact, this result about evolution parameters is the most outstanding one of this study. I would say that, if it cannot be refuted, it should be accepted the General Theory of Conditional Evolution of Life, at least, in its main idea of the existence of a finalist evolution and the abandonment of the theory of random mutations and, consequently, of natural selection as the main mechanism of the evolution.