systemity

Part IIa. The map of civilization thrust

Part I. All people are equal but some are more equal (systemity.livejournal.com/631478.html)

As was earlier noted, the increase of life expectancy and income per capita occurs not only in those countries whose contribution to the global improvement of the standard of living in the world is most noticeable. It seems that the overall improvement of life in the world in the long-term perspective which transpires from the data presented by H. Rosling cannot be fully explained by either humanitarian aid to poor countries or by the diffusion processes caused by population migration. It is also evident that gaining independence does not warrant prosperity, as there are many countries which gained independence a long time ago but whose population's living conditions are far from being decent. The overall standard of living is certainly going up in the world, and this is apparently a consolidated, however, uneven process that has some characteristics of spontaneity.

There is a phenomenon of system organization that is 'self-organizing systems' (SoS). Despite the fact that there is vast reference literature on the subject of self-organization, no concrete practical and theoretical approaches are available that would allow for investigation of this phenomenon that widely occurs both in Nature and human society. Popular science writers like to think that there is nothing new in it -- it was, of course, known to Plato and other ancient Greek philosophers, and scholars in the Middle and not so middle ages had intensively thought on this and related subjects: synergetics, non-linear systems, chaos, etc.

However, the tendency to simplify this practically unsolvable problem clashes with the reality when it comes to prediction of behavior of real-world self-organizing systems, such as, for example, the global climate, human brain, the Universe, economy, subatomic systems, social behavior, and many others. Actually, classical science is perceived as inexhaustible namely because of the phenomenon of unpredictability of real-world systems.

Within the past several years, many definitions were proposed for what should be considered a self-organizing system. In my opinion, the best definition was given by Norbert Wiener, the founder of the science of cybernetics. He proposed that there are two kinds of systems: those that have a control center and those that do not and whose structural-functional activities occur due to spontaneous interactions between their constituent elements.

N. Wiener compared the latter -- self-organizing systems -- to a "black box" whose behavior can only be studied through its responses to external impacts. Any interference with the mechanism of a black box's functioning will make such a black box obsolete as it will be no longer the original system but a system that has been tempered by the intrusion of an observer/investigator. As was said by Mephistopheles in Goethe's Faust,

To know and describe a living thing
You first get rid of all its spirit:
Then the parts are all in the palm of your hand,
And all that you lack is the spirit that binds them!

Modern classical science is not intended for investigations into SoS as any such system can be investigated only as a whole, whereas the currently available methods of quantitative analysis are all based on reductionism. There are various specific methods of approaching the systems under investigation, which, however, do not guarantee that the obtained results will reflect the actual processes occurring in the systems under study. In a general (and optimal) case, their behavior can only be imitated, and, as far as I know, the only currently available technology for imitation of behavior of self-organizing systems is a software system MeaningFinder.

MeaningFinder is based on three U.S. patents [1 - 3] and a two-decade long research work in the area of evolutionary transformation of similarity matrices. The description of MeaningFinder as well as a detailed explanation of why it allows to actually imitate behavior of SoS will be provided further in this publication. If I started this paper with the description of methods and discussion of my theoretical positions, full of technicalities, most of the readers would stop reading it even before they could get to the point where I demonstrate the results which might be of real interest to them. Therefore I will first demonstrate some of the results obtained with the use of MeaningFinder, so that the following parts of this paper can be aimed at those readers who will find the presented results interesting. Having that group of readers in mind, I will be explaining the specifics of MeaningFinder, apart from the demographic problems discussed in this paper. Such an organization of the material will be also helpful in the event that the readers may have specific questions on the methods of investigation, which I can answer in full detail in the subsequent parts of this publication.

In this study, we analyzed age-sex pyramids of 220 countries for the year 2000 (a so-called longitudinal analysis, as was mentioned in Part I of this publication). We used two methods. The first method included a comparison of population pyramids of individual countries to an artificial population pyramid constructed so that the portion of each subsequent age cohort was by 30% less than the portion of the preceding age group, while the total of all the age cohorts for each gender was equal to 1. That is, for instance, the number of 0 - 4 years old males was by 30% more than the number of 5 - 9 years old males (whose portions in the total male population were 0.3007 and 0.2105, respectively), etc. This model pyramid is further referred to as the EX30 pyramid.

As a result of such comparison by using our method of image recognition [3], all 220 population pyramids were arranged in a row, depending on each pyramid's dissimilarity to the EX30 pyramid. The said method [3] provides for assessment of dissimilarities between objects -- in this case between the countries described by 34 demographic parameters. The country least similar to EX30 was Monaco (91.9% dissimilarity), and the country most similar to EX30 was Uganda (29.0% dissimilarity).

The second method that was used in this study and is described in detail further in this paper -- computation of the SoS cooperativity index -- is more complex than the former. It provides for determination of a certain coefficient that reflects the degree of connectedness of each of the countries (in this case, through their population pyramids) to the entire system representing the global population. The dependence of that coefficient, which I refer to as 'power of connectedness' (P220), on the degree of dissimilarity of the population pyramids to the EX30 model pyramid was described by two successive curves, similar to normal distribution curves, representing, respectively, 132 countries (the Uganda group) and 88 countries (the Monaco group).

Fig. 1. Distribution of 220 countries by groups.

Briefly, the method of determination of cooperativity index is based on the concept that the structural-functional foundation of a self-organizing system is created by spontaneous interactions between its constituent elements. In case of this study, the interacting elements are population pyramids, each of them described by 34 parameters. Determination of cooperativity index is based on presumption that functioning SoS cannot consist of identical elements that interact in an identical way.

Based on this presumption, the introduction of a certain number of copies of one of the system's elements should cause that element, along with its copies, to separate from the system in the form of an inert mass. Thus, 'power of connectedness' (P) of country X in the self-organizing system of the global population is such a number of copies of the country X's population pyramidadded to the total number of all population pyramids, which will result in the division of the entire system of the pyramids into two clusters: (1) X and (2) the rest of the countries. As it follows from the above-said, the P220 coefficient of each country is the result of evaluation of the behavior of that country in the entire system of the global population.

A more detailed investigation with the use of some other techniques developed by us, which are described further in this paper, demonstrated that between the group of Uganda and the group of Monaco there is a distinctive intermediary group of countries which are marked in yellow on the graph. Thus, all the countries appeared to fall into three groups: the Uganda group (red dots), the intermediary group (yellow dots), and the Monaco group (green dots), which included, respectively, 94, 62, and 64 countries. As is seen in Fig. 1 above, the intermediary group connects the countries of the two other groups.

The existence of the intermediary group can be demonstrated with the help of a few techniques. Here, I will show one of them which is based on determination of the so-called Kex coefficient. In addition to cooperativity indices of the population pyramids of 220 countries (P220), we also determined cooperativity indices of individual countries in the system which included along with the population pyramids of 220 countries, the artificial pyramid EX30 (P221ex). Coefficient Kex equals the ratio of P221ex to P220. Fig. 2 shows the data that unequivocally demonstrate the existence of the intermediary group that stands apart from the two other groups.

Fig. 2. Demonstration of the existence of the intermediary group of countries.

As I mentioned above, all of the methodological details including the tables of values of the coefficients are provided and discussed in the following sections of this paper. However, I would like to start the discussion by demonstrating the world map that shows the distribution of 220 countries in three groups as discovered by us.

Fig. 3. Map of the grouping of the countries by their contribution to civilization (Click three times for enlarged view).

On the above-shown map, which could be referred to as a "map of civilization thrust", countries of the Uganda group are colored in red, the intermediary group countries are colored in yellow, and the countries of the Monaco group are colored in green. First of all, one can see that the grouping of the countries has a distinctive geographic character. For example, the "yellow" group includes the major parts of the Central and South America as well as Southeast Asia. Turkey, India, Indonesia, Sri Lanka, and all the countries with predominantly Chinese population, except Hong Kong, Malaysia, Thailand and Vietnam, belong to that group.

The "reds" include practically the entire Africa, excluding RSA, Tunisia and Gabon (which is one of the most developed countries of Africa), as well as the Middle East, excluding Israel and Lebanon, and the major part of the Central Asia. The "greens" are the entire Europe, Russia, North America, Japan, Australia, New Zealand, and a few other countries. In this group, there are no African countries, and the only Asian and Latin American countries are Japan and Uruguay.

Fig. 4. Cooperativity indices for the Uganda group.

*** На равновесный характер группирования указывает также следующее обстоятельство. In addition to comparison to the EX30 model pyramid, the population pyramids of 220 countries for the year 2000 were compared to another, alternative model pyramid, referred to as the UN-pyramid. In construction of the UN-pyramid including 17 five-year cohorts of male and female populations, the portion of each cohort was 1/17, i.e. equaled 5.882%. Thus, the UN-pyramid was not even urn-shaped but rather had a rectangular shape. As is seen on the graph in Fig. 5, the coefficients of dissimilarity of the real pyramids to the artificial pyramids, EX30 and UN, are inversely proportionalк чему?

Fig. 5. Correlation between coefficients of dissimilarity of the population pyramids of 220 countries to model population pyramids EX30 and UN.

In one of the following parts of this publication, I will provide a complete table of data on all of the countries under analysis, for those who might be interested in a thorough review of the methodological aspect of this work. However, the results demonstrated on the above-shown map seem to be sufficient for drawing conclusions.

There is no doubt that the countries which on the above-shown map are colored in green (the Monaco group) are those countries that are and have been the source of the "civilization thrust", of the impact that has resulted in the overall increase of life expectancy and standard of living in the world, even in those countries that do not care about it and view themselves as "victims" of the civilization thrust brought about by the "green" countries.

It is the population of the "green" countries that have created all the technological innovations that are now used by the mankind all over the world. This is not just about academic science, electronics, computers and communication technologies. This is about what and how people eat, how they dress, communicate, entertain, travel, pack and ship goods, extract ores, produce energy, build homes, fight wars...

Nothing like that came from the countries of the Uganda group, colored in red. Many of these countries are rich in oil, gas and minerals, and to produce them, they use technologies developed by the "green" countries and hire specialists, consultants and managers from the countries of the Monaco group. Majority of the population of these countries live the same way as their ancestors lived a hundred years ago and slowly adopt modern technologies due to migrants, international aid, individual enthusiasts, and due to information coming in through available mass-media.