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"Куда идет мир? Каково будущее науки? Как "объять необъятное", получая образование - высшее, среднее, начальное? Как преодолеть "пропасть двух культур" - естественнонаучной и гуманитарной? Как создать и вырастить научную школу? Какова структура нашего познания? Как управлять риском? Можно ли с единой точки зрения взглянуть на проблемы математики и экономики, физики и психологии, компьютерных наук и географии, техники и философии?"

«NONLINEAR DYNAMICS AND THE PROBLEMS OF PREDICTION DISCUSSION AT THE RAS PRESIDIUM» 
G.G. Malinetskii and S.P. Kurdyumov

It was probably the ability to effectively isolate riverbeds and to learn not only by trial and error and perfecting its predictive system, but also by relying on the commonsense that gave mankind the decisive advantage in its evolutionary development. We can also take a broader view: different theories, approaches, and sciences prove to be useful and necessary if they succeed in finding the right riverbeds. After all, science is an art of simplification, and it is particularly easy to simplify when dealing with riverbeds. Of course, on average, we cannot glimpse at what is beyond the forecast horizon, but in particular, having found ourselves within the riverbed parameters and became aware of the fact, we can act intelligently and with caution.
This raises the following questions: Where does the riverbed start and end? What is the structure of our ignorance? How can we go from one information field and notions adequate to this riverbed to others when this riverbed is at an end? As one comes to know different economic, psychological, or biological theories, one has a persistent feeling that their originators deal, without realizing it, with different realities, or different riverbeds. This is akin to the principle in quantum mechanics, where the answer to the question of whether an electron is a wave or a particle depends on the experiment.

THE FORECASTING AND DYNAMICS OF COMPLEX SOCIO-TECHNOLOGICAL SYSTEMS

Having realized the existence of a forecast horizon, understood how complex the systems with which we deal can be, clarified the questions that can be asked and the data we need to be able to answer these questions, we obtained a tool for the description of a great variety of phenomena and processes. It is particularly useful when we predict the behavior of socio-technological systems, for which quantitative patterns determining their dynamics are yet unknown.
Modeling the development of higher education
In 1994, we were approached by the Russian Ministry of Education and the International Bank for Reconstruction and Development. The matter at hand was the granting of a two billion credit for the reconstruction of Russia’s higher education; it was a more trouble-free time than the one we are living in today. The following question arose: If the World Bank’s wishes were realized, what would it lead to in a five-, ten-, and twenty-year perspective at a macrolevel (the macroeconomic level), middle level, and a microlevel. Let us dwell on the macromodel.
We analyzed United Nations statistics within a nonlinear dynamics framework. It was found that industrial development and the role of science and education can be determined (if we aim at a crude, qualitative picture) by the computer analysis of a discrete mapping of three variables [14]. One describes the resources; another, output (gross domestic product); and the third one, science plus education (Fig. 11). There are two key quantities in this system. The first is the time lag. If science and education improve their performance tomorrow, the economy is not likely to see the results until three to five years later. The second is receptivity to innovation. According to available statistics, if we take the receptivity of the Japanese economy as 10, then that of the United States economy will be 8, that of Western Europe will be 6, and that of the Soviet Union will be 1.
Now let us assume a model situation. A country rich in resources initiates industrialization and invests in science. However, its economy has a receptivity factor that equals zero. Science is making great progress in this country, but because its economy is not receptive to any research findings, we eventually find ourselves at the renewable resources level (Fig. 1la). The role of science in this situation is quite different: we need it in order to find new sources of development. To illustrate this point, uranium salts were known to be fine dyes in the 1930s. Later, it was discovered that uranium had some other useful applications.

Рис. 11. Макроэкономические траектории экономики, невосприимчивой к нововведениям (а), восприимчивой к инновациям (б), восприимчивой к инновациям при урезании финансирования (в).
Кривые показывают, как меняются выраженные в условных единицах ресурсы (1), объем производства (2) и научно-технический потенциал (3) в некоторой стране с течением времени;
а — соответствует «банановой республике»,
б — ситуация, когда общество достигает некоторого уровня развития, после чего происходит смена основных ресурсов развития и дальнейший рост обеспечивается интеллектуальной сферой,
в — ситуация, когда в результате сокращения вдвое финансирования интеллектуальной сферы к критическому моменту начала спада производства развитие этой сферы не достигло необходимого уровня и не смогло оказать заметного влияния на развитие общества

Now, let us imagine that we have managed, through some reforms, to raise the receptivity of our economy. Our postreform situation is close to what happened in Japan, where an accelerated growth was in evidence (Fig. 11b). If, during this rapid growth, we reduce the support of education and science by half, the country will find itself in the same situation it was in from the beginning (Fig. 11c). We are in a trap: science is not supported because the economy is poor; the economy is poor because there are no projects or effective technologies.
The IBRD models with which we compared our results yielded roughly the same picture. The bank’s experts believe that creating a sustainable low-productivity operation would be normal for Russia. We think otherwise.
Toward a «direct-action sociology»
Totally new opportunities are opening up in societal management. We shall use the terms «social barometer» or «direct-action sociology» to describe them. What do they mean?
Let us assume that we are measuring some parameters of our society. The question is how many variables, in reality, characterize it. Sociological survey data and the capacity available in many Russian regions make it possible to monitor public opinion, yielding dozens and hundreds of indicators. If computer networks are used, this kind of monitoring can be carried out at daily or hourly intervals. However, what use is this vast and, evidently, important information to us? Decision makers can keep in their field of view only a handful of factors and qualitative indicators, not more than seven, if we are to believe psychologists. How do we select these indicators and help make intelligent and balanced decisions?
The fact that help is possible is shown by a simple device like the barometer. If we cannot effectively solve equations describing atmospheric dynamics, from which we could predict the weather, our barometers still warn us before a storm that problems may await us.
For social systems, computer technologies can serve as a barometer of sorts: they reduce the information available to a few indicators that help us in decision making. Techniques tested in earthquake prediction furnished the basis for these approaches [8]. We do not know the equations that we can solve to forecast a disaster, but we have a vast file of data we can use to teach appropriate computer systems to forecast. We have conducted work on the sociological applications of these approaches jointly with I.V. Kuznetsov and his colleagues at the RAS International Institute of Mathematical Geophysics and Earthquake Forecasting Theory and also with S.A. Kashchenko and researchers at Yaroslavl State University.
A word of caution against excessive expectations typical of a society pinning too many hopes upon computer technologies. Initially, it was supposed that computerized control systems would be instrumental in raising the efficiency of the economy, but the economy proved unprepared for this. Great expectations were entertained for an experiment in a computer-aided solution of various equations. However, it was found that we lacked suitable equations for the description of many important entities, and even if we had these equations, finding the coefficients and adjusting the model was in itself a challenging problem.
Data is the Achilles’ heel of prediction algorithms for socioeconomic systems and risk management problems. To teach a computer system, we need long arrays of valid and reasonably accurate data describing the different aspects of the concerned object. So far, this has been lacking practically everywhere. If this gap is filled,’ the quality of our forecasts can be greatly improved.
When we took up sociological data, we found many curious things. It transpired that the reaction of Moscow and St. Petersburg to many events was the direct opposite to that of the rest of the country (Fig. 12). Obviously, this behavior is connected with the socio-economic structure of our society and its terms of reference. Using these approaches, many of the conclusions made by researchers at the RAS Institute of Social and Political Studies [15] can be corroborated and rationalized in quantitative terms at another, deeper level.

Рис. 12. Разность между позитивными (и нейтральными) и негативными ответами на вопросы ВЦИОМ в Москве и Санкт-Петербурге и в остальной России. а — «Что бы вы могли сказать о своем настроении в последние дни?»; б — «Как бы вы оценили в настоящее время материальное положение вашей семьи?»

These methodologies, like most research findings, cut both ways. By relying on them we can, on the one hand, manipulate the behavior of our electorate even more successfully than we do today. On the other hand, they show key variables and order parameters in the social conscience. It is they that determine the main problems of the future and opportunities connected with Russia’s revival after the crisis.The curves show changes through time in a nation’s resources, expressed in conventional units (1), output (2), and scientific and technological potential (3), with (a) standing for a «banana republic,» (b) standing for a situation where society arrives at a certain level of development, followed by a change in the main development resources, with the further growth supported by the knowledge sphere; and (c) standing for a situation where, due to the support of the knowledge sphere being cut by half. the development of this sphere has not reached the necessary level, by the critical point of the start of the decline in production, and could not have a pronounced effect upon societal development.

Innovation development. Scenarios for Russia
Today, many hopes are being centered on what is called the «innovation economy.» We’ at the Institute of Applied Mathematics, together with colleagues at other RAS institutes, are conducting a study, commissioned by the RF Ministry of Industry, Science, and Technologies, into the possibilities available to Russia for embarking on a sustainable development path and shifting to an innovation economy.
Our analysis has shown that, from a ten-year perspective, the complex socioeconomic system of Russia is threatened by collapse. Its systemic crisis has brought the nation to a line where the supercritical depreciation of the main assets leads to a series of anthropogenic and social disasters, the growth of energy prices leads to the ultimate destruction of the manufacturing industry and the raising of transportation rates, and to the irreversible breakup of the nation. Given the present trends, the nation will completely lose its sovereignty and disintegrate, and the Russian people will disappear from the historical arena.
Because of its geographic and geoeconomic position, and by virtue of the high energy intensity of the industry and living in this cold country, which has four-fifths of its territory located in the permafrost area, Russia cannot for any length of time be a raw-materials appendage of the «golden billion» nations [16]. Therefore, the question of the new development of resources became a vitally important one [14]. One possibility is to redirect some of the sectors of the economy to high-technology production. Russia’s government has announced a strategy of transfer from a «tube economy» to innovative development.
The official view of innovation focuses on the neoliberal conception and the imitation of foreign models. It treats an innovation as something that has found its place in the market, lays an emphasis on the development of venture businesses, and sees the state’s role as that of an arbitrator providing the conditions and infrastructure for the application of innovations. Studies made at the Institute of Applied Mathematics and other RAS institutes have shown that this is a dead end path for Russia.
Innovation in today’s Russia should ensure the solution of strategic tasks in the sustenance of its population and its gradual transition to a progressive, sustainable development path, not the «filling up of the market,» «assuring macroeconomic stabilization,» etc. Most of the innovations of vital importance for Russia are non-market ones. They include the production of high-quality and affordable foodstuffs and medicines, the building of housing and roads, the provision of communications, alternative technologies, and innovations increasing the safety of the technosphere. Many of the innovations being publicly discussed today [16, 17] are not needed for the economy’s harmonization but for the nation’s survival. Reliability, endurance, and maintainability are characteristics of new technologies at a premium for Russia today.
The state can and must be the only customer for such innovations. It must assume the key function of goal setting in the fields of economic and social development. This calls for a fundamentally different level of coordination compared to the present one and much higher demands on the prediction and monitoring of the socioeconomic system. This presupposes the reestablishment, on the basis of new methods of social management, forecasting, and modern information technologies, of something along the lines of the State Planning Committee of Russia.
Its primary tasks should be:
- To raise the reliability and quality of forecasts;
- To make use of Russia’s available resources;
- To define the nation’s scope of opportunities, given alternative development strategies; and
- To detail the policy chosen (not only in cost but also physical indicators).
We must realize that the country is in an emergency. a historical dead end. To lead our country out of this dead end, we need programs on the scale of ED. Roosevelt’s New Deal [18]. The development of such a course should be the central task for the nation’s research community and leadership alike.
Returning to innovation, we shall note that the variables that the Ministry of Industry and Science regarded as the key ones and the mechanisms it acknowledged as important-innovation/production complexes, their accelerated development, market penetration, etc.-are actually secondary. When we analyzed the items on which hopes were pinned, these hopes proved to be unjustified. What matters is not innovation/production complexes but their symbiosis. The Zelenograd Innovation and Production Complex is a case in point. It includes the Proton plant, which is a donor for a host of smaller enterprises. Each of them receives money from the government. However, if we cast the total (how much such an enterprise receives and how much it contributes to GDP), it turns out that they give about ten times more than receive. Therefore, as we encourage innovation in this particular case, we should think not only about small businesses, but more importantly, about the Proton plant. As can be seen, when one analyzes seemingly obvious things from the standpoint of nonlinear dynamics and information processes, the results can be rather unexpected.
Theoretical history, or, a search for alternatives
Arnold Toynbee, one of the greatest historians of our time, wrote a very short work, a «historical heresy» as he termed it later in his memoirs, «If Philip and Artax-erxes Had Survived» [19]. It is on record that Alexander the Great came to power as a result of a plot allegedly engineered by his mother. It was for this reason that his mother was to die very soon thereafter. According to Toynbee, history would have taken a radically different course if there had been no Alexander and, correspondingly, his opponent. There would have been no Rome, the era of great European empires would never have come, and city-states would have long retained very good development prospects. At the same time, Oriental despotisms would have slowly transformed while retaining their stability.
The techniques, methods, and formalisms offered by nonlinear dynamics and undergoing active development make it possible to consider historical development alternatives for some simple model situations [14, 20, 21]. Here is an example relating to the situation examined by Toynbee. Computer calculations of the Mediterranean population densities yield two variant developments (Fig. 13). According to the first, there is Rome, and history has developed precisely as it has developed. In 96% of the cases, computations do indeed yield this variant. But there is still the 4%, when history takes quite a different course: if there is no Rome, there is no Roman civilization, whereas Greece is developing at an accelerated pace. In other words, computer analysis admits to both the possibilities that Toynbee foresaw.

Рис. 13. Результаты компьютерного расчета плотности населения в Средиземноморье Слева — вариант, реализовавшийся в истории, справа — альтернативный, когда нет Рима и Римской империи

Of course, these simple models are rather conditional. They only recognize elementary links between natural, social, and demographic factors-a very limited set in comparison with the vast file of data that professional historians deal with. However, even the recognition of these few relationships allows one to see historical alternatives. It is to be hoped that more complex models of this kind will be useful in strategic planning, and in due course, history will increasingly pose as an applied science, a kind of whetstone on which to sharpen global dynamics models, whose importance is growing in the context of the sustainable development concept.
To summarize, researchers working in different scientific disciplines have reached a common understanding of essential problems in forecasting and fundamental limitations connected with prediction. In order to pursue a sensible policy (technological, innovation, or economic), it is critically important in some instances that we have both a forecast and a team capable of making it.
References
1. Lorenz, E.N., Deterministic Nonperiodic Flow, J. Atmosph. ScL, 1963.vol.20.pp. 130-141.
2. Predely predskawemosti (Prediction Limits), Moscow: Tsentrkom, 1997.
3. Malinetskii, G.G., Khaos. Sfruktury. Vychislitel’nyi eks-periment. Vvedeme v nelineinuyu dinamiku (Chaos. Structures. Computer Experimentation: Introduction of Nonlinear Dynamics), Moscow: Editorial URSS, 2000.
4. Sornette, D. and Johansen, A., Large Financial Crashes, Phys. A, 1997, vol. 245, nos. 3-4.
5. Johansen, A., Sornette, D., et al.. Discrete Scaling in Earthquake Precursory Phenomena: Evidence in the Kobe Earthquake, J. Phys. France, 1996, vol. 6.
6. Rezhimy s obostreniem. Evolyutsiya idei: Zakony koevolyutsii slozhnykh struktur (Aggravation Modes. The Evolution of an Idea: The Laws of Coevolution of Complex Structure), Moscow: Nauka, 1998.
7. Proceedings of the Workshop «Reduction and Predictability of Natural Disasters» held Jan. 5-9, 1994, in Santa Fe, Rundle, J.B., Turcotte, D.L., and Klein, W., Eds., New Mexico, 1995.
8. Vladimirov, V.A., Vorob’ev, Yu.L., Malinetskii. G.G., et al., Upravlenie riskom. Risk, ustoichivoe razvilie, sinergetika (Risk Management. Risk, Sustainable Development, and Synergetics), Moscow: Nauka, 2000.
9. Larichev, O.I., Teoriya i melody prinyatiya reslienii (Theory and Methods of Decision Making), Moscow: Logos, 2000.
10. Bak, P. How Nature Works: The Science of Self-organized Criticality, New York: Springer, 1996.
11. Malinetskii, G.G. and Podlazov, A.V., The Self-organized Criticality Paradigm: The Hierarchy of Models and the Limits of Predictability, Izv. Viiw. Prikl. Nelineinaya Dinam., 1997, vol. 5, no. 5.
12. Waldrop, M.M., Complexity: The Emerging Science at the Edge of Order and Chaos, New York: Touchstone, 1993.
13. Malinetskii, G.G. and Potapov, A.B., Sovremeimye problemy nelineinoi dinamiki (Problems of Nonlinear Dynamics Today), Moscow: Editorial URSS, 2000.
14. Kapitsa, S.P, Kurdyumov, S.P., and Malinetskii, G.G., Sinergetika i prognozy biidushchego (Synergetics and Forecasts of the Future), Moscow: Nauka, 1997.
15. Rossiya u kriticheskoi cherty: vozrozhdenie ili katastrofa. Sotsial’nay a i sotsial’no-politicheskaya sitiiatsiva v Rossii v 1996 godii: analiz i prognoz (Russia at the Critical Line: Revival or Catastrophe. The Social and Socio-Political Situation in Russia in 1996: An Analysis and Forecast), Osipov, G.V., Levashov, V.K., and Loko-sov.V.V., Eds., Moscow: Respublika, 1997. Why is Russia Not America, Moscow:
16. Parshev, A.P., Forum, 2000.
17. Weizsecker, E., Lovince, E., and Lovince, L. Factor Four, Moscow: Academia, 2000. Translated under the title Faktor chetyre.
18. Roosevelt, FD., Fireside Chat, Moscow: Gos. Duma R.F, 1995. Translated under the title Besedy u kamina.
19. Toynbee, A.J., If Philip and Artaxerxes Had Survived, Znanie-sila, 1994, no. 8. Translated from English.
20. Malkov, S.Yu., Kovalev, V.I., and Malkov, A.S., Mankind’s History and Stability: A Mathematical Modeling Experiment, Strateg. Stabil’nost’, 2000, no. 3.
21. Chernavskii, D.S., Pirogov, G.G., et at. The Dynamics of the Economic Structure of Society, /zu Vuzov. Prikl. Nelinein. Dinam., 1996, vol. 4, no. 3.

Discussion at the RAS Presidium

This scientific communication was discussed by RAS academicians, R.F. Ganiev, Yu.A. Izrael’, N.A. Kuznetsov, D.S. L’vov, G.A. Mesyats, R.T. Nigmatulin, N.A. Plate, D.V. Rundkvist, V.I. Subbotin, and S.Yu. Malkov, Dr. Sci. (Eng.) of the Center for Strategic Nuclear Forces at the Academy of Military Science.

G.G. Malinetskii, having made a scientific communication on «Nonlinear Dynamics and Prediction Problems» at the RAS Presidium, answered questions.

Academician Yu.A. Izrael’: You have showered us with a tremendous amount of information, which seems to have a fair share of emotion thrown in. There are different kinds of forecasts but you pretend to use all of them, natural, economic, and political alike. I wish to center my question on natural processes.
Early in your report, you mentioned the prediction limit. What is your view of the prediction limit: is it lack of information, lack of theory, or a matter of principle? If it is a matter of principle, i.e., there is a prediction limit, how can it be determined?
Malinetskii: There is indeed a prediction limit; this is the point I wanted to make. It appears that nature being what it is, near paths diverge in many systems, even fairly simple, low-dimensionality ones. That is to say small causes lead to great effects. The rate at which these effects grow with time determines the forecast horizon. When Edward N. Lorenz became aware of this fundamental limitation, he gave the following striking example: If the earth’s atmosphere is what we think it is, a butterfly’s wingbeat — a very small action at the right place at the right time — can change the weather in a vast region in, say, two to three weeks time. In other words, the formulated limit is as much a matter of principle in meteorology as it is in quantum mechanics or thermodynamics.
There are different ways to determine our time horizon. In particular, we can maintain monitoring and recording, every tenth second, the position of a specific ball in the pendulum I have demonstrated. Furthermore, we use computer techniques to measure quantitative characteristics of the ball’s path. Many a time, our ball finds itself in the vicinity of one and same point in the phase space. Let us have a single path. A second path, starting from an adjacent point, can be considered a disturbed first path. From these two paths we can determine the mean rate of their divergence, and hence, the forecast horizon.
Izrael’: You estimate the forecast horizon in meteorology at two to four weeks. Can you give us a more definite figure?
Malinetskii: We once settled on three weeks. Visiting American specialists maintained that three weeks was, indeed, the magnitude.
I wish to be understood correctly; therefore, I will return to my pendulum. After I start it, there is a five-percent chance that it will go over to a simple periodic mode, which is perfectly predictable. In other words, there are strange spots in the phase space where predictability is anomalously good. In meteorology, there is a well known phenomenon called blocking. If the atmosphere is in a certain special state, we find ourselves in the neighborhood of a quite definite point in the phase space, in which the forecast horizon can be rather distant. On average, however, the system has a particular finite time horizon.
Academician G.I. Marchuk: Mikhail Alekseevich Lavrent’ev at the RAS Siberian Division made some experiments. There is a wave, a rather big one, perhaps even a tsunami wave. Then it begins to rain, and suddenly the wave’s energy dissipates, the wave grows smaller and smaller, and finally disappears. How does this experiment fit the theory you are developing?
Malinetskii: To be frank, I have anticipated this question. Here is a demonstration I have prepared. Look at this toy (see picture). When in equilibrium, it has a steady form which is unchanged irrespective of the action it is exposed to. If we start to slowly alter a parameter, at some point there will be an abrupt change, and this form of equilibrium disappears. The change is followed by a bifurcation, with the system becoming very sensitive to small actions. This is a typical picture in many complex systems, from social to economic. It would appear that our Novosibirsk colleagues observed this kind of phenomenon.

Игрушка, иллюстрирующая аномальную чувствительность системы вблизи точки бифуркации. Эта игрушка имеет два устойчивых состояния равновесия (а, б). Меняя число витков пружины, зажатых в руке, мы изменяем параметр. Вблизи точки бифуркации (в), где исчезает одно из состояний равновесия, пружина обладает аномальной чувствительностью к малым возмущениям. Последние скачком могут привести пружину в состояние равновесия а

Academician N.A. Shilo: I once noticed that the time distribution of the half-lives of both stable and radioactive isotopes of chemical elements fit in the Fibonacci series. What is the relation between the Fibonacci series and the tremendous process of decay of radioactive elements, which can be said to embrace the whole Universe?
Malinetskii: We did not address this problem; we simply did not meet people who asked these kinds of questions.A toy demonstrating the anomalous sensitivity of a system near a bifurcation point.
The toy has two steady states of equilibrium, (a, b). By varying the number of coils of the spring clutched in the hand we change a parameter Near the bifurcation point (c), where one of the states of equilibrium disappears, the spring has an anomalous sensitivity to small disturbances. The latter can bung the spring by a rapid change to the state of equilibrium.
Academician R.I. Nigmatulin: It seems to me that thresholds are one of the reasons for the appearance of various uncertainties. Another reason is that most processes are described by numerous parameters, and since we cannot cover all of them, we have to reduce their number. Instead of billions, we have seven or eight equations. For instance, in classical mechanics, uncertainty is the price paid for there being thresholds or the reduction of the number of variables or other values.
Malinetskii: What you are speaking about are indeed important causes of uncertainty. However, along with these causes, there is an even deeper-seated reason. An elementary system like the Lorenz system has no thresholds, none of the factors you listed, but it does have uncertainty. Nature made it so that there would be a fundamental limitation associated with the forecast horizon.
Academician V.A. Kabanov: A few years ago, you read a report at the chemical faculty of the Moscow University in which you analyzed the possibility of predicting the state of education in Russia depending on the amount of funding received. If 1 remember correctly, your model led to the following conclusion: if we take a country with a fairly high level of development of science and education, which are funded in one way or another, and then reduce the funding, this high level will persist for the time being, followed by a collapse. Today, the percentage of the GDP appropriated for science and education is decreasing in this country. Is it possible to use your computations to predict in how many years science and education will collapse in Russia?
Malinetskii: The study you mentioned was concerned primarily with education. We did establish a funding threshold, after which «science plus education» cease to have any effect on the macroeconomy. This is not to say that education has no effect on a microlevel; people satisfy their curiosity, raise their social status, etc. We also traced the future development of the teaching community. This was in 1995.
When we submitted our forecasts, we were highly praised, but told that our prediction was too gloomy, that we should be realistic, and that there was no way the funding of science and education could be increased, not just by a matter of percent, but several times over, as we advised. Unfortunately, the reality proved to be close to our predictions, if not gloomier still. According to our computations, the reform of higher and secondary education being conceived today is criminal, for it will lead to a rapid degradation of our whole system.
I believe that it is, in principle, possible to analyze the scientific sector and the innovation sector at a macrolevel, but there are two problems. One is social need: there has to be people who are really interested in forecasts; the other is that a large body of data is required. We work with the Yaroslavl region and with the Moscow government. We have found that all data are privatized and every item has to be paid for. These two circumstances prevent our work team from developing the same kinds of model and speak about science as seriously as we once spoke about education.
Academician V.I. Subbotin: You used the term extradesign accidents. It is not of your invention, but it carries very dangerous undertones. To be sure, nothing is absolutely safe. A system created by humans has a right to accidents but not to disasters. If this cannot be achieved, this area must be simply closed, and other approaches to the final goal should be sought. The idea of an extradesign accident creates the possibility of a collapse.
Malinetskii: I will give an example which is related not to nuclear power, but to oil extraction. Drilling platforms are operating both in the North Sea and the Gulf of Mexico. They represent more than one million tons of metal and concrete, and their total cost is upwards of two billion dollars. The platforms are built to be extra safe. When they were started, the general feeling was that no accidents at all could occur. The risk estimates made at the time said that a breakdown could occur, not once in one million years as in the case of an atomic reactor, but in 20 million years; that is to say, they were designed with an order of magnitude more reliable than an atomic reactor. Nevertheless, heavy accidents have occurred at 15 platforms.
We must face the fact that disasters can occur in complex engineering systems. We should count our money, but we should also build when it pays to risk. It is simply not possible to rule out the likelihood of a disaster, as we realize now, therefore, during the design process we should have in mind the worst of possible scenarios too.
There is one last point to make in this connection. Individuals, with their skills, psychological state, etc., are also a part of a technological system. When human factors sharply deteriorate, what happens is something that we have always warned about: through human fault, the technosphere starts to break down. Roughly speaking, given particular human skills and pay level, we can use particular technologies; when the skills and pay level are decreased, the use of sophisticated technologies is pregnant with disaster and catastrophe. This aspect seems to be very important for Russia.
Academician G.S. Golitsyn: I should like to remind you that the problem of prediction was first formulated by the astronomer and meteorologist Philip Thompson as early as the mid-1950s. Lorenz further developed all of this.
You never mentioned-perhaps for lack of time- the fact that we can predict the statistics of events or the weather. A weather forecast is made for a particular time period and an averaged area. As a rule, the longer the time period, and the larger the area for which we make a prediction, the greater the forecast horizon. We already know the prediction limit in the study of climates. Are there examples of the extension of forecast idomains in time, space, etc., to other fields of science?
Malinetskii: In the technosphere, we faced what is called the planner paradox. Let us assume that we have very good models, a fine strategy, and very good solutions designed for five years. The question is, «What happens after 10 years?» These strategies may prove to be ineffective in 10 years, and simply criminal after 20 years. This raises the question: «How long are we going to live, and how are we going to average?» If we are going to live in the Principality of Muskovy and average for Moscow oblast, we will have particular models and solutions. If, on the other hand, we are going to act throughout the Russian territory, there should be a different strategy. Russia borrowed much money in the belief that everything would be fine after ten years. This never came to pass. Moreover, this money was borrowed from the condition of the domestic market and not the global dynamics. This money was borrowed on our good intentions.
Therefore, let us set our task straight-what we want to have. Further, depending on the formulation of our problem, we will arrive at different equations and different models. Here, in my opinion, the situation is the same as in meteorology. True, it is easy to predict the climate, but it is extremely difficult to predict the weather.
G.S. Golitsyn: It is very important that we realize what we can and what we cannot do, and what is dangerous. Science is undergoing commercialization, which in itself poses a number of important mathematical problems.
Academician N.P. Laverov: I am baffled by the State Duma having adopted, in the first reading, a new prediction law, which covers the prediction of both processes and phenomena. Considering the great influence that various external contingencies exert upon an operating system, have we matured enough to pass in the Duma a law forecasting processes and events?
Izrael’: Isn’t it a wonder what the Duma is doing! How can we enact a prognostication law?
Laverov: The Duma will be the Duma, but we should be kept advised of what is being done there.
Malinetskii: I can explain why this kind of law is being passed because I happened to talk with the experts. Their comment is this: today, no one in this country bears any responsibility for any forecast.
How is a forecast made in a normal situation in a normal country? Assume that a forecast for the development of the economy has been made. There are verifications and models, which are discussed, there are competent people who can state: «Yes, our scientific community realizes that, at present, we have no better model, therefore, given the present standard of our technology, we shall rely on it in our predictions of the economy. In the course of time, we shall see how well we have predicted the future and correct our models.»
What happens in this country? The government team is changing rather often. It sets up its analytical center and recruits forecasters who make decisions. When the government is asked, «What has happened?,» or «What have you done?,» the usual answer is this: «You know, that was the forecast we had, and we relied on it.» I believe that it was in order to avoid this kind of talk, and partly succumbing to emotion, that the Duma is passing a prediction law.
Laverov: I will continue. If this law is enacted, we shall act in the framework defined by the law, and shift the blame for failed forecasts to the fact that a law has been passed, and we need no change in our models. Have you seen what the law says at all?
Malinetskii: Yes, I have. Its attitude to prognostication is as if we lived in Laplace’s times. Notably, it ignores the existence of an objective forecast horizon. Viewed in this perspective, the law is, I believe, ill-judged. We should be more sober in our appraisal of the capabilities of contemporary science. Also, it fails to acknowledge one circumstance. Conceptually, a forecast is a process. There is a commission. You submit a forecast to it, and you find out whether or not your methodology works. You find it out primarily from whether or not your forecast has held up. However, this kind of mechanism is not found in either the State Committee for Engineering Supervision, or a host of other vitally important departments, notably the Ministry of Defense. If the general attitude were the same as in earthquake prediction, namely, that a forecast is a nonrecurrent act, on the one hand, and a process and ongoing work that must be perfected, on the other, we would face no problems.
I feel that if the Russian Academy of Sciences does not make a move to introduce amendments, the prediction law will be passed unamended.
Academician D.S. L’vov: In contemporary economics, there are so-called alternative approaches to socioeconomic forecasting, which can be presented by cones expanding with time. In our present situation, the overlapping area of these cones proves so short in duration as to render the different options in the development of, say, Russia’s macroeconomic parameters practically indistinguishable. In this connection, I have two questions to ask. Have you investigated Gref’s famous forecast, if only as a rough estimate? Have you estimated how much it will cost to expand our forecast horizon and to see somewhat further than we can today?
Malinetskii: Unfortunately, we did not find a client for this work, although we wanted very much to undertake it.
When Gref’s program was discussed at our institute, the first thought that came to mind was, where are the models that underlie, e.g., that dreadful pension alternative visualized by Gref? True, the workforce will decrease but so will the number of pensionable-aged peoples! Thus, based on ambiguous models, quite awful things are adopted.