Автор: ЧАЙКО ВЛАДИМИР ИВАНОВИЧ / CHAIKO VLADIMIR IVANOVICH
Introduction
Despite technological progress in the field of computing, scientists around the world use old methods of modeling human dynamics, which have a large number of flaws. Other approaches, although they exist, are still unrealizable due to the lack of computing power of the computer. This suggests that today there is a need to develop a new method that will allow predicting the future of humanity with greater accuracy.
The scientific novelty of the work lies in the fact that it offers a new method for modeling the system dynamics of mankind.
The purpose of the scientific work is to substantiate and develop a new approach to modeling the dynamics of humanity, followed by proof of its (approach) viability in practice by creating a model of a logger's activity in the forests of Siberia, and to verify by comparing its forecasts with the real activities of loggers.
The objectives of this study are:
1. To study the existing models of the global dynamics of humanity.
2. To reveal the shortcomings of these models.
3. Develop and propose a new method for modeling human dynamics.
4. Demonstrate the viability of the proposed new approach by creating a model of a logger's activity by the proposed method.
5. Check the correctness of the model's forecasts by comparing its forecasts with the real activity of loggers.
The model itself, technical documentation and instructions for it are available for download from Yandex Disk at the link: https://disk .yandex.ru/d/s345Azta2Uiyrg .
The main part
Existing models of human dynamics
Throughout its existence, humanity has sought to learn how to predict the future. In ancient times, predictions were made by various sages, fortune tellers, seers and shamans based on their intuition. [1] Needing more reliable methods, humanity began to develop forecasting based on science. So in the XVII century, higher mathematics with mathematical analysis [2] and probability theory appeared. They made it possible to describe existing processes using formulas, determine possible options for their development and evaluate the probabilities of these options. Statistics were developed in the XVII century to verify such forecasts. [3] The world has come to create mathematical models.
In the middle of the twentieth century, forecasting began to be carried out using computers. So in the 1950s and 1960s there was a boom in the creation of mathematical models with their subsequent calculation on a computer. Unfortunately, they were not made public, including by the United States and the Soviet Union. So the USSR developed a model of the evolution of the general ecological situation on Earth. An absolutely accurate description of the physical processes of the planet made this model difficult, including for calculating on a computer. [4] To create a model of the dynamics of humanity, at that time, a more formal approach and a project open to scientists around the world were required.
In 1968, a group of enthusiastic industrialists led by Aurelio Peccei and Alexander King created a public organization for the study and solution of global problems, called the "Club of Rome". In 1970, MIT professor Jay Foresetr proposed to this organization to create a mathematical model of the state of the planet and humanity. [5] After making some sketches of his model on paper, Forrester called it "World". Later, he redesigned his model for calculation on a computer, creating a graphical programming language DINAMO. This model was named "World2". Its scheme is shown in Figure 1 of Appendix A. The "World2" model itself is a set of indexes that affect each other. The relationship between them was established empirically by Forrester.
The model predicted a gradual decline in the world's population, a fixation of the standard of living and an increase in pollution of the planet from 2020. The result of this simulation is shown in Figure 1 of Appendix B.
Forrester believed that the computer does not make mistakes in calculations, so his model is accurate, which is not true. There are a large number of factors that lead to errors in the execution of programs, [6] which led to modeling errors in this model as well: during the modeling process of one of the scenarios of human development, the model showed a jump in the quality of life after a sharp jump in the pollution of the planet. The results of this simulation are presented in Figure 2 of Appendix B.
Such models are erroneous initially because of the formal approach, and create only "time scans" of processes, and not "predictions". The reliability of these "predictions" is very low: these processes strongly depend on wars, epidemics, scientific and technological progress, which were not included in the model due to the impossibility of predicting their appearance in the form of a formula.
Some time later, Forrester decided to transfer the development of the model to others. [4] A group of scientists from MIT enthusiastically took up the creation of such a model. Despite the shortcomings of the quantitative approach, scientists continued to use it due to the lack of other methods. Creating their own model, they proceeded from the fact that there are only 5 most important global parameters: population, industrial production, agricultural production, resources and pollution of the planet. All these 5 parameters were presented in the form of 5 variables, the values of which reflect the state of the entire planet. Each of the variables has its own unit of measurement, reduction and increase contours. These contours are shown in Table 1.
Variable Magnification contour Reduction contour
Population Birth rate Mortality
Production Investment Failure of industrial capital
Agricultural industry Investment Reduction and depletion of arable land
Non-renewable
resources Discovery of new deposits Resource usage
Pollution Increase in production Natural mechanisms of decomposition of pollutants
Table 1. 5 variables and their contours
Each of these contours affects the probability of increasing and decreasing variables. The relationship between them (variables) was established analytically (by mathematical formulas - functions f(x)). The formulas were derived by analyzing the existing world statistics at that time. All variables affect each other, which describes the interaction of all processes within the planet. This kind of forecasting is undoubtedly very crude, but it was the only possible one for computers of that time. [7] This approach to creating models is called "quantitative", and the models themselves are called "quantitative models". [4] This model was named "World3".
The forecast of the model, like itself, was described in 1972 in the book of the Club of Rome "Limits of Growth". The forecast is as follows: for humanity to exist, resources are needed that are finite. Resources will run out and humanity will perish. Given that the growth of production occurs linearly, and the population exponentially, there will be a "human collapse" (a sharp drop in all parameters) by 2100, the first stage of which is already underway, in our time.
The book "Limits of Growth" quickly spread around the world and made a lot of noise in the press, with the exception of the USSR, as Soviet scientists refuted its predictions. So, for example, in addition to the inherited flaws of the "World" and "World2" models, the "World3" model assumed that there was a capitalist system all over the world,[7] which in fact was completely wrong, because in the 70s most of the world was socialist (52 countries had a socialist orientation). [8] There were violations of the laws of thermodynamics: in the book "Limits of Growth" it was told that working machines at the factory emit heat, thereby heating the planet. [7] These and many other errors were confirmed when comparing the results of the "World3" model with the results of Soviet models of ecology based on physics and ecology (in particular, the "Vasily Ivanovich Vernadsky school"). [4] The Soviet worldview was also an important reason for criticism – scientific communism did not allow Soviet scientists to even think about "purposeful birth control and the population of the planet." [8] Even more flaws in the model were found in the period from 2001 to 2003 by the Russian research group "Designing the Future". [4]
MIT scientists themselves urged not to take into account the values of variables, but to work only with their behavior. Forecasts were recognized as too rough and inaccurate, and the scale was not observed on the charts (even there were no scale divisions). MIT scientists constantly made changes to the model itself, because they constantly found inaccuracies in the relationships of variables. [7] Nevertheless, despite all this, there were people who took the results as an accurate forecast: the US government launched a program to reduce the world's population to protect the military and food security of their country. [9][10]
Having modeled the "actual" forecast on the "World3" model until 2100 and 2400, I came across their fallacy. Screenshots of the simulation results are shown in Figures 3 and 4 of Appendix B. As shown by the second simulation (up to 2400), after the "human collapse", all parameters will stabilize. Pollution will drop to 0 in 2150 and will never increase again. This is due to the fact that humanity for 350 years after the collapse will not try to revive science and production. There will be zero growth of humanity. This is definitely a mistake – such a scenario is impossible in real life, because exponential growth and the revival of production with science is the basis for the survival of mankind in such conditions. [7]
Over the next 17 years, the Club of Rome worked with scientists from all over the world (including the USSR), accepted criticism and reports of modeling errors for subsequent improvement of its model. There have been many comparisons of the "World3" model with other similar models from around the world in order to identify shortcomings and adopt any ideas. The result of this work was the book "Beyond Growth" by Eduard Pestel, which, in fact, is the report of the Club of Rome on the flaws and errors of the "World3" model. In his book, Eduard Pestel mentioned the boundless faith of illiterate people in the results given out by computers, calling it "computer fetishization", and about all the hype of the "greens" and government programs to "save humanity" from different countries expressed himself as follows: "those who thought that the world was already mature enough to so that it could be turned upside down, the demand for zero growth, expressed by respectable gentlemen from the Club of Rome, was perceived as a concrete signal for action against the existing industrial, commercial and political order."[11]
In 1991, the Club of Rome released an improved model "World3-91", implemented in a more modern graphical programming language STELLA. As a result of experiments on this model and its further improvement, scientists have clarified the dependencies of variables (the cost of technology and the impact of the quantity of products on fertility). Patterns of scientific and technological progress were found and introduced into the model (albeit very roughly). The addition of the variables "human well-being" and "environmental burden" made it possible to simplify the analysis of modeling results and the establishment of cause-and-effect relationships. This version of the model was released in 2003 under the name "World3-03". The general scheme of this model is shown in Figure 2 of Appendix A. This model turned out to be much better than its predecessors, but also does not take into account the social factor (human behavior), wars and epidemics. [12]
The noticeable deterioration of the environment, the situation with the ozone hole, [4] the Earth Summit conferences [13] and the United Nations Conference on Sustainable Development [14] led not only to the opening of state programs on climate impact [15] and weather management [16], but also renewed interest in the Rome the club and the "Limits of growth". This prompted the creators of the "World3" model to release a new book: "Limits of growth. 30 years later" in 2004. She not only described the current (at that time) state of affairs, but also presented new, more correct time scans of the dynamics of humanity. The creators of the model themselves admit that their model, like the previous "World" models, is incorrect and "cannot give a forecast for 30 or 50 years ahead with any accuracy." [12]
It is worth noting that the analysis of the "forecasts" of the "World3" model (1972), conducted by KPMG (one of the largest audit companies in the world) in 2020, showed that the values of the parameters of some global processes coincide with the actual values of these parameters at the time of verification. [18] This coincidence occurred because these processes are stable and develop according to fairly simple laws that can be described analytically. An example of such a process is the exponential growth of the population. The same coincidence should be expected from the "World3-03" model.
In 2012, Jorgen Randers (one of the creators of the "World3" models) and a large number of international experts published the book "2052 – A Global forecast for the next 40 years", which describes how the life of mankind will change by 2052. For forecasting, they used two models of global dynamics, including a slightly modified "World3-03" and C-ROADS (greenhouse gas accumulation model). The updated version of the "World3-03" model has more detailed blocks of ecology and economics, and also takes into account social tensions (in a very general and rough form). [19] This model, like its predecessors, is also unable to predict such important phenomena as epidemics, wars and cataclysms.
The quantitative model "C-ROADS" was created by the same method as the models of the "World" family, but it models not the dynamics of humanity, but the accumulation of greenhouse gases. In this model, the dependence of the increase in the average temperature on the planet on production emissions is established. The result of the simulation depends on the values of 6 variables: "year of peak emissions", "year of the beginning of emission reduction", "annual emission reduction coefficient", "reduction of forest destruction" and "greening". [20]
The quantitative model "EN-ROADS", a relative of "C-ROADS", is a similar but more in-depth model. It establishes the dependence of the increase in average temperature not on emissions, but on various human activities, such as energy supply, transport and carbon removal work. [21]
A screenshot of the simulation result of the actual CO2 accumulation on the C-ROADS model is shown in Figure 5 of Appendix B. The second simulation turned out to be less realistic: I introduced a zero emission growth policy from 2022, as a result, the emissions growth not only stopped, but also began to decrease, which is impossible in real life. A screenshot of the result of this simulation is shown in Figure 6 of Appendix B.
A screenshot of the simulation result of the actual CO2 accumulation on the EN-ROADS model is shown in Figure 7 of Appendix B. In the second simulation, I increased the level of electrification, and began converting power plants to coal, oil and natural gas to the detriment of renewable sources and nuclear energy. This should lead to an increase in power plants running on "dirty fuel" and to an increase in pollution, but instead, the model showed that the amount of CO2 emitted has decreased quite significantly. A screenshot of the results of this simulation is shown in Figure 8 of Appendix B.
The "C-ROADS" and "EN-ROADS" models are too general models and do not take into account a large number of other ways to combat greenhouse gases, so their forecasts are very approximate. Like the "World" models, they are not able to predict events such as, for example, damage to gas pipelines. That is why their creators, as well as the creators of the "World" models, say that these models can be used only as general checks of environmental policy or as a training simulator. But, despite the large error in the results of these models, they are used for decision-making by the policy of the European Union, a large number of large companies and even the UN. [20] [21]
On October 18, 2019, an event called "Evil-201" was held - a public simulation of the spread of the coronavirus shortly before the start of its pandemic. This simulation was conducted by the Johns Hopkins Center for Health Safety with the support of the World Economic Forum and the Bill and Melinda Gates Foundation. Of all the possible strains of the coronavirus family, the nCoV-2019 strain participated in the simulation – exactly the strain that caused the global pandemic. [22]
For this event, Johns Hopkins University created a quantitative model of the coronavirus epidemic consisting of 6 variables: "susceptibility", "incubation time", "number of patients with mild disease", "number of patients with severe disease" and "number of deaths". The relationship between these variables is established analytically (by simple mathematical functions). The scheme of this model is shown in Figure 3 of Appendix A. Modeling was applied to most US cities, [23] as a result of which the general forecast of a pandemic for the United States was predicted – the death of 65 million Americans. This forecast turned out to be erroneous: the actual number of deaths in the United States on 17.10.2022. – 1035,865 people ( ≈62.75 times less than in the forecast). [24] This model does not reflect the real processes of virus spread, replacing them with probabilities, so the results of its modeling cannot be considered as a forecast. This was stated by Johns Hopkins University itself on January 24, 2020. [25] Nevertheless, this did not prevent the media around the world from staging a "global hysteria" about this, convincing the governments of all countries to apply restrictive measures that violate laws, human rights and universal values in principle. This issue has even been raised by the United Nations. [26]
There are a lot of other, more advanced models (for example, CovidSIM [27] and COVID-19 in Universities [28]), but they are all analogs of the Evil-201 model and cannot be used to predict the course of a pandemic. The scheme of the "COVID-19 in Universities" model is shown in Figure 4 of Appendix A.
As an experiment, I used the CovidSIM model and modeled the course of the COVID-19 pandemic for Russia. A screenshot of the simulation result is shown in Figure 9 of Appendix B. The simulation predicted that the peak of the pandemic in Russia would be on the 142nd day of the pandemic, and the number of cases would be 22316740 people. In real life, the peak of the pandemic in Russia occurred on day 470 (February 12, 2022), and the number of patients was only 203494 people ( ≈109.67 times less than the forecast). [29]
All the models described above in this paper cannot be used for the purposes for which they were created. The reason for this is a number of the same problems that each quantitative model has:
1. Very large and complex systems are represented as a simple variable. Such systems as "production", "electrification", "construction" are complex and very large systems. Due to the complexity of recreating mathematical models, we have to think of them not as systems with their own structure, but as a simple variable.
2. The values of variables do not reflect the actual state of a complex system. The state of a complex structure, for example, "economy", "production", "pollution", cannot be adequately assessed by one variable. So, in order to increase the value of the GDP of the economy, it is enough to increase the cost of products, and not to improve enterprises. This value does not reflect the real state of the economy.
3. The dependence of variables is described by simple mathematical formulas. There are a huge number of interconnections between such large systems as "human population", "pollution", "resources". In addition, there is a mutual influence of interconnections on each other. Naturally, it is not possible to introduce them all into a mathematical model, so scientists have to generalize them all to one common averaged relationship. Such a connection is very rough and carries the risk of inappropriate behavior of the model. So, one small and insignificant connection can change the state of the entire system (with the help of a chain reaction), but this will not happen in the simulation due to rudeness. The lack of bread can greatly affect people's lives, but within the framework of the model, this will not be noticeable if people's well-being is determined by the value of the country's GDP.
4. They cannot predict events in the future. Such models are completely unsuitable by nature for predicting a number of events, such as the outbreak of wars or epidemics, due to the impossibility of predicting their appearance in the form of a formula, and in fact such events have a very strong influence on the dynamics of humanity.
All 4 of the above problems are a consequence of the technology of creating models using the quantitative method. [4] These models are not able to behave exactly like the object they model.[30] The USSR initially did not use such a modeling technology, but developed its own, which consisted in the fact that the biosphere was conceived as "a set of interacting biogeocenoses with their own temporal characteristics." The complexity of the resulting models turned out to be much higher than the models created by the technology of formalization and simplification, which led to the inability to implement them on computers of both that time and those that exist now. [4] [31]
The creation of models of global dynamics continues today using quantitative technology in the absence of another working approach. An example of this is the "World4" model being created today, which will have not 5, but about 50 main variables and more precise formulas for their interdependence. [32] It is safe to say that this model, like all its predecessors "World", will have the same 4 problems, and its results cannot be considered forecasts (only temporary deployments of existing processes). The scheme of this model is shown in Figure 5 of Appendix A. The same can be said about the relatively recently created Earth4All model [33].
Simulation object-oriented modeling
It is time for humanity to move away from creating global models based on a quantitative approach in favor of methods that allow creating more reliable models. Moreover, these methods should not be a modification of the quantitative approach and not a revival of the old Soviet method (it will become possible after the invention of the quantum supercomputer) – it should be a completely new approach.
As such an approach, I propose a method that I call "Simulated Object-oriented Modeling" (IOOM).
The concept of IOM is that the whole world is thought of as a set of interacting polygons and objects located on the same plane. Landfills emit minerals, territories of countries and buildings. People, cars, and any other movable objects are modeled as objects. During the simulation, objects interact both with polygons and with other objects, as a result of which their parameters change. So, for example, the object "man" entered the landfill "work", as a result of which its parameters changed: fatigue and the amount of money increased. Changing the parameters of objects is the source of their decision–making: low satiety – go eat, no food – go to the store, no money for food - go to work.
It is possible to create a model that will model the whole of humanity in this way already today. Sufficient computing power for such a model can be achieved thanks to cloud computing technologies. The surface of the earth with polygons will also not have to be created – such cartographic services as Googl Maps, [34] Yandex Maps [35] and 2GIS [36] are already similar models implemented using polygons. It is most expedient to create such models using object-oriented programming. [37]
Models built according to this approach, as a result of multiple simulations, will allow humanity to see most of the possible options for the development of humanity, as well as the probability of their occurrence! [3] For the first time, humanity will receive a tool that allows us to make real forecasts with some "margin of reliability", and the opportunity to see how our actions affect our future.
In addition to forecasts of the future, this model can be used in the study of the past: a comparison of events that have already happened with the results of modeling will tell the researcher what he did not take into account when analyzing history. Perhaps it will open up new social processes that are elusive in statistics.
This method also has prospects for development: if today objects make decisions using an algorithm based on the values of its (object) parameters, then in the future, such objects will be controlled by their own separate artificial intelligence.
The practical part
"It has become a tradition to criticize
quantitative models
of social systems for their
lack of perfection.
Instead of this criticism, we
need to offer alternatives."
Jay Forrester
To prove the suitability of IOM for modeling social processes, IOM created a model of a woodcutter in Siberia in the programming language Blitz BASIC [38]. Its appearance is shown in Figure 6 of Appendix A.
Part of the model is a mountainous terrain polygon of the Google Maps mapping service. [34]
This model simulates the activity of a woodcutter who has to cut down trees. During his activity, he gets tired, both physically and mentally. As a process of accumulation of fatigue and the process of rest, a two-parameter model of "critical power" is used, used at all international sports competitions, only in a modified form for the BASIC model. [39]
The result of modeling, as in real life, consists of the cumulative flow of processes, so it is impossible to predict how they will affect each other and what they will lead to. To find out the final result, you need to "lose" it completely. The data of the simulation results is automatically recorded in the CSV file "oth.csv", which allows you to open the data in any analytical program or DBMS for subsequent analysis and plotting. [40] This model is described in detail in the technical documentation [41] and user instructions [42].
The correctness of the model was proved by comparing its forecasts with the practical activity of felling trees (harvesting firewood). The forecast error was 22-25%. With repeated playback of the model, and taking the average result, the error was: 10-12%. Comparing the average of forecasts with the average result of several cutting approaches, the error was 2-3%.
Graphs of the modeling processes of the IOM model are presented in Figures 10, 11 and 12 of Appendix B.
Conclusion
1. Models constructed by the quantitative method are erroneous initially, since the approach used in them is not suitable for predicting the future of social systems for 4 reasons: complex systems are represented as one simple variable, the values of variables do not reflect the actual state of a complex system, the dependence between variables is described by a simple mathematical formula and cannot predict some types of future events (wars, epidemics, catastrophes).
2. Despite its drawbacks, the quantitative approach is still used in the creation of large models for lack of other methods.
3. The future of modeling human dynamics is behind simulation object-oriented modeling (IOM), as a more reliable alternative, the implementation of which is already possible today.
4. The actual suitability of IOM models for predicting social systems and, as a result, the future of all mankind, has been proven in practice by creating a model of the activity of a woodcutter in Siberia.
5. The models created by IOM will allow us to anticipate all possible futures and make appropriate decisions to increase the likelihood of the future we want.
6. IOOM models can be used to study the past.
7. This method has prospects for development in the form of an artificial intelligence (AI) supplement.
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